{"selection":"all","summary":{"snapshot_date":"2026-10-11","downloaded_submissions":43013,"reference_sections_detected":42901,"candidates_checked":3604,"year_definition":"First public release year where known, using cached arXiv and publisher metadata; older fallback years may be inferred from arXiv identifiers. Known earlier web releases override later arXiv dates. This is not the conference acceptance year.","method":"Candidates: arXiv IDs from all years cited explicitly by at least 30 submissions, expanded with 2025–2026 IDs cited by at least 15 submissions, plus frequent titles from the earlier catalog and four verified additions. Initial release years come primarily from arXiv metadata; known earlier publications are corrected. Counts merge explicit arXiv identifiers with Unicode-normalized full-title matches and reviewed/high-similarity title variants, once per citing submission. Nested title matches inside longer paper titles are suppressed. Short titles require a standalone bibliography title or an arXiv ID. Model-release announcement blog posts are excluded; research papers and formal technical reports/model cards are included. Rankings are estimates, not an exhaustive resolved citation graph; papers outside the candidate discovery thresholds may be missed. Includes public withdrawn/desk-rejected submissions. No acceptance claims.","readable_pdfs":43011,"unreadable_pdfs":2,"metadata_note":"Older paper metadata partly uses recurring bibliography titles and years inferred from arXiv IDs, because additional API metadata requests were rate limited. These entries are marked individually. This is a candidate-based estimate, not a complete ranking of every cited work."},"papers":[{"title":"Qwen3 Technical Report","arxiv_id":"2505.09388","year":2025,"url":"https://arxiv.org/abs/2505.09388","citing_submissions":5115,"explicit_arxiv_matches":4705,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"preprint arXiv:2104.08378, 2021. Jeff Pool and Chong Yu. Channel permutations for n: M sparsity. Advances in neural information processing systems, 34:13316–13327, 2021. Haoxuan Wang and Chen Wang. Hierasparse: Hierarchical semi-structured sparse kv attention. arXiv preprint arXiv:2604.16864, 2026. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xi","arxiv_match":true},{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"g Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jianwei Zhang, Jianxin Ma, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Keqin Chen, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. Jiecao Yu, Andrew Lukefahr, David Palframan, Ganesh Dasika, Reetuparna Das, and Scott Mahlke","arxiv_match":true},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"u, Junyang Lin, Kai Dang, Keqin Bao, Kexin Yang, Le Yu, Lianghao Deng, Mei Li, Mingfeng Xue, Mingze Li, Pei Zhang, Peng Wang, Qin Zhu, Rui Men, Ruize Gao, Shixuan Liu, Shuang Luo, Tianhao Li, Tianyi Tang, Wenbiao Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yuqiong Liu, Zekun Wang, Zeyu Cui, Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu. Qwen3 technical report, 2025a. URL https://arxiv.org/abs/2505.09388. Dongjie Yang, Xiaodong Han, Yan Gao, Yao Hu, Shilin Zhang, and Hai Zhao. PyramidInfer: Pyramid KV ","arxiv_match":true}],"id":"2505.09388","metadata_source":"Cached arXiv API metadata","rank":1},{"title":"LoRA: Low-Rank Adaptation of Large Language Models","arxiv_id":"2106.09685","year":2021,"url":"https://arxiv.org/abs/2106.09685","citing_submissions":4306,"explicit_arxiv_matches":1257,"examples":[{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"uilibrium. In Proceedings of the Advances in Neural Information Processing Systems, 2017. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In Proceedings of the International Conference on Learning Representations, 2022. Minyoung Huh, Brian Cheung, Pulkit Agrawal, and Phillip Isola. Straightening out the straightthrough estimator: Over","arxiv_match":false},{"id":"ueZOHGh0yB","title":"ProtegoFed: Sample-Level Backdoor Defense for Federated Instruction Tuning with Interspersed Poisoned Data","forum_url":"https://openreview.net/forum?id=ueZOHGh0yB","excerpt":"itigating sybils in federated learning poisoning. arXiv preprint arXiv:1808.04866, 2018. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, 2022. Hai Huang, Zhengyu Zhao, Michael Backes, Yun Shen, and Yang Zhang. Composite bac","arxiv_match":false},{"id":"nSsaEp75xm","title":"EmbTracker: Traceable Black-box Watermarking for Federated Language Models","forum_url":"https://openreview.net/forum?id=nSsaEp75xm","excerpt":"distribution for federated visual classification. arXiv preprint arXiv:1909.06335, 2019. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, 2022. Kelvin Jiang, Dekun Wu, and Hui Jiang. Freebaseqa: A new factoid QA data set mat","arxiv_match":false}],"id":"2106.09685","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":2},{"title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","arxiv_id":"2402.03300","year":2024,"url":"https://arxiv.org/abs/2402.03300","citing_submissions":3840,"explicit_arxiv_matches":3545,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"pers), pp. 1511– 1524, Albuquerque, New Mexico, April 2025. Association for Computational Linguistics. ISBN 979-8-89176-189-6. doi: 10.18653/v1/2025.naacl-long.71. URL https://aclanthology. org/2025.naacl-long.71/. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. URL https://arxiv.org/abs/ 2402.03300. Antoine Simoulin, Namyong Park, Xiaoyi Liu, and Grey Yang. Memory-efficient fine-tuning of transfo","arxiv_match":true},{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"conflicts in LLMs. In International Conference on Learning Representations, pp. 25142–25161, 2026. URL https://proceedings.iclr.cc/paper_files/paper/ 2026/file/2abb64561fc02424152256eef40f220e-Paper-Conference.pdf. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. DeepSeekMath: Pushing the limits of mathematical reasoning in open language models, 2024. URL https://arxiv.org/abs/2402 .03300v3. arXiv preprint, version 3. Zhennan Shen, Yanshu Li, Qingyu Yin, Chak Tou Leong, Zhilin Wang, Yanxu","arxiv_match":true},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"istics: Human Language Technologies (Volume 1: Long Papers), pp. 5377–5400, 2024. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Hai","arxiv_match":true}],"id":"2402.03300","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":3},{"title":"The Llama 3 Herd of Models","arxiv_id":"2407.21783","year":2024,"url":"https://arxiv.org/abs/2407.21783","citing_submissions":3199,"explicit_arxiv_matches":2879,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"https://proceedings.iclr.cc/paper_files/paper/2026/file/ d368aba36f74776cc7a1079332a31973-Paper-Conference.pdf. Jiading Gai, Shuai Zhang, Xiang Song, Bernie Wang, and George Karypis. Dualkv: Sharedprompt flash attention for efficient rl training with large rollouts and long contexts, 2026. URL https://arxiv.org/abs/2605.15422. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, et al. The llama 3 herd of models, 2024. URL https://arxiv.org/abs/2407.21783. Zhiwei He, Tian Liang, Jiahao Xu, Qiuzhi Liu, Xingyu Chen, Yue Wang, Linfeng Song, Dian Yu, Zhenwe","arxiv_match":true},{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"Cai, Abedelkadir Asi, Wayne Xiong, Yue Dong, and Wen Xiao. Not all heads matter: A head-level kv cache compression method with integrated retrieval and reasoning. In International Conference on Learning Representations, volume 2025, pp. 99269–99290, 2025. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W Mahoney, Yakun S Shao, Kurt Keutze","arxiv_match":true},{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"chine Learning and Systems, 5:768–779, 2023. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv e-prints, pp. arXiv–2407, 2024. Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv prepr","arxiv_match":false}],"id":"2407.21783","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":4},{"title":"Learning transferable visual models from natural language supervision","arxiv_id":"2103.00020","year":2021,"url":"https://arxiv.org/abs/2103.00020","citing_submissions":3171,"explicit_arxiv_matches":181,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":" pattern recognition, pp. 14297–14306, 2023. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 8748–8763. PmLR, 2021. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international","arxiv_match":false},{"id":"zoKRDAQpft","title":"AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval","forum_url":"https://openreview.net/forum?id=zoKRDAQpft","excerpt":" Conference on Computer Vision (ECCV), 2020. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning (ICML), 2021. Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. Colbertv2: Effective and efficient retrieval via ","arxiv_match":false},{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"n CVPR, 2026. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, 2021. Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou. Timechat: A time-sensitive multimodal large language model for long video understanding. In CVPR, 2024. Xiaoqian Shen, Wenxuan","arxiv_match":false}],"id":"2103.00020","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":5},{"title":"Attention Is All You Need","arxiv_id":null,"year":2017,"url":null,"citing_submissions":3089,"explicit_arxiv_matches":0,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"ficient long-context llm inference. arXiv preprint arXiv:2406.10774, 2024. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and ad","arxiv_match":false},{"id":"XIvhtY214J","title":"SOLAR: SVD-Optimized Lifelong Attention for Recommendation","forum_url":"https://openreview.net/forum?id=XIvhtY214J","excerpt":"i: 10.1145/3726302.3730228. URL https://doi.org/10. 1145/3726302.3730228. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need, 2017. URL https://proceedings.neurips.cc/paper/2017/hash/ 3f5ee243547dee91fbd053c1c4a845aa-Abstract.html. Ruoxi Wang, Rakesh Shivanna, Derek Zhiyuan Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed ","arxiv_match":false},{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":" for end-to-end autonomous driving. arXiv preprint arXiv:2603.29163, 2026. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Jianyuan Wang, Minghao Chen, Shangzhan Zhang, Nikita Karaev, Johannes Schönberger, Patrick Labatut, Piotr Bojanowski, David Novotny, Andr","arxiv_match":false}],"id":"ab8508f26511d617","metadata_source":"Curated bibliography catalog / publisher metadata","rank":6},{"title":"Decoupled Weight Decay Regularization","arxiv_id":"1711.05101","year":2017,"url":"https://arxiv.org/abs/1711.05101","citing_submissions":2806,"explicit_arxiv_matches":1020,"examples":[{"id":"AFB7bSu4ee","title":"Gradient–Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks","forum_url":"https://openreview.net/forum?id=AFB7bSu4ee","excerpt":"ntations, 2021b. Qiang Liu, Mengyu Chu, and Nils Thuerey. Config: Towards conflict-free training of physics informed neural networks. In International Conference on Learning Representations, volume 2025, pp. 59531–59566, 2025. Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pp. 3730–3738, 2015. Ilya Loshchilov and Frank Hutter. arXiv:1711.05101, 2017. Decoupled weight decay regularization. arXiv preprint Lu Lu, Pengzhan Jin, Guofei Pang, Zhon","arxiv_match":true},{"id":"IlRPpE9Npn","title":"LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking","forum_url":"https://openreview.net/forum?id=IlRPpE9Npn","excerpt":" Heng Fan, Zhipeng Zhang, Yuqing Huang, Yaowei Wang, Yong Xu, and Haibin Ling. Loratv2: Enabling low-cost temporal modeling in one-stream trackers. In NeurIPS, 2026. Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019. Matthias Müller, Adel Bibi, Silvio Giancola, Salman Alsubaihi, and Bernard Ghanem. TrackingNet: A large-scale dataset and benchmark fo","arxiv_match":false},{"id":"V3eOjjQapB","title":"Fine-Grained and Reliable Cell Classification from Pathology Images","forum_url":"https://openreview.net/forum?id=V3eOjjQapB","excerpt":"nurag J Vaidya, Richard J Chen, Drew FK Williamson, Ahrong Kim, et al. HEST-1k: A dataset for spatial transcriptomics and histology image analysis. In NeurIPS, 2024. Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. Jérôme Lüscher, Nora Koreuber, Jannik Franzen, Fabian H Reith, Claudia Winklmayr, Elias Baumann, Christian M Schürch, Dagmar Kainmüller, and Josef Lorenz Rumberger. PathoCellBenc","arxiv_match":false}],"id":"1711.05101","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":7},{"title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","arxiv_id":"2501.12948","year":2025,"url":"https://arxiv.org/abs/2501.12948","citing_submissions":2763,"explicit_arxiv_matches":1685,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"reasoning models, 2025. URL https://arxiv.org/abs/25 05.00949. BytedTsinghua-SIA. Open-MOPD Data. Hugging Face dataset, 2026. URL https://hu ggingface.co/datasets/BytedTsinghua- SIA/Open- MOPD- Data. Processed RL/OPD prompt mixture and evaluation suite; release manifest inspected at revision 9e897efe3257599d4300e2d5ee865a1cc714af87. DeepSeek-AI. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning, 2025. URL https://arxiv.org/abs/2501.12948v1. arXiv preprint, version 1. Di-viner. LLM-Fusion: Consolidating RLVR capabilities across domains. G","arxiv_match":true},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"nformation processing systems, 27, 2014. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. Minillm: Knowledge distillation of large language models. In International Conference on Learning Representations, volume 2024, pp. 32694–32717, 2024. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. Constructing a multi-hop qa","arxiv_match":true},{"id":"okGS6velwf","title":"Focus-KV: Head-Wise KV Cache Compression Focus On Reasoning Heads","forum_url":"https://openreview.net/forum?id=okGS6velwf","excerpt":", Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, and Zhen Zhang. Deepseek-r1 incentivizes reasoning in llms through reinforcement learning. Nature, 645(8081):633–638, 2025. ISSN 1476-4687. doi: 10.1038/s41586-025-09422-z. URL http://dx.doi.org/10.1038/s41586-025-09422-z. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven B","arxiv_match":false}],"id":"2501.12948","metadata_source":"Cached arXiv API metadata","rank":8},{"title":"ReAct: Synergizing Reasoning and Acting in Language Models","arxiv_id":"2210.03629","year":2022,"url":"https://arxiv.org/abs/2210.03629","citing_submissions":2728,"explicit_arxiv_matches":911,"examples":[{"id":"xb6eQlQ8CB","title":"Light SAM3 Agent: Efficient Agentic Segmentation via Latent NPs Extraction and Action Space Slimming","forum_url":"https://openreview.net/forum?id=xb6eQlQ8CB","excerpt":"reasoning from observations for efficient augmented language models. arXiv preprint arXiv:2305.18323, 2023. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR), 2023. Kai Ye, Xiaotong You, Jianghang Lin, Jiayi Ji, Pingyang Dai, and Liujuan Cao. Evolving, not training: Zero-shot reasoning segmen","arxiv_match":false},{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":". Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In Proceedings of EMNLP, 2018. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum? id=WE_vluYUL-X. Hanrong Zhang, Jingyuan Huang, Kai Mei, Yifei Yao, Zhenting Wang, Chenlu","arxiv_match":false},{"id":"lxLFc3ujqT","title":"RevMMSearch: Evidence-Grounded Identity Revision for Multimodal Search Agents","forum_url":"https://openreview.net/forum?id=lxLFc3ujqT","excerpt":" In International Conference on Machine Learning, 2026. URL https://openreview.net/ forum?id=XZAOCytvKQ. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations, 2023. Xiaohan Yu, Chao Feng, Lang Mei, and Chong Chen. M3 Searcher: Modular multimodal information seeking agency with retrieval-oriented rea","arxiv_match":false}],"id":"2210.03629","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":9},{"title":"Training Verifiers to Solve Math Word Problems","arxiv_id":"2110.14168","year":2021,"url":"https://arxiv.org/abs/2110.14168","citing_submissions":2679,"explicit_arxiv_matches":2272,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"arxiv.org/abs/1604.06174. Sakshi Choudhary, Utkarsh Saxena, and Kaushik Roy. Oasis: Online activation subspace learning for memory-efficient training, 2026. URL https://arxiv.org/abs/2604.09406. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems, 2021. URL https://arxiv.org/ abs/2110.14168. Wei Cui. MetroRLHF: Enabling memory-effective training for on-policy RLHF via adaptive sequence st","arxiv_match":true},{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"ei Xiao, Dawn Song, and Bo Li. Agentpoison: Red-teaming LLM agents via poisoning memory or knowledge bases. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems, 2021. URL https://arxiv.org/ abs/2110.14168. Haowen Dai, Zonghao Ying, Wenfeng Li, Xiangfan Wu, Yisong Xiao, Tianyuan Zhang, Jiaye Lin, Lei Wei","arxiv_match":true},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"g-Kuang Wu, Yun-Nung Chen, and Hsin-Hsi Chen. Self-ICL: Zero-shot incontext learning with self-generated demonstrations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15651–15662, December 2023. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, et ","arxiv_match":true}],"id":"2110.14168","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":10},{"title":"Flow Matching for Generative Modeling","arxiv_id":"2210.02747","year":2022,"url":"https://arxiv.org/abs/2210.02747","citing_submissions":2492,"explicit_arxiv_matches":908,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"p. 7890–7911, 2026c. Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang, Cheng Wang, Sixu Yan, Xinbang Zhang, Xiangyu Li, Ying Zhang, Qian Zhang, et al. Diffusiondrive: Truncated diffusion model for endto-end autonomous driving. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12037–12047. IEEE, 2025. Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022. Lin Liu, Caiyan Jia, Guanyi Yu, Ziying Song, JunQiao Li, Feiyang Jia, Peiliang Wu, Xiaoshuai","arxiv_match":true},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"manan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, pp. 740–755, 2014. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In ICLR, 2023. Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In ICLR, 2023. Zechun Liu, Changsheng Zhao, Igor Fedoro","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"ce Zitnick. Microsoft coco: Common objects in context. In ECCV, pp. 740–755. Springer International Publishing, 2014. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In ICLR, 2023. URL https://openreview.net/ forum?id=PqvMRDCJT9t. Qiang Liu. Rectified flow: A marginal preserving approach to optimal transport. arXiv preprint arXiv:2209.14577, 2022. Yifan Lu, Qi W","arxiv_match":false}],"id":"2210.02747","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":11},{"title":"Qwen3-VL Technical Report","arxiv_id":"2511.21631","year":2025,"url":"https://arxiv.org/abs/2511.21631","citing_submissions":2399,"explicit_arxiv_matches":2245,"examples":[{"id":"BGtau6E80m","title":"Image Editing without Typing","forum_url":"https://openreview.net/forum?id=BGtau6E80m","excerpt":"u, Dunjie Lu, Ruilin Luo, Chenxu Lv, Rui Men, Lingchen Meng, Xuancheng Ren, Xingzhang Ren, Sibo Song, Yuchong Sun, Jun Tang, Jianhong Tu, Jianqiang Wan, Peng Wang, Pengfei Wang, Qiuyue Wang, Yuxuan Wang, Tianbao Xie, Yiheng Xu, Haiyang Xu, Jin Xu, Zhibo Yang, Mingkun Yang, Jianxin Yang, An Yang, Bowen Yu, Fei Zhang, Hang Zhang, Xi Zhang, Bo Zheng, Humen Zhong, Jingren Zhou, Fan Zhou, Jing Zhou, Yuanzhi Zhu, and Ke Zhu. Qwen3-VL Technical Report. arXiv preprint arXiv:2511.21631, 2025. Vineet Bhat, Sungsu Kim, Valts Blukis, Greg Heinrich, Prashanth Krishnamurthy, Ramesh Karri,","arxiv_match":true},{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"terleaved visual-textual tokens. arXiv:2404.03413, 2024a. Kirolos Ataallah, Xiaoqian Shen, Eslam Abdelrahman, Essam Sleiman, Mingchen Zhuge, Jian Ding, Deyao Zhu, Jürgen Schmidhuber, and Mohamed Elhoseiny. Goldfish: Vision-language understanding of arbitrarily long videos. In ECCV, 2024b. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, et al. Qwen3-vl technical report. arXiv:2511.21631, 2025a. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang,","arxiv_match":true},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"n Luo, Chenxu Lv, Rui Men, Lingchen Meng, Xuancheng Ren, Xingzhang Ren, Sibo Song, Yuchong Sun, Jun Tang, Jianhong Tu, Jianqiang Wan, Peng Wang, Pengfei Wang, Qiuyue Wang, Yuxuan Wang, Tianbao Xie, Yiheng Xu, Haiyang Xu, Jin Xu, Zhibo Yang, Mingkun Yang, Jianxin Yang, An Yang, Bowen Yu, Fei Zhang, Hang Zhang, Xi Zhang, Bo Zheng, Humen Zhong, Jingren Zhou, Fan Zhou, Jing Zhou, Yuanzhi Zhu, and Ke Zhu. Qwen3-vl technical report, 2025b. URL https://arxiv.org/abs/2511.21631. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie","arxiv_match":true}],"id":"2511.21631","metadata_source":"Cached arXiv API metadata","rank":12},{"title":"Denoising diffusion probabilistic models","arxiv_id":"2006.11239","year":2020,"url":"https://arxiv.org/abs/2006.11239","citing_submissions":2311,"explicit_arxiv_matches":171,"examples":[{"id":"zPfUFbzLtK","title":"Numerical Calibration for Physics-Informed Diffusion Posterior Sampling","forum_url":"https://openreview.net/forum?id=zPfUFbzLtK","excerpt":"ce? on the discretization mismatch errors in neural operators. In International Conference on Learning Representations, volume 2025, pp. 19360–19384, 2025b. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. Peiyan Hu, Xiaowei Qian, Wenhao Deng, Rui Wang, Haodong Feng, Ruiqi Feng, Tao Zhang, Long Wei, Yue Wang, Zhi-Ming Ma, et al. Fr","arxiv_match":false},{"id":"JQG8LaeAwe","title":"Reinforcement Learning by Change of Measure: A BSDE Theory for Generative Policies","forum_url":"https://openreview.net/forum?id=JQG8LaeAwe","excerpt":"afei Wang, and Lisheng Fan. Generative actor-critic with soft bridge policies. arXiv preprint arXiv:2605.08733, 2026. URL https://arxiv.org/abs/2605. 08733. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, volume 33, pp. 6840– 6851, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/ 4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html. Vineet Jain","arxiv_match":false},{"id":"D2lvweC7MC","title":"When Are Diffusion Recovery Clocks Identifiable? A Spectral Measurement Protocol for Teacher-Forced Denoising","forum_url":"https://openreview.net/forum?id=D2lvweC7MC","excerpt":"i, and Sergio Verdú. Mutual information and minimum mean-square error in gaussian channels. IEEE transactions on information theory, 51(4):1261–1282, 2005. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. Xingchang Huang, Corentin Salaun, Cristina Vasconcelos, Christian Theobalt, Cengiz Oztireli, and Gurprit Singh. Blue noise for ","arxiv_match":false}],"id":"2006.11239","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":13},{"title":"Qwen2.5 Technical Report","arxiv_id":"2412.15115","year":2024,"url":"https://arxiv.org/abs/2412.15115","citing_submissions":2242,"explicit_arxiv_matches":1967,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"g Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https://arxiv.org/abs/2412.15115. Charbel Sakr and Brucek Khailany. Espace: Dimensionality reduction of activations for model compre","arxiv_match":true},{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"e model for multicore architectures. Communications of the ACM, 52(4):65–76, 2009. doi: 10.1145/1498765.1498785. An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jianwei Zhang, Jianxin Ma, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Keqin Chen, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao,","arxiv_match":true},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"planations. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 55562–55590. Curran Associates, Inc., 2024. doi: 10.52202/079017-1765. URL https://proceedings.neurips.cc/paper_files/paper/2024/file/ 647af5f6b2538524f6c047c1d9170fd9-Paper-Conference.pdf. Qwen, :, An Yang, Baosong Yang, and Others. Qwen2.5 technical report. 2025. URL https: //arxiv.org/abs/2412.15115. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish S","arxiv_match":true}],"id":"2412.15115","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":14},{"title":"Reflexion: Language Agents with Verbal Reinforcement Learning","arxiv_id":"2303.11366","year":2023,"url":"https://arxiv.org/abs/2303.11366","citing_submissions":2236,"explicit_arxiv_matches":372,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"e 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7370–7392, 2024. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in neural information processing systems, 36:8634–8652, 2023. Theodore R Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L Griffiths. Cognitive architectures for language agents. arXiv p","arxiv_match":false},{"id":"EUiwYKQQVt","title":"When External Memory Becomes Agent State: Recorded Lineage and Controlled Reuse in LLM Agents","forum_url":"https://openreview.net/forum?id=EUiwYKQQVt","excerpt":"on-flow security. IEEE Journal on Selected Areas in Communications, 21(1):5–19, 2003. doi: 10.1109/JSAC.2002.806121. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems, volume 36, pp. 8634–8652, 2023. doi: 10.52202/075280-0377. URL https://arxiv. org/abs/2303.11366. Saksham Sahai Srivastava and Haoyu He. MemoryG","arxiv_match":false},{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":"Vashurin, and Maxim Panov. Redact: Uncertainty-aware deferral for llm agents. arXiv preprint arXiv:2604.07036, 2026. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems, volume 36, 2023. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. ALFWorld: Aligning te","arxiv_match":false}],"id":"2303.11366","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":15},{"title":"Proximal Policy Optimization Algorithms","arxiv_id":"1707.06347","year":2017,"url":"https://arxiv.org/abs/1707.06347","citing_submissions":2214,"explicit_arxiv_matches":2098,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":" Attanasio, Federico Bianchi, and Dirk Hovy. Xstest: A test suite for identifying exaggerated safety behaviours in large language models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 5377–5400, 2024. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchu","arxiv_match":true},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"ction on the llm response simplex. arXiv preprint arXiv:2605.06139, 2026. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36:53728–53741, 2023. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, A","arxiv_match":true},{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"https://openai.com/index/ introducing-gpt-5-4-mini-and-nano/. Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Haozhan Shen, Peng Liu, Jingcheng Li, Chunxin Fang, Yibo Ma, Jiajia Liao, Qiaoli Shen, Zilun","arxiv_match":true}],"id":"1707.06347","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":16},{"title":"Let's Verify Step by Step","arxiv_id":"2305.20050","year":2023,"url":"https://arxiv.org/abs/2305.20050","citing_submissions":1992,"explicit_arxiv_matches":554,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"an, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 65535–65548, 2026d. URL https://proceedings.iclr.cc/paper_files/paper/2026/file/ 6b4880ced785077159b340710ceb7681-Paper-Conference.pdf. Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step, 2023. URL https://arxiv.org/abs/2305.20050. Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, ","arxiv_match":true},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":" Representations, volume 2024, pp. 29927–29962, 2024. Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step. In International Conference on Learning Representations, volume 2024, pp. 39578–39601, 2024. Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao, Peter Belcak, Mingjie Liu, Min-Hung Chen, Hongxu Yin, Yu","arxiv_match":false},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"ry synthesis. arXiv preprint arXiv:2603.20278, 2026c. Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step. In International Conference on Learning Representations, volume 2024, pp. 39578–39601, 2024. Yu Meng, Mengzhou Xia, and Danqi Chen. Simpo: Simple preference optimization with a reference-free reward","arxiv_match":false}],"id":"2305.20050","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":17},{"title":"Adam: A Method for Stochastic Optimization","arxiv_id":"1412.6980","year":2014,"url":"https://arxiv.org/abs/1412.6980","citing_submissions":1842,"explicit_arxiv_matches":986,"examples":[{"id":"AFB7bSu4ee","title":"Gradient–Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks","forum_url":"https://openreview.net/forum?id=AFB7bSu4ee","excerpt":"202/079017-2442. Youngsik Hwang and Dong-Young Lim. Dual cone gradient descent for training physics-informed neural networks. Advances in Neural Information Processing Systems, 37:98563–98595, 2024. George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning. Nature Reviews Physics, 3(6):422–440, 2021. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andr","arxiv_match":true},{"id":"XXNQ4STUz3","title":"CEGINO: CONTINUOUS EQUIVARIANT GEOMETRY- INFORMED NEURAL OPERATORS FOR 3D PDES","forum_url":"https://openreview.net/forum?id=XXNQ4STUz3","excerpt":" Thickness-aware E(3)-equivariant 3D mesh neural networks. arXiv preprint arXiv:2505.21572, 2025. Sungwon Kim, Juho Song, Seungmin Shin, Guimok Cho, Sangkook Kim, and Chanyoung Park. EqGINO: Equivariant geometry-informed Fourier neural operators for 3D PDEs. arXiv preprint arXiv:2606.03260, 2026. Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations, 2015. URL https://arxiv.org/abs/1412. 6980. Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stu","arxiv_match":true},{"id":"QiYD616GNr","title":"Fast-ZSAD: Patch-wise Adaptive Inference for Efficient Zero-Shot Anomaly Detection","forum_url":"https://openreview.net/forum?id=QiYD616GNr","excerpt":"anguage representation learning with noisy text supervision, 2021. URL https://arxiv.org/abs/2102.05918. Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan. Maple: Multi-modal prompt learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19113–19122, June 2023. Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2017. URL https://arxiv.org/abs/1412.6980. Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei Niu, Xuan Shen, Geng Yuan, Bin","arxiv_match":true}],"id":"1412.6980","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":18},{"title":"Chain-of-thought prompting elicits reasoning in large language models","arxiv_id":"2201.11903","year":2022,"url":"https://arxiv.org/abs/2201.11903","citing_submissions":1821,"explicit_arxiv_matches":198,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"u. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023. Jason Wei, Xuezhi Wang, Dale Schuurmans, et al. Chain of thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, 2022. Jingxuan Wei, Caijun Jia, Xi Bai, Xinglong Xu, Siyuan Li, et al. Ggbench: A geometric generative reasoning benchmark for unified multimoda","arxiv_match":false},{"id":"OaKuNlZFmJ","title":"SCRATCH: Efficient Math Inference for Large Reasoning Models","forum_url":"https://openreview.net/forum?id=OaKuNlZFmJ","excerpt":"onal Linguistics. doi: 10.18653/v1/N19-1421. URL https://aclanthology.org/N 19-1421/. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 24824–24837. Curran Associates, Inc., 2022. doi: 10.522","arxiv_match":false},{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"ws. In International Conference on Learning Representations, volume 2026, pp. 82559–82622, 2026. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837, 2022. Diankun Wu, Fangfu Liu, Yi-Hsin Hung, and Yueqi Duan. Spatial-MLLM: Boosting MLLM capabilities in visual-based spatial intell","arxiv_match":false}],"id":"2201.11903","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":19},{"title":"Highresolution image synthesis with latent diffusion models","arxiv_id":"2112.10752","year":2021,"url":"https://arxiv.org/abs/2112.10752","citing_submissions":1820,"explicit_arxiv_matches":118,"examples":[{"id":"D2lvweC7MC","title":"When Are Diffusion Recovery Clocks Identifiable? A Spectral Measurement Protocol for Teacher-Forced Denoising","forum_url":"https://openreview.net/forum?id=D2lvweC7MC","excerpt":"Heinonen, and Arno Solin. Generative modelling with inverse heat dissipation. arXiv preprint arXiv:2206.13397, 2022. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. Highresolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10684–10695, 2022. Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw, Jason Hartford, and Yoshua Bengio. Le","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"ining Xie. Scalable diffusion models with transformers. In ICCV, 2023. Pexels. Pexels. https://www.pexels.com, 2025. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. Highresolution image synthesis with latent diffusion models. In CVPR, 2022. Seyedmorteza Sadat, Otmar Hilliges, and Romann M. Weber. Eliminating oversaturation and artifacts of high guidance scales in diffusion models. In ICLR, 2025. Tim Salimans and Jonathan","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"ts/oklab/. URL https:// William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. Highresolution image synthesis with latent diffusion models. In CVPR, 2022. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In NeurIPS, 2016. Axel Sauer, Tero Karras, Samuli Laine","arxiv_match":false}],"id":"2112.10752","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":20},{"title":"DINOv2: Learning Robust Visual Features without Supervision","arxiv_id":"2304.07193","year":2023,"url":"https://arxiv.org/abs/2304.07193","citing_submissions":1772,"explicit_arxiv_matches":806,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"luba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024. Jack Parker-Holder and Shlomi Fruchter. Genie 3: A new frontier for world models. Google DeepMind Blog, 2025. URL https://deepmind.google/blog/ genie","arxiv_match":false},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":". OpenAI, :, Sandhini Agarwal, Lama Ahmad, and Others. gpt-oss-120b & gpt-oss-20b model card. 2025. URL https://arxiv.org/abs/2508.10925. Maxime Oquab, Timothée Darcet, Théo Moutakanni, and Others. DINOv2: Learning robust visual features without supervision. 2024. URL https://openreview.net/forum?id= a68SUt6zFt. Featured Certification. Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayana","arxiv_match":false},{"id":"3Wt005uqGW","title":"PGL-3D: Towards Progressive Geometric Learning for 3D Visual Query Localization","forum_url":"https://openreview.net/forum?id=3Wt005uqGW","excerpt":"Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024. Liang Peng, Bohan Tan, Zhipeng Zhang, Haobo Li, Yifan Jiao, Xingping Dong, and Libo Zhang. Towards visual query localization in the 3D world. In Proc","arxiv_match":false}],"id":"2304.07193","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":21},{"title":"Training language models to follow instructions with human feedback","arxiv_id":"2203.02155","year":2022,"url":"https://arxiv.org/abs/2203.02155","citing_submissions":1737,"explicit_arxiv_matches":230,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"long video comprehension. arXiv:2411.13093, 2024. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. NeurIPS, 2022. Jihao Qiu, Lingxi Xie, Xinyue Huo, Qi Tian, and Qixiang Ye. Longvideo-r1: Smart navigation for low-cost long video understanding. In CVPR, 2026. Alec Radford, Jong Wook Kim, Chris Hal","arxiv_match":false},{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"ng Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, volume 35, pp. 27730–27744. Curran Associates, Inc., 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/fi le/b1efde53be364a73914f5","arxiv_match":false},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"tillation. arXiv preprint arXiv:2608.09263, 2026. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35: 27730–27744, 2022. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. Measuring and narrowing the compositionalit","arxiv_match":false}],"id":"2203.02155","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":22},{"title":"Deep residual learning for image recognition","arxiv_id":"1512.03385","year":2015,"url":"https://arxiv.org/abs/1512.03385","citing_submissions":1720,"explicit_arxiv_matches":81,"examples":[{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":" volume 70 of Proceedings of Machine Learning Research, pp. 1321–1330. PMLR, 06–11 Aug 2017. URL https://proceedings.mlr.press/v70/ guo17a.html. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016. doi: 10.1109/CVPR.2016.90. Pengcheng He, Jianfeng Gao, and Weizhu Chen. DeBERTav3: Improving deBERTa usi","arxiv_match":false},{"id":"uTIoY3tprZ","title":"More Information, Less Uncertainty? Monotonic Uncertainty Regularization under Progressive Information","forum_url":"https://openreview.net/forum?id=uTIoY3tprZ","excerpt":" Kilian Q. Weinberger. On calibration of modern neural networks. ArXiv, abs/1706.04599, 2017. URL https://api.semanticscholar.org/ CorpusID:28671436. Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2015. URL https://api.semanticscholar.org/CorpusID:206594692. Dan Hendrycks and Kevin Gimpel. A baseline for dete","arxiv_match":false},{"id":"MpNPoDTf3B","title":"Semantic Fields Are Interfaces: Consumer-Typed Functional Migration for Representation Autoencoders","forum_url":"https://openreview.net/forum?id=MpNPoDTf3B","excerpt":"CLR, 2026. Minghui Guo, Shengqiong Wu, and Hao Fei. V-RAE: Rethinking video latent spaces for generation. arXiv preprint arXiv:2608.13556, 2026. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pp. 770–778, 2016. Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Ju","arxiv_match":false}],"id":"1512.03385","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":23},{"title":"Measuring Mathematical Problem Solving With the MATH Dataset","arxiv_id":"2103.03874","year":2021,"url":"https://arxiv.org/abs/2103.03874","citing_submissions":1665,"explicit_arxiv_matches":753,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"tical dataset for advancing reasoning, 2025. URL https://arxiv.org/abs/2504.11456. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. In J. Vanschoren and S. Yeung (eds.), Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1, 2021. URL https: //datasets-benchmarks-proceedings.neurips.","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"talis, Despina Christou, Joshua Myers, Steven Jones, Keith Lambert, Adam Amos-Binks, Zohreh Dannenhauer, and Dustin Dannenhauer. Memory matters: The need to improve long-term memory in llm-agents. In Proceedings of the AAAI Symposium Series, volume 2, pp. 277–280, 2023. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zh","arxiv_match":true},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"://arxiv.org/abs/2505.08775. Anisha Gunjal, Anthony Wang, Elaine Lau, Vaskar Nath, Yunzhong He, Bing Liu, and Sean Hendryx. Rubrics as rewards: Reinforcement learning beyond verifiable domains. arXiv preprint arXiv:2507.17746, 2025. URL https://arxiv.org/abs/2507.17746. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021. Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar","arxiv_match":true}],"id":"2103.03874","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":24},{"title":"Judging LLM-as-a-judge with MT-Bench and chatbot arena","arxiv_id":"2306.05685","year":2023,"url":"https://arxiv.org/abs/2306.05685","citing_submissions":1632,"explicit_arxiv_matches":278,"examples":[{"id":"ROD8EjYEgO","title":"One Answer to Grade Them All? Reference-Based Evaluation and Its Optimization Effects in Search Agents","forum_url":"https://openreview.net/forum?id=ROD8EjYEgO","excerpt":"096-Paper-Conference.pdf. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph Gonzalez, and Ion Stoica. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 46595–46623. Curran Associates, Inc., 2023. doi: 10.5","arxiv_match":false},{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":"v:2506.05176, 2025. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. In Advances in Neural Information Processing Systems, volume 36, pp. 46595–46623, 2023. Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulić, and Anna Korhonen. Fairer preferences elicit ","arxiv_match":false},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":" long video generation. arXiv preprint arXiv:2608.27922, 2026. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in neural information processing systems, 36:46595–46623, 2023. Hongzhou Zhu, Min Zhao, Guande He, Hang Su, Chongxuan Li, and Jun Zhu. Causal forcing: Autoregressive diffusion distillation ","arxiv_match":false}],"id":"2306.05685","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":25},{"title":"Qwen2.5-VL Technical Report","arxiv_id":"2502.13923","year":2025,"url":"https://arxiv.org/abs/2502.13923","citing_submissions":1580,"explicit_arxiv_matches":1420,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"ei Ding, Chang Gao, Chunjiang Ge, et al. Qwen3-vl technical report. arXiv:2511.21631, 2025a. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2. 5-vl technical report. arXiv:2502.13923, 2025b. Lin Chen, Xilin Wei, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong D","arxiv_match":true},{"id":"nSsaEp75xm","title":"EmbTracker: Traceable Black-box Watermarking for Federated Language Models","forum_url":"https://openreview.net/forum?id=nSsaEp75xm","excerpt":" Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923, 2025. Jieming Bian, Yuanzhe Peng, Lei Wang, Yin Huang, and Jie Xu. A survey on parameter-efficient","arxiv_match":true},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"u. Qwen3-vl technical report, 2025b. URL https://arxiv.org/abs/2511.21631. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report, 2025c. URL https://arxiv.org/abs/2502.13923. Shuhao Gu, Jialing Zhang, Siyuan Zhou, Kevin Yu, Zhaohu Xing, Liangdong Wang, Zhou Cao, Jintao Jia","arxiv_match":true}],"id":"2502.13923","metadata_source":"Cached arXiv API metadata","rank":26},{"title":"Evaluating Large Language Models Trained on Code","arxiv_id":"2107.03374","year":2021,"url":"https://arxiv.org/abs/2107.03374","citing_submissions":1554,"explicit_arxiv_matches":1377,"examples":[{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":" Zhiping Zhang, Zeya Chen, Eryue Xu, Yinuo Yang, Ibrahim Khalilov, Simret A Gebreegziabher, Yanfang Ye, Ziang Xiao, Yaxing Yao, et al. Comparing human oversight strategies for computer-use agents. arXiv preprint arXiv:2604.04918, 2026. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021. Paula J Dalley. A theory of agency law. University of Pittsburgh law review, 72(3), 2011. Ed","arxiv_match":true},{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"Siyu Yuan, Aili Chen, Zhicheng Cai, Weinan Dai, Hongli Yu, Qiying Yu, Xuefeng Li, Jiaze Chen, et al. Enigmata: Scaling logical reasoning in large language models with synthetic verifiable puzzles. arXiv preprint arXiv:2505.19914, 2025. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021. Francois Chollet, Mike Knoop, Gregory Kamradt, Bryan Landers, and Henry Pinkard. Arc-agi-2: ","arxiv_match":true},{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"machine learning agents on machine learning engineering. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview. net/forum?id=6s5uXNWGIh. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021. URL https:// arxiv.org/abs/2107.03374. DeepSeek-AI. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. a","arxiv_match":true}],"id":"2107.03374","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":27},{"title":"Efficient memory management for large language model serving with PagedAttention","arxiv_id":"2309.06180","year":2023,"url":"https://arxiv.org/abs/2309.06180","citing_submissions":1549,"explicit_arxiv_matches":226,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"on Empirical Methods in Natural Language Processing, pp. 25961–25970, 2025. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with PagedAttention. In Proceedings of the 29th Symposium on Operating Systems Principles, pp. 611–626, 2023. Chris Latimer, Nicoló Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, and Naren Rama","arxiv_match":false},{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":"nce with personalized spatial memory-aware assistant in everyday augmented reality. In 2026 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 217–227. IEEE, 2026. Woosuk Kwon et al. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium on operating systems principles, pp. 611–626, 2023. Patrick Lewis et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural inf","arxiv_match":false},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"of the IEEE international conference on computer vision, pp. 706–715, 2017. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium on operating systems principles, pp. 611–626, 2023. Ivan Laptev. On space-time interest points. International journal of computer vision, 64(2):107–123, 2005. Ji","arxiv_match":false}],"id":"2309.06180","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":28},{"title":"Scalable diffusion models with transformers","arxiv_id":"2212.09748","year":2022,"url":"https://arxiv.org/abs/2212.09748","citing_submissions":1544,"explicit_arxiv_matches":139,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":" Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 8748–8763. PmLR, 2021. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4195–4205, 2023. Sucheng Ren, Qihang Yu, Ju He, Alan Yuille, and Liang-Chieh Chen. Grouping first, attending smartly: ","arxiv_match":false},{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"ur Agustsson, and Michael Tschannen. Finite scalar quantization: VQ-VAE made simple. In Proceedings of the International Conference on Learning Representations, 2024. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4195–4205, 2023. Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville. FiLM: Visual reason","arxiv_match":false},{"id":"v4zSwenn2E","title":"EviCal: Learning and Calibrating Evidence for Generalizable AI-Generated Image Detection","forum_url":"https://openreview.net/forum?id=v4zSwenn2E","excerpt":"Using thousands of generators to train fake image detectors. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8245–8257. IEEE, 2025. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4195–4205, 2023. Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery de","arxiv_match":false}],"id":"2212.09748","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":29},{"title":"An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale","arxiv_id":"2010.11929","year":2020,"url":"https://arxiv.org/abs/2010.11929","citing_submissions":1523,"explicit_arxiv_matches":535,"examples":[{"id":"e4gvA0ZGY5","title":"ReasonRSOS: Reasoning Salient Object Segmentation in Remote Sensing Images","forum_url":"https://openreview.net/forum?id=e4gvA0ZGY5","excerpt":"feng Gu. Cross-modal bidirectional interaction model for referring remote sensing image segmentation, 2025. URL https:// arxiv.org/abs/2410.08613. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale, 2021. URL https://arxiv.org/abs/2010.11929. Yubin Gu, Siting Chen, Xiaoshuai Sun, Jiayi Ji, Yiyi Zhou, and Rongrong Ji. Optical remote sensing","arxiv_match":true},{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"967. Bo Dong, Mingchen Zhuge, Ying Wang, Huchuan Bi, and Geng Chen. Accurate camouflaged object detection via mixture convolution and interactive fusion. arXiv preprint arXiv:2101.05687, 2021. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Camouf","arxiv_match":true},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"sovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. URL https: //openreview.net/forum?id=YicbFdNTTy. Ran El-Yaniv and Yair Wiener. On the foundations of noise-free selective classificatio","arxiv_match":false}],"id":"2010.11929","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":30},{"title":"Learning Multiple Layers of Features from Tiny Images","arxiv_id":null,"year":2009,"url":null,"citing_submissions":1445,"explicit_arxiv_matches":0,"examples":[{"id":"NzzGCMwHGU","title":"Hierarchical Symbol Coupling for Generative Modeling and Symbolic Inference","forum_url":"https://openreview.net/forum?id=NzzGCMwHGU","excerpt":"4, 2017. 5 Rostislav Korst and Arip Asadulaev. Adversarial training improves joint energy-based generative modelling. arXiv preprint arXiv:2207.08950, 2022. 5 Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 6 Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio. Maximum entropy generators for energy-based models. arXiv preprint arXiv:1901.08508, 2019. 8 Ziwei Liu, Ping ","arxiv_match":false},{"id":"LBibdel21S","title":"ParaDa: Pretrained Parameters as Data for Federated Few-Shot Learning","forum_url":"https://openreview.net/forum?id=LBibdel21S","excerpt":"et for fine-grained image categorization: Stanford dogs. In CVPR Workshop on Fine-Grained Visual Categorization, 2011. URL https://people.csail.mit.edu/khosla/papers/fgv c2011.pdf. 5 Alex Krizhevsky. Learning multiple layers of features from tiny images. Technical report, University of Toronto, 2009. URL https://cave.cs.toronto.edu/kriz/learning-featu res-2009-TR.pdf. 5 Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, and Eugene Belilovs","arxiv_match":false},{"id":"jJbc2wpXoo","title":"Scaling Online Optimal Transport Minibatches for Flow Matching","forum_url":"https://openreview.net/forum?id=jJbc2wpXoo","excerpt":"asserstein-2 barycenter estimation without minimax optimization. In ICLR, 2021b. Alexander Korotin, Daniil Selikhanovych, and Evgeny Burnaev. Neural optimal transport. In ICLR, 2023. Alex Krizhevsky. Learning multiple layers of features from tiny images. Tech Report, 2009. Tianyi Lin, Nhat Ho, and Michael Jordan. On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms. In ICML, 2019. Tianyi Lin, Nhat Ho, Marco","arxiv_match":false}],"id":"e448dcc530e6ff08","metadata_source":"Curated bibliography catalog / publisher metadata","rank":31},{"title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","arxiv_id":"2503.14476","year":2025,"url":"https://arxiv.org/abs/2503.14476","citing_submissions":1439,"explicit_arxiv_matches":667,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei-Ying Ma, Ya-Qin Zhang, Lin Yan, Yonghui Wu, and Mingxuan Wang. DAPO: An open-source LLM reinforcement learning system at scale. In Advances in Neural Information Processing Systems, volume 38, pp. 113222–113244. Curran Associates, Inc., 2025. doi: 10.52202/085713-3775. URL https://proceedings.neurips.cc/paper_fil es/paper/20","arxiv_match":false},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"ances in neural information processing systems, 36:11809–11822, 2023. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, et al. Dapo: An open-source llm reinforcement learning system at scale. Advances in Neural Information Processing Systems, 38:113222–113244, 2026. Lifan Yuan, Wendi Li, Huayu Chen, Ganqu Cui, Ning Ding, Kaiyan Zhang, Bowen Zhou, Zhiyuan Liu, and Hao Peng. Free process r","arxiv_match":false},{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei-Ying Ma, Ya-Qin Zhang, Lin Yan, Yonghui Wu, and Mingxuan Wang. DAPO: An open-source LLM reinforcement learning system at scale. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview. net/forum?id=2a36EMSSTp. Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Z","arxiv_match":false}],"id":"2503.14476","metadata_source":"Cached arXiv API metadata","rank":32},{"title":"Direct preference optimization: Your language model is secretly a reward model","arxiv_id":"2305.18290","year":2023,"url":"https://arxiv.org/abs/2305.18290","citing_submissions":1439,"explicit_arxiv_matches":200,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"ed rlvr as target-projection on the llm response simplex. arXiv preprint arXiv:2605.06139, 2026. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36:53728–53741, 2023. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv","arxiv_match":false},{"id":"4ihDGJ5CAY","title":"What Early Stopping Hides: Cost-Aware Auditing in Post-Training","forum_url":"https://openreview.net/forum?id=4ihDGJ5CAY","excerpt":"enru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https://arxiv.org/abs/2412.15115. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36:53728–53741, 2023. Christoph Sawade, Niels Landwehr, Steffen Bickel, and Tobias Scheffer. Active risk estimation. In Proceedings of the 27th Int","arxiv_match":false},{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"anguage supervision. In International conference on machine learning, pp. 8748–8763. PmLR, 2021. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36:53728–53741, 2023. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. Highresolution image synthesis with latent ","arxiv_match":false}],"id":"2305.18290","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":33},{"title":"The unreasonable effectiveness of deep features as a perceptual metric","arxiv_id":"1801.03924","year":2018,"url":"https://arxiv.org/abs/1801.03924","citing_submissions":1403,"explicit_arxiv_matches":96,"examples":[{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"apse in vqvaes: Evidence and remedies. In Proceedings of the International Conference on Learning Representations, 2025. Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. Chuanxia Zheng and Andrea Vedaldi. Online clustered codebook. In Proceedings of the IEEE/CVF International","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"h outlier smoothing and per-thread INT4 quantization. In ICML, 2025. URL https://proceedings.mlr.press/v267/zhang25ae.html. Richard Zhang, Phillip Isola, Alexei Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. Zekai Zhang, Deqing Li, Kuan Cao, Yujia Wu, Chenfei Wu, Yu Wu, Liang Peng, Hao Meng, Jiahao Li, Jie Zhang, Kaiyuan Gao, Kun Yan, Lihan Jiang, Ningyuan Tang, Shengming Yin, Tianhe Wu, ","arxiv_match":false},{"id":"ROquSvIhjs","title":"Inverse Garment Design with Visual Agents","forum_url":"https://openreview.net/forum?id=ROquSvIhjs","excerpt":"iffusion: High-fidelity texture transfer for 3d garments generation from in-the-wild images. In ACM SIGGRAPH Asia, 2024. Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595, 2018. Feng Zhou, Ruiyang Liu, Chen Liu, Gaofeng He, Yong-Lu Li, Xiaogang Jin, and Huamin Wang. Design2g","arxiv_match":false}],"id":"1801.03924","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":34},{"title":"Measuring Massive Multitask Language Understanding","arxiv_id":"2009.03300","year":2020,"url":"https://arxiv.org/abs/2009.03300","citing_submissions":1381,"explicit_arxiv_matches":541,"examples":[{"id":"mVJzfFoRun","title":"Checking Leakage Witnesses versus Certifying Bounded Non-Leakage","forum_url":"https://openreview.net/forum?id=mVJzfFoRun","excerpt":"6/0304-3975(85)90144-6. URL https://www.sciencedirect.com/ science/article/pii/0304397585901446. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In International Conference on Learning Representations (ICLR), 2021. URL https://openreview.net/forum?id= d7KBjmI3GmQ. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious cas","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"utational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 8068–8089, 2025. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In The Ninth International Conference on Learning Representations, 2021a. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuri","arxiv_match":false},{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"oi: 10.1137/090771806. Y. Hao, Y. Cao, and L. Mou. Flora: Low-rank adapters are secretly gradient compressors, 2024. D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt. Measuring massive multitask language understanding. In International Conference on Learning Representations (ICLR), 2021. I. Hubara, B. Chmiel, M. Island, R. Banner, J. Naor, and D. Soudry. Accelerated sparse neural training: A provable and efficient","arxiv_match":false}],"id":"2009.03300","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":35},{"title":"Swe-bench: Can language models resolve real-world github issues?","arxiv_id":"2310.06770","year":2023,"url":"https://arxiv.org/abs/2310.06770","citing_submissions":1360,"explicit_arxiv_matches":307,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"lective labeling with provable statistical guarantees. arXiv preprint arXiv:2510.14581, 2025. Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can language models resolve real-world github issues? In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 54107–54157, 2024. URL https://proceedings.iclr.cc","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"ks using pipeline parallelism. Advances in neural information processing systems, 32, 2019. Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan. SWE-bench: Can language models resolve real-world github issues? In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview. net/forum?id=VTF8yNQM66. Kimi, Tongtong Bai, Yifan Bai, Yiping Bao, SH Cai, Yuan Cao, Y Charles, HS","arxiv_match":false},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, and Jiawei Han. s3: You Don’t Need That Much Data to Train a Search Agent via RL. arXiv preprint arXiv:2505.14146, 2025. URL https://arxiv.org/abs/2505.14146. Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. SWE-bench: Can Language Models Resolve Real-World GitHub Issues? In International Conference on Learning Representations, 2024. URL https://arxiv.org/abs/ 2310.06770. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Ö. Arık, Dong Wang, Hamed Zamani, and Ji","arxiv_match":true}],"id":"2310.06770","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":36},{"title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","arxiv_id":"2203.11171","year":2022,"url":"https://arxiv.org/abs/2203.11171","citing_submissions":1299,"explicit_arxiv_matches":478,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"ciation for Computational Linguistics (Volume 1: Long Papers), pp. 6106– 6131, 2024a. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023. Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan","arxiv_match":false},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"iation for Computational Linguistics (Volume 1: Long Papers), pp. 37251–37265, 2026. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023. Jason Wei, Xuezhi Wang, Dale Schuurmans, et al. Chain of thought prompting elicits reasoning in large language models. In ","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"cak, Hanrong Ye, Hongxu Yin, et al. Toolorchestra: Elevating intelligence via efficient model and tool orchestration. arXiv preprint arXiv:2511.21689, 2025. Leslie G Valiant. A bridging model for parallel computation. Communications of the ACM, 33(8): 103–111, 1990. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022. John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik R Narasimh","arxiv_match":true}],"id":"2203.11171","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":37},{"title":"Visual instruction tuning","arxiv_id":"2304.08485","year":2023,"url":"https://arxiv.org/abs/2304.08485","citing_submissions":1248,"explicit_arxiv_matches":99,"examples":[{"id":"e4gvA0ZGY5","title":"ReasonRSOS: Reasoning Salient Object Segmentation in Remote Sensing Images","forum_url":"https://openreview.net/forum?id=e4gvA0ZGY5","excerpt":". Chang Liu, Henghui Ding, and Xudong Jiang. Gres: Generalized referring expression segmentation, 2023a. URL https://arxiv.org/abs/2306.00968. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 34892–34916. Curran Associates, I","arxiv_match":false},{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":"aining for visual language models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 26679–26689. IEEE, 2024. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36:34892–34916, 2023. Zhijian Liu, Ligeng Zhu, Baifeng Shi, Zhuoyang Zhang, Yuming Lou, Shang Yang, Haocheng Xi, Shiyi Cao, Yuxian Gu, Dacheng Li, ","arxiv_match":false},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"lucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023b. Bin Lin, Zhenyu Tang, Yang Ye, Jinfa Huang, Junwu Zhang, Yatian Pang, Peng Jin, Munan Ning, Jiebo Luo, and Li Yuan. Moe-llava: Mixture of experts for large vision-language models. IEEE Transactions on Multimedia, 28:4408–4419, 2026. doi: 10.1109/TMM.2026.3654458. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023. URL https://arxiv.org/abs/2304.08485. Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava-nex","arxiv_match":true}],"id":"2304.08485","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":38},{"title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","arxiv_id":"2209.03003","year":2022,"url":"https://arxiv.org/abs/2209.03003","citing_submissions":1242,"explicit_arxiv_matches":465,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3719–3728, 2026a. Lin Liu, Ziying Song, Caiyan Jia, Hangjun Ye, Xiaoshuai Hao, Long Chen, et al. Driveworld-vla: Unified latent-space world modeling with vision-language-action for autonomous driving. arXiv preprint arXiv:2602.06521, 2026b. Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. Yushan Liu, Tianxiong Lv, Bohua Wang, Hangqi Fan, Chenxu Zhao, He Zheng, Xuchang Zhong, Yifa","arxiv_match":true},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":" pp. 740–755, 2014. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In ICLR, 2023. Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In ICLR, 2023. Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort. SpinQuant: LLM quantization w","arxiv_match":false},{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023a. URL https://openreview.net/forum?id=xzEtNSuDJk. Xingchao Liu, Chengyue Gong, and qiang liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In The Eleventh International Conference on Learning Representations, 2023b. URL https://openreview.net/forum?id=XVjTT1nw5z. Justin Lovelace, Christian K Belardi, Sofian Zalouk, Adhitya Polavaram, S","arxiv_match":false}],"id":"2209.03003","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":39},{"title":"Wan: Open and Advanced Large-Scale Video Generative Models","arxiv_id":"2503.20314","year":2025,"url":"https://arxiv.org/abs/2503.20314","citing_submissions":1234,"explicit_arxiv_matches":1166,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"t llm inference. arXiv preprint arXiv:2406.10774, 2024. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314, 2025. Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett, and Eitan Farchi. Convex bounds on","arxiv_match":true},{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":" arXiv:2511.23429, 2025. URL https: //arxiv.org/abs/2511.23429. Tencent Hunyuan. HunyuanVideo 1.5. https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5, 2025. Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. Diffusion Models Are Real-Time Game Engines. arXiv preprint arXiv:2408.14837, 2024. URL https://arxiv.org/abs/2408.14837. Wan Team, Ang Wang, Baole Ai, Bin Wen, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314, 2025. Jiahao Wang, Yufeng Yuan, Rujie Zheng, Youtian Lin, Jian Gao, Lin-Zhuo Chen, Yajie Bao, Chan","arxiv_match":true},{"id":"goQTywWWdx","title":"VestEdit: Inversion-Free Video Editing via Spatio-Temporal Transport Stabilization","forum_url":"https://openreview.net/forum?id=goQTywWWdx","excerpt":"on, pp. 12822–12831, 2025. Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and-play diffusion features for text-driven image-to-image translation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1921–1930, 2023. Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314, 2025. Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo, Yue Ma, Nisha Huang, Yuxin Chen, Xiu Li, a","arxiv_match":true}],"id":"2503.20314","metadata_source":"Cached arXiv API metadata","rank":40},{"title":"Language models are few-shot learners","arxiv_id":"2005.14165","year":2020,"url":"https://arxiv.org/abs/2005.14165","citing_submissions":1214,"explicit_arxiv_matches":136,"examples":[{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"arXiv preprint arXiv:2412.08905, 2024. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. Younghoon Byun and Youngjoo Lee. Partially-structured transformer pruning with patch-limited xor-gate compression for stall-fre","arxiv_match":false},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"uage monkeys: Scaling inference compute with repeated sampling. arXiv preprint arXiv:2407.21787, 2024. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pp. 1877–1901, 2020. Meiqi Chen, Fandong Meng, and Jie Zhou. Figure it out: Improve the frontier of reasoning with executable visual ","arxiv_match":false},{"id":"AzPpBFMquF","title":"Segment Anything In-Context via Visual Demonstrations","forum_url":"https://openreview.net/forum?id=AzPpBFMquF","excerpt":"cessing Systems, 38:60884–60937, 2026. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object","arxiv_match":false}],"id":"2005.14165","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":41},{"title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","arxiv_id":"2005.11401","year":2020,"url":"https://arxiv.org/abs/2005.11401","citing_submissions":1213,"explicit_arxiv_matches":123,"examples":[{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":"cs: main volume, pp. 874–880, 2021. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33: 9459–9474, 2020. Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, and Sebast","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"AHaystack, 2024. GitHub repository. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33: 9459–9474, 2020. Xianxuan Long, Zhikai Chen, Shenglai Zeng, Shouren Wang, Kai Guo, and Jiliang Tang. Memtrace: Probing what final accuracy miss","arxiv_match":false},{"id":"lMGk9GrpHK","title":"Enabling Validation for Robust Few-Shot Recognition","forum_url":"https://openreview.net/forum?id=lMGk9GrpHK","excerpt":", 2022. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive nlp tasks. In Advances in Neural Information Processing Systems, 2020. Alexander Cong Li, Ellis Langham Brown, Alexei A Efros, and Deepak Pathak. Internet explorer: Targeted representation learning on the open","arxiv_match":false}],"id":"2005.11401","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":42},{"title":"Self-Refine: Iterative refinement with self-feedback","arxiv_id":"2303.17651","year":2023,"url":"https://arxiv.org/abs/2303.17651","citing_submissions":1213,"explicit_arxiv_matches":226,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"p on RL from World Feedback, 2026. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, et al. Self-refine: Iterative refinement with self-feedback. In Advances in Neural Information Processing Systems, volume 36, pp. 46534–46594, 2023. Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-vqa: A visual question answering ben","arxiv_match":false},{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"n, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refinement with self-feedback. In Advances in Neural Information Processing Systems, volume 36, pp. 46534–46594, 2023. doi: 10.52202/075280-2019. URL https://proceedings.neurips.cc/paper_files/paper/2023/hash/ 91edff07232fb1b55a5","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"esentations, volume 2026, pp. 146197–146246, 2026. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback. Advances in neural information processing systems, 36:46534–46594, 2023. Vasilije Markovic, Lazar Obradovic, Laszlo Hajdu, and Jovan Pavlovic. Optimizing the interface between knowledge graphs and l","arxiv_match":false}],"id":"2303.17651","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":43},{"title":"Score-Based Generative Modeling through Stochastic Differential Equations","arxiv_id":"2011.13456","year":2020,"url":"https://arxiv.org/abs/2011.13456","citing_submissions":1202,"explicit_arxiv_matches":379,"examples":[{"id":"zPfUFbzLtK","title":"Numerical Calibration for Physics-Informed Diffusion Posterior Sampling","forum_url":"https://openreview.net/forum?id=zPfUFbzLtK","excerpt":"rint arXiv:2411.15101, 2024. Dule Shu, Zijie Li, and Amir Barati Farimani. A physics-informed diffusion model for high-fidelity flow field reconstruction. Journal of Computational Physics, 478:111972, 2023. Ian Naismith Sneddon. Fourier transforms. Courier Corporation, 1995. Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon. Solving inverse problems in medical imag","arxiv_match":true},{"id":"gsDsA2ZjkO","title":"PhysDEM: Physics-Defined Energy-Matching Diffusion for Spatiotemporal Field Generation under Scarce Measurements","forum_url":"https://openreview.net/forum?id=gsDsA2ZjkO","excerpt":" Dyffusion: A dynamics-informed diffusion model for spatiotemporal forecasting. Advances in neural information processing systems, 36:45259–45287, 2023. Igal Sason and Sergio Verdú. f -divergence inequalities. IEEE Transactions on Information Theory, 62(11):5973–6006, 2016. Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. Andrew M Stuart. Inverse problems: a bayesian perspective. Acta numerica, 19:451–559, 2010. ","arxiv_match":true},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"ing Xie. Improved baselines with representation autoencoders. arXiv preprint arXiv:2605.18324, 2026. Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In ICLR, 2021. Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. YFCC100M: The new data in multimedia research. Communications ","arxiv_match":false}],"id":"2011.13456","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":44},{"title":"Think You Have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","arxiv_id":"1803.05457","year":2018,"url":"https://arxiv.org/abs/1803.05457","citing_submissions":1197,"explicit_arxiv_matches":1129,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"l Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id= iUjGNJzrF1. KuanChao Chu, Yi-Pei Chen, and Hideki Nakayama. Exploring and controlling diversity in llmagent conversation. arXiv preprint arXiv:2412.21102, 2024. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018. Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving fact","arxiv_match":true},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"ristina Toutanova. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2924–2936, 2019. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? Try ARC, the AI2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthi","arxiv_match":true},{"id":"OaKuNlZFmJ","title":"SCRATCH: Efficient Math Inference for Large Reasoning Models","forum_url":"https://openreview.net/forum?id=OaKuNlZFmJ","excerpt":"raj, Kiri Wagstaff, and Jerry Zhu (eds.), Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 9487–9499. PMLR, 13–19 Jul 2025. URL https://proceedings.mlr.press/v267/chen25bx.html. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try ARC, the AI2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018. URL https://arxiv.org/abs/1803.0 5457. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark ","arxiv_match":true}],"id":"1803.05457","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":45},{"title":"DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence","arxiv_id":"2606.19348","year":2026,"url":"https://arxiv.org/abs/2606.19348","citing_submissions":1181,"explicit_arxiv_matches":901,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"eek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems,","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":" Aaron Chatterji, Thomas Cunningham, David J Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, and Kevin Wadman. How people use chatgpt. Technical report, National Bureau of Economic Research, 2025. DeepSeek-AI. Deepseek-v3.2: Pushing the frontier of open large language models, 2025b. URL https://arxiv.org/abs/2512.02556. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. URL https://arxiv.org/abs/2606.19348. Xinle Deng, Yida Xue, Yijun Chen, Mingjun Mao, Ruobin Zhong, Buqiang Xu, Jizhan Fang, Haoming Xu, ","arxiv_match":true},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"n Wang, Yue Wang, Linghe Kong, Weiran Huang, and Lichao Sun. First sft, second rl, third upt: Continual improving multi-modal llm reasoning via unsupervised post-training. Advances in Neural Information Processing Systems, 38:62293–62318, 2026b. Anyi Xu, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, Chenchen Ling, et al. Deepseek-v4: Towards highly efficient milliontoken context intelligence. arXiv preprint arXiv:2606.19348, 2026. Renjun Xu and Yang Yan. Agent skills for large language models: Architecture, acquisition, s","arxiv_match":true}],"id":"2606.19348","metadata_source":"Cached arXiv API metadata","rank":46},{"title":"OpenVLA: An Open-Source Vision-Language-Action Model","arxiv_id":"2406.09246","year":2024,"url":"https://arxiv.org/abs/2406.09246","citing_submissions":1181,"explicit_arxiv_matches":584,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":"in Balakrishna, Suraj Nair, Rafael Rafailov, Ethan P Foster, Pannag R Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn. Openvla: An open-source vision-language-action model. In Pulkit Agrawal, Oliver Kroemer, and Wolfram Burgard (eds.), Proceedings of The 8th Conference on Robot Learning, volume 270 of Proceedings of Machine Learning Research, pp. 2679–2713. PMLR, 06–09","arxiv_match":false},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"ng generalization in robot learning through video world models, 2025. URL https://arxiv.org/abs/2505.12705. Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn. Openvla: An open-source vision-language-action model, 2024. URL https://arxiv.org/ abs/2406.09246. Moo Jin Kim et al. Cosmos policy: Fine-tuning video models for visuomotor control and planning, 20","arxiv_match":true},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"Wang, Yongqiang Dou, Yanjun Chen, Li FeiFei, Anima Anandkumar, Yuke Zhu, and Linxi Fan. VIMA: General robot manipulation with multimodal prompts. In International Conference on Machine Learning, 2023. URL https: //arxiv.org/abs/2210.03094. Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246, 2024. Matthew Lai, Keegan Go, Zhibin Li, Torsten Kröger, Stefan Schaal, Kelsey Allen, and Jonatha","arxiv_match":true}],"id":"2406.09246","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":47},{"title":"Layer Normalization","arxiv_id":"1607.06450","year":2016,"url":"https://arxiv.org/abs/1607.06450","citing_submissions":1165,"explicit_arxiv_matches":327,"examples":[{"id":"zoKRDAQpft","title":"AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval","forum_url":"https://openreview.net/forum?id=zoKRDAQpft","excerpt":"are binary masks indicating selected and candidate positions, NormSt (·) standardizes each token’s feedback by the response statistics of the selected set, MHA(q, k, v) is multi-head attention, LN is layer normalization, and et is a learnable decision-context embedding for step t. The policy first lets candidate tokens attend to the selected tokens: \u0001 Ht = LN HCt + MHA(HCt , HSt , HSt ) . (17) The feedback matrix is","arxiv_match":false},{"id":"IlRPpE9Npn","title":"LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking","forum_url":"https://openreview.net/forum?id=IlRPpE9Npn","excerpt":"tween TU updates. The response P weights the normalized search features X̄ to update this state as ∑︁ Pi LN(X̄i ) + . (10) h = (1 − g)h + g i ∑︁ i Pi The index i denotes a search position, LN denotes layer normalization, and ⊙ below denotes element-wise multiplication. Confidence gate g ∈ [0, 1] is computed from the difference between the response peak and spatial mean through a learned affine transformation followe","arxiv_match":false},{"id":"LBibdel21S","title":"ParaDa: Pretrained Parameters as Data for Federated Few-Shot Learning","forum_url":"https://openreview.net/forum?id=LBibdel21S","excerpt":" classifiers (Chen et al., 2019; Tian et al., 2020). FiT (Shysheya et al., 2023) and NormFiT (Motamedi et al., 2025) enable parameter-efficient adaptation through feature-wise affine adapters and pre-layer-normalization updates, respectively. These methods still estimate target classifiers primarily from the available support examples. They do not treat a pretrained source classification head as a reusable semantic-","arxiv_match":false}],"id":"1607.06450","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":48},{"title":"Distilling the Knowledge in a Neural Network","arxiv_id":"1503.02531","year":2015,"url":"https://arxiv.org/abs/1503.02531","citing_submissions":1153,"explicit_arxiv_matches":1021,"examples":[{"id":"IlRPpE9Npn","title":"LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking","forum_url":"https://openreview.net/forum?id=IlRPpE9Npn","excerpt":"ion transformers for efficient object tracking. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6694–6703. IEEE, 2024. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv, 2015. Lingyi Hong, Jinglun Li, Xinyu Zhou, Shilin Yan, Pinxue Guo, Kaixun Jiang, Zhaoyu Chen, Shuyong Gao, Runze Li, Xingdong Sheng, et al. General compression framework for efficient transfo","arxiv_match":false},{"id":"QiYD616GNr","title":"Fast-ZSAD: Patch-wise Adaptive Inference for Efficient Zero-Shot Anomaly Detection","forum_url":"https://openreview.net/forum?id=QiYD616GNr","excerpt":"ation, 2015. URL https://arxiv.org/abs/1411. 5752. Jianfang He, Min Cao, Silong Peng, and Qiong Xie. RareCLIP: Rarity-Aware Online Zero-Shot Industrial Anomaly Detection. In International Conference on Computer Vision, pp. 24478– 24487, 2025. doi: 10.1109/ICCV51701.2025.02269. URL https://mlanthology.org/ iccv/2025/he2025iccv-rareclip/. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network, 2015. URL https://arxiv.org/abs/1503.02531. Yanning Hou, Jingyuan Zhang, Xiaoyun Wang, Qixiang Ma, Sihang Zhou, and Ke Xu. Keep the needle, pr","arxiv_match":true},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"Ninth International Conference on Learning Representations, 2021a. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the MATH dataset. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1, 2021b. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015. Presented at the NIPS 2014 Deep Learning and Representation Learning Workshop. Cheng-Ping Hs","arxiv_match":true}],"id":"1503.02531","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":49},{"title":"ImageNet: A Large-Scale Hierarchical Image Database","arxiv_id":null,"year":2009,"url":null,"citing_submissions":1142,"explicit_arxiv_matches":0,"examples":[{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"ang Wang. Scalable training for vector-quantized networks with 100% codebook utilization. arXiv preprint arXiv:2509.10140, 2025. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 248–255, 2009. Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image ","arxiv_match":false},{"id":"lMGk9GrpHK","title":"Enabling Validation for Robust Few-Shot Recognition","forum_url":"https://openreview.net/forum?id=lMGk9GrpHK","excerpt":"ilson, and Ryan Mukherjee. Functional map of the world. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao. Clip-adapter: Better v","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"Gao, and Zizheng Pan. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437, 2025. URL https://arxiv.org/abs/2412.19437. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pp. 248–255, 2009. Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson. RedCaps: Web-curated image-text data created by the people, for the people. In NeurIPS Datasets and Benchmarks","arxiv_match":false}],"id":"fc0de89c84bd4b6b","metadata_source":"Curated bibliography catalog / publisher metadata","rank":50},{"title":"$π_{0.5}$: a Vision-Language-Action Model with Open-World Generalization","arxiv_id":"2504.16054","year":2025,"url":"https://arxiv.org/abs/2504.16054","citing_submissions":1136,"explicit_arxiv_matches":624,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, and Sergey Levine. Zero-shot robotic manipulation with pretrained image-editing diffusion models. In International Conference on Learning Representations (ICLR), 2024b. Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Manuel Y. Galliker, et al. π0.5 : a vision-languageaction model with open-world generalization. arXiv preprint arXiv:2504.16054, 2025. Yuhui Chen, Shuai Tian, Shugao Liu, Yingting Zhou, Haoran Li, and Dongbin Zhao. Conrft: A re","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":" Karl Pertsch, Allen Z. Ren, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, and Ury Zhilinsky. π0.5 : a Vision-Language-Action Model with Open-World Generalization. In Proceedings of The 9th Conference on Robot Learning, volume 305 of Proceedings of Machine Learning Research, pp. 17– 40. PMLR, 2025. URL https://proceedings.mlr.press/v305/black25a.html. Junhao C","arxiv_match":false},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":" Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, et al. pi06: a vla that learns from experience. arXiv preprint arXiv:2511.14759, 2025a. Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. pi05: a vision-language-action model with open-world generalization. arXiv preprint arXiv:2504.16054, 2025b. Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, ","arxiv_match":true}],"id":"2504.16054","metadata_source":"Cached arXiv API metadata","rank":51},{"title":"π0: A Vision-Language-Action Flow Model for General Robot Control","arxiv_id":"2410.24164","year":2024,"url":"https://arxiv.org/abs/2410.24164","citing_submissions":1123,"explicit_arxiv_matches":705,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":" André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, et al. Paligemma: A versatile 3b vlm for transfer. arXiv preprint arXiv:2407.07726, 2024. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. π0 : A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164, 2024a. Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, an","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"ah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, and Ury Zhilinsky. π0 : A visionlanguage-action flow model for general robot control. arXiv preprint arXiv:2410.24164, 2024. URL https://arxiv.org/abs/2410.24164. Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Robe","arxiv_match":true},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":" Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. pi0: A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164, 2024. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, K","arxiv_match":true}],"id":"2410.24164","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":52},{"title":"Microsoft coco: Common objects in context","arxiv_id":"1405.0312","year":2014,"url":"https://arxiv.org/abs/1405.0312","citing_submissions":1098,"explicit_arxiv_matches":78,"examples":[{"id":"zoKRDAQpft","title":"AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval","forum_url":"https://openreview.net/forum?id=zoKRDAQpft","excerpt":"t multimodal retrieval and ranking. arXiv preprint arXiv:2601.04720, 2026. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision (ECCV), 2014. Rui Meng, Ziyan Jiang, Ye Liu, Mingyi Su, Xinyi Yang, Yuepeng Fu, Can Qin, Zeyuan Chen, Ran Xu, Caiming Xiong, Yingbo Zhou, Wenhu Chen, and Se","arxiv_match":false},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"t multimodal retrieval and ranking. arXiv preprint arXiv:2601.04720, 2026. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision – ECCV 2014, pp. 740–755, 2014. Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your mu","arxiv_match":false},{"id":"AzPpBFMquF","title":"Segment Anything In-Context via Visual Demonstrations","forum_url":"https://openreview.net/forum?id=AzPpBFMquF","excerpt":"on. In European conference on computer vision, pp. 280–296. Springer, 2022. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740–755. Springer, 2014. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings ","arxiv_match":false}],"id":"1405.0312","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":53},{"title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","arxiv_id":"2305.16291","year":2023,"url":"https://arxiv.org/abs/2305.16291","citing_submissions":1097,"explicit_arxiv_matches":649,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"ng Papers), pp. 8416–8439, 2025. Xiangru Tang, Tianrui Qin, Tianhao Peng, Ziyang Zhou, Daniel Shao, Tingting Du, Xinming Wei, Peng Xia, Fang Wu, He Zhu, et al. Agent kb: Leveraging cross-domain experience for agentic problem solving. arXiv preprint arXiv:2507.06229, 2025. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023. Zheng Wang, Zhongyang Li, Zeren Jiang, Dandan Tu, and Wei Shi. Crafting personalized agents ","arxiv_match":true},{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":"rning. In International Conference on Learning Representations, 2021. Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang. AdaPlanner: Adaptive planning from feedback with language models. In Advances in Neural Information Processing Systems, volume 36, 2023. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023. Menghua Wu, Cai Zhou, Stephen Bates, and Tommi Jaakkola. Thought calibration: Efficient and ","arxiv_match":true},{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"cement learning. arXiv preprint arXiv:2605.06130, 2026. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in neural information processing systems, 36:8634–8652, 2023. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023. Kangrui Wang, Pingyue Zhang, Zihan Wang, Yaning Gao, Linjie Li, Qineng Wang, Hanyang Chen, Y","arxiv_match":true}],"id":"2305.16291","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":54},{"title":"On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes","arxiv_id":"2306.13649","year":2023,"url":"https://arxiv.org/abs/2306.13649","citing_submissions":1072,"explicit_arxiv_matches":190,"examples":[{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":" Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, and Olivier Bachem. On-policy distillation of language models: Learning from selfgenerated mistakes. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. URL https://openreview. net/forum?id=3zKtaqxLhW. Saba Ahmadi, P","arxiv_match":false},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":" Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, and Olivier Bachem. On-policy distillation of language models: Learning from selfgenerated mistakes. In International Conference on Learning Representations, volume 2024, pp. 21246–21263, 2024. Rahul K. Arora, Jason Wei, Rebecca Soskin Hicks, Preston Bowman, Joaquin Quiñonero-Candela, Foivos Tsimpo","arxiv_match":false},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"aximum a posteriori policy optimisation. arXiv preprint arXiv:1806.06920, 2018. Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, and Olivier Bachem. On-policy distillation of language models: Learning from selfgenerated mistakes. In International Conference on Learning Representations, volume 2024, pp. 21246–21263, 2024. Jose A Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and ","arxiv_match":false}],"id":"2306.13649","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":55},{"title":"RoFormer: Enhanced transformer with rotary position embedding","arxiv_id":"2104.09864","year":2021,"url":"https://arxiv.org/abs/2104.09864","citing_submissions":1039,"explicit_arxiv_matches":236,"examples":[{"id":"AzPpBFMquF","title":"Segment Anything In-Context via Visual Demonstrations","forum_url":"https://openreview.net/forum?id=AzPpBFMquF","excerpt":"ournal of roentgenology, 174(1):71–74, 2000. Nathan Silb, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European conference on computer vision, pp. 746–760. Springer, 2012. SINE. Github. https://github.com/aim-uofa/SINE/blob/main/sine/model/ model.py#L118, 2024. commit: a7a989c. J Su, Y Lu, S Pan, A Murtadha, B Wen, and YL Roformer. Enhanced transformer with rotary position embedding. arXiv preprint arXiv:2104.09864, 2021. Tim Thornton. Tacit knowledge as the unifying factor in evidence based medicine and clinical","arxiv_match":true},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"ansNet V2: An Effective Deep Network Architecture for Fast Shot Transition Detection. arXiv preprint arXiv:2008.04838, 2020. Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. RoFormer: Enhanced Transformer with Rotary Position Embedding. Neurocomputing, 568:127063, 2024. Yao Teng, Yue Wu, Han Shi, Xuefei Ning, Guohao Dai, Yu Wang, Zhenguo Li, and Xihui Liu. DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis. arXiv pr","arxiv_match":false},{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":"mar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In ICLR, 2020. Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 2024. Mark Veillette, Siddharth Samsi, and Chris Mattioli. Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology. In NeurIPS, 2020. V","arxiv_match":false}],"id":"2104.09864","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":56},{"title":"Toolformer: Language models can teach themselves to use tools","arxiv_id":"2302.04761","year":2023,"url":"https://arxiv.org/abs/2302.04761","citing_submissions":990,"explicit_arxiv_matches":150,"examples":[{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"41586-023-06924-6. URL https://doi.org/10.1038/s41586-023-06924-6. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. In Advances in Neural Information Processing Systems, volume 36, pp. 68539–68551, 2023. URL https://proceedings.neurips.cc/paper/2023/ hash/d842425e4bf79ba039352da0f658a906-Abstract-Conference.html.","arxiv_match":false},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"ational Conference on Learning Representations, 2024. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. Proceedings of Advances in neural information processing systems, pp. 68539–68551, 2023. Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units","arxiv_match":false},{"id":"ROquSvIhjs","title":"Inverse Garment Design with Visual Agents","forum_url":"https://openreview.net/forum?id=ROquSvIhjs","excerpt":"ances in Neural Information Processing Systems, 2024. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. In Advances in Neural Information Processing Systems, 2023. Gaurav Sharma, Wencheng Wu, and Edul N. Dalal. The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and ","arxiv_match":false}],"id":"2302.04761","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":57},{"title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","arxiv_id":"1706.08500","year":2017,"url":"https://arxiv.org/abs/1706.08500","citing_submissions":978,"explicit_arxiv_matches":75,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":" Kolter, and Kaiming He. Mean flows for one-step generative modeling. arXiv preprint arXiv:2505.13447, 2025. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et","arxiv_match":false},{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"tion with distributional matching: A theoretical and empirical study. arXiv preprint arXiv:2506.15078, 2025. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of the Advances in Neural Information Processing Systems, 2017. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"Hyper-connected transformers for high-fidelity pixel-space diffusion. arXiv preprint arXiv:2605.15741, 2026. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, 2017. Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS Workshop, 2021. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In","arxiv_match":false}],"id":"1706.08500","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":58},{"title":"Denoising diffusion implicit models","arxiv_id":"2010.02502","year":2020,"url":"https://arxiv.org/abs/2010.02502","citing_submissions":970,"explicit_arxiv_matches":335,"examples":[{"id":"goQTywWWdx","title":"VestEdit: Inversion-Free Video Editing via Spatio-Temporal Transport Stabilization","forum_url":"https://openreview.net/forum?id=goQTywWWdx","excerpt":" for zero-shot text-based video editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15932–15942, 2023. Litu Rout, Yujia Chen, Nataniel Ruiz, Constantine Caramanis, Sanjay Shakkottai, and Wen-Sheng Chu. Semantic image inversion and editing using rectified stochastic differential equations. arXiv preprint arXiv:2410.10792, 2024. Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. arXiv prepr","arxiv_match":true},{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":" Conference on Computer Vision (ICCV), Oct 2017. Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, and Tom Goldstein. Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks. Advances in Neural Information Processing Systems, 34:6695–6706, 2021. Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv:2010.02502, October 2020. URL https://arxiv.org/abs/2010.02502. Yunzhou Song, Long Le, Yong-Hyun Park, Jie Wang, Junyao Shi, Lingjie Liu, Jiatao Gu, Eric Eaton, D","arxiv_match":true},{"id":"ZLwbkenbR8","title":"Belief-Based Risk Reduction for Robust Reinforcement Learning under Adversarial Observation Perturbations","forum_url":"https://openreview.net/forum?id=ZLwbkenbR8","excerpt":"nce Review, 34:100199, 2019. R. Tyrrell Rockafellar and Stanislav Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2(3):21–41, 2000. Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021a. URL https://openreview.net/ forum?id=St1giarCHLP. Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, ","arxiv_match":false}],"id":"2010.02502","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":59},{"title":"SWE-agent: Agent-computer interfaces enable automated software engineering","arxiv_id":"2405.15793","year":2024,"url":"https://arxiv.org/abs/2405.15793","citing_submissions":970,"explicit_arxiv_matches":255,"examples":[{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":":2505.09388, 2025a. Jingkang Yang et al. Egolife: Towards egocentric life assistant. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 28885–28900, 2025b. John Yang et al. Swe-agent: Agent-computer interfaces enable automated software engineering. Advances in Neural Information Processing Systems, 37:50528–50652, 2024. Shunyu Yao et al. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022. Woongyeo","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"haran Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022. John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik R Narasimhan, and Ofir Press. SWE-agent: Agent-computer interfaces enable automated software engineering. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https://arxiv.org/abs/2405.15793. Xiyuan Yang, Jiaru Zou, Rui Pan, Ruizhong Qiu, Pan Lu, Shizhe Diao, Jindong Jiang, Hanghang Tong, ","arxiv_match":true},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":", Bairu Hou, Wei Wei, Yujia Bao, and Shiyu Chang. Kvlink: Accelerating large language models via efficient kv cache reuse. arXiv preprint arXiv:2502.16002, 2025. John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik R Narasimhan, and Ofir Press. SWE-agent: Agent-computer interfaces enable automated software engineering. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024b. URL https://arxiv.org/abs/2405.15793. Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated delta networks: Improving mamba2 with delta ru","arxiv_match":true}],"id":"2405.15793","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":60},{"title":"GPQA: A Graduate-Level Google-Proof Q&A Benchmark","arxiv_id":"2311.12022","year":2023,"url":"https://arxiv.org/abs/2311.12022","citing_submissions":959,"explicit_arxiv_matches":448,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"ets_and_Benchmarks_Track.html. Datasets and Benchmarks Track. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. GPQA: A graduate-level Google-proof Q&A benchmark. In First Conference on Language Modeling, 2024. URL https://openreview .net/forum?id=Ti67584b98. Zixuan Ren, Jinliang Lu, Junhong Wu, Yang Zhao, Dai Dai, Hua Wu, Haifeng Wang, and Chengqing Zong. En","arxiv_match":false},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":", Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https://arxiv.org/abs/2412.15115. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman. Gpqa: A graduate-level google-proof q&a benchmark. arXiv preprint arXiv:2311.12022, 2023. Paul Röttger, Hannah Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hov","arxiv_match":true},{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"technical report, 2025. URL https://arxiv.org/abs/2412.15115. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. GPQA: A graduate-level google-proof q&a benchmark. In First Conference on Language Modeling, 2024. URL https://openreview. net/forum?id=Ti67584b98. Stephane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured pr","arxiv_match":false}],"id":"2311.12022","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":61},{"title":"Classifier-Free Diffusion Guidance","arxiv_id":"2207.12598","year":2022,"url":"https://arxiv.org/abs/2207.12598","citing_submissions":951,"explicit_arxiv_matches":648,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, 2017. Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS Workshop, 2021. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020. Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. Simple diffusion: ","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, 2017. Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS Workshop, 2021. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020. Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. Simple diffusion: ","arxiv_match":false},{"id":"oq0BXtA19v","title":"Test-Time Compositional Generalization in Diffusion Models via Prototype Discovery","forum_url":"https://openreview.net/forum?id=oq0BXtA19v","excerpt":"1, 1988. doi: 10.1016/0010-0277(88)90031-5. Amirata Ghorbani, James Wexler, James Y. Zou, and Been Kim. Towards automatic concept-based explanations. In Advances in Neural Information Processing Systems, volume 32, 2019. Geoffrey E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771–1800, 2002. doi: 10.1162/089976602760128018. Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Adva","arxiv_match":true}],"id":"2207.12598","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":62},{"title":"Scaling rectified flow transformers for high-resolution image synthesis","arxiv_id":"2403.03206","year":2024,"url":"https://arxiv.org/abs/2403.03206","citing_submissions":950,"explicit_arxiv_matches":89,"examples":[{"id":"D2lvweC7MC","title":"When Are Diffusion Recovery Clocks Identifiable? A Spectral Measurement Protocol for Teacher-Forced Denoising","forum_url":"https://openreview.net/forum?id=D2lvweC7MC","excerpt":".ai/ 2024/09/02/spectral-autoregression.html. Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine learning, 2024. Fabian Falck, Teodora Pandeva, Kiarash Zahirnia, Rachel Lawrence, Richard Turner, Edward Meeds, Javier Zazo, and Sushrut Karmalkar.","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"at gans on image synthesis. In NeurIPS, 2021. Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In ICML, 2024. Funing Fu, Tenghui Wang, Guanyu Zhou, Junyong Cen, and Qichao Zhu. Jlt: Clean-latent prediction in latent diffusion transformers. arXiv preprint arXiv:2605.27102, 2026. Anne Gagneux, ","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"ahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling rectified flow transformers for high-resolution image synthesis. In ICML, 2024. Weichen Fan, Chenyang Si, Junhao Song, Zhenyu Yang, Yinan He, Long Zhuo, Ziqi Huang, Ziyue Dong, Jingwen He, Dongwei Pan, Yi Wang, Yuming Jiang, Yaohui Wang, Peng Gao, Xinyuan Chen, H","arxiv_match":false}],"id":"2403.03206","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":63},{"title":"Policy Distillation","arxiv_id":"1511.06295","year":2015,"url":"https://arxiv.org/abs/1511.06295","citing_submissions":947,"explicit_arxiv_matches":55,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"preprint, version 1. Jingang Zhou, Yuyi Zhou, Haiyang Guo, Xukai Wang, Shuai Feng, Sirui Gao, Jian Xu, Qingpei Guo, and Xu-Yao Zhang. Teacher should think ahead: Adaptive continuations for reliable onpolicy distillation, 2026b. URL https://arxiv.org/abs/2609.22254v1. arXiv preprint, version 1. A Post-training adapts pretrained models through supervised fine-tuning (SFT), reinforcement learning, and distillation. SFT","arxiv_match":false},{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"istillation. CoRR, abs/2601.07568, 2026. doi: 10.48550/ARXIV.2601.07568. URL https://doi.org/10.48550/arXiv.2601. 07568. Haolin Ren, Ziyang Huang, Chenhao Yuan, Jun Zhao, and Kang Liu. Trace-based on-policy distillation for masked diffusion language models. CoRR, abs/2607.16872, 2026. doi: 10.48550/ ARXIV.2607.16872. URL https://doi.org/10.48550/arXiv.2607.16872. Xingyu Su, Jacob Helwig, Shubham Parashar, Atharv Cha","arxiv_match":false},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"ubric rewards, and generation and optimization settings reported above. Its rollout and update budgets also match: 16 queries with eight responses each per update, for 712 updates over two epochs. On-policy distillation comparison. We include on-policy distillation (OPD) as a supplementary comparison of supervision schemes. Following the standard OPD procedure (Agarwal et al., 2024), we use Qwen3.5-397B-A17B as the ","arxiv_match":false}],"id":"1511.06295","metadata_source":"arXiv abstract page, verified 2026-10-11","rank":64},{"title":"Image Quality Assessment: From Error Visibility to Structural Similarity","arxiv_id":null,"year":2004,"url":null,"citing_submissions":945,"explicit_arxiv_matches":0,"examples":[{"id":"ROquSvIhjs","title":"Inverse Garment Design with Visual Agents","forum_url":"https://openreview.net/forum?id=ROquSvIhjs","excerpt":"uman clothing with semantic annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600–612, 2004. Fangsheng Weng, Junhao Chen, Xiang Li, Jie Qin, Hanzhong Guo, Shaochun Hao, and Xiaoguang Han. Garmentgpt: Compositional garment pattern ","arxiv_match":false},{"id":"ugeUCOyVn0","title":"FlowTalker: Causal Streaming Audio-Driven 3D Head Animation via Factorized Motion Modeling with Rectified Flow","forum_url":"https://openreview.net/forum?id=ugeUCOyVn0","excerpt":"er: Unifying identity, lip sync, emotion, and spatial dynamics in expressive 3d talking avatars. arXiv preprint arXiv:2602.10516, 2026. Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600– 612, 2004. Yue Wu, Xuanhong Chen, Yufan Wu, Wen Li, Yuxi Lu, and Kairui Feng. Fastavatar: Towards unified and fast 3d avatar reconstruction with lar","arxiv_match":false},{"id":"lfm8frx0XK","title":"Perception or Prior? Legibility-Guided Path Selection for Text Image Restoration in the Wild","forum_url":"https://openreview.net/forum?id=lfm8frx0XK","excerpt":"with pure synthetic data. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 1905–1914, 2021. Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600–612, 2004. doi: 10.1109/TIP.2003.819861. Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world i","arxiv_match":false}],"id":"01a1956db98964aa","metadata_source":"Curated bibliography catalog / publisher metadata","rank":65},{"title":"GPT-4 Technical Report","arxiv_id":"2303.08774","year":2023,"url":"https://arxiv.org/abs/2303.08774","citing_submissions":940,"explicit_arxiv_matches":852,"examples":[{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":" Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. Anthropic. Claude opus 4.6. https://www.anthropic.com/news/claude-opus-4-6, 2026a. Anthropic","arxiv_match":true},{"id":"r1zsoUu97b","title":"Advancing the Calibration-Compute Frontier of LLM Classifiers via Asymmetric Duos","forum_url":"https://openreview.net/forum?id=r1zsoUu97b","excerpt":"ovember 2018. Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. Large language models: A survey. ArXiv, abs/2402.06196, 2024. OpenAI. Gpt-4 technical report. ArXiv, abs/2303.08774, 2023. Pouya Pezeshkpour and Estevam Hruschka. Large language models sensitivity to the order of options in multiple-choice questions. In Findings of the Association for Comput","arxiv_match":false},{"id":"w8aZY88Jbh","title":"ReVA: A New Scene-Centric Dataset for Remote Sensing Video Question Answering","forum_url":"https://openreview.net/forum?id=w8aZY88Jbh","excerpt":" Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu","arxiv_match":true}],"id":"2303.08774","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":66},{"title":"LIBERO: Benchmarking knowledge transfer for lifelong robot learning","arxiv_id":"2306.03310","year":2023,"url":"https://arxiv.org/abs/2306.03310","citing_submissions":933,"explicit_arxiv_matches":163,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"eventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=PqvMRDCJT9t. Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, qiang liu, Yuke Zhu, and Peter Stone. LIBERO: Benchmarking knowledge transfer for lifelong robot learning. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023a. URL https://openreview.net/forum?id=xzEtNSuDJk. Xingchao Liu, Chengyue Gong, and qiang liu","arxiv_match":false},{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":"ndy Zeng. Code as policies: Language model programs for embodied control. In arXiv preprint arXiv:2209.07753, 2022. Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 44776–44791. Curran Associates, Inc., 2023. doi: 10.5","arxiv_match":false},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"ultirobot reaching with graph neural networks and reinforcement learning. Science Robotics, 10(106):eads1204, 2025. Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. Advances in Neural Information Processing Systems, 36:44776–44791, 2023. Songming Liu, Bangguo Li, Kai Ma, Lingxuan Wu, Hengkai Tan, Xiao Ouyang, Hang Su, and Jun Zhu. Rdt2: Exploring the scaling li","arxiv_match":false}],"id":"2306.03310","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":67},{"title":"Diffusion policy: Visuomotor policy learning via action diffusion","arxiv_id":"2303.04137","year":2023,"url":"https://arxiv.org/abs/2303.04137","citing_submissions":918,"explicit_arxiv_matches":121,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"t al. Pseudo-simulation for autonomous driving. arXiv preprint arXiv:2506.04218, 2025. Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research, 44(10-11):1684–1704, 2025. Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, and Andreas Geiger. Transfuser: Imitation with tran","arxiv_match":false},{"id":"oq0BXtA19v","title":"Test-Time Compositional Generalization in Diffusion Models via Prototype Discovery","forum_url":"https://openreview.net/forum?id=oq0BXtA19v","excerpt":"terpretable image recognition. In Advances in Neural Information Processing Systems (NeurIPS), 2019. Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. In Robotics: Science and Systems, 2023. doi: 10.15607/RSS.2023.XIX.026. URL https://www. roboticsproceedings.org/rss19/p026.html. Dorin Comaniciu and Peter Meer. Mean shift: A robust approach toward","arxiv_match":false},{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"g method for vla models via consistency policy. arXiv preprint arXiv:2502.05450, 2025. Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research, 44(10-11):1684–1704, 2025. Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning u","arxiv_match":false}],"id":"2303.04137","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":68},{"title":"MemGPT: Towards LLMs as Operating Systems","arxiv_id":"2310.08560","year":2023,"url":"https://arxiv.org/abs/2310.08560","citing_submissions":913,"explicit_arxiv_matches":758,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"eting of the Association for Computational Linguistics, pp. 13851–13870, 2024. NVIDIA. NVIDIA A100 Tensor Core GPU. https://www.nvidia.com/en-us/ data-center/a100/, 2020. Accessed: 2025-04-01. OpenAI. Introducing GPT-5.5. https://openai.com/index/ introducing-gpt-5-5/, 2026. Accessed: 2026-08-30. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, and Joseph E. Gonzalez. MemGPT: Towards LLMs as operating systems. arXiv preprint arXiv:2310.08560, 2023. Wenbo Pan, Shujie Liu, Xiangyang Zhou, Shiwei Zhang, Wanlu Shi, Mirror Xu, and Xiaohua Jia. ","arxiv_match":true},{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"pp. 94327–94354, 2026. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pp. 1–22, 2023. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Joseph E Gonzalez. Memgpt: Towards llms as operating systems. arXiv preprint arXiv:2310.08560, 2023. Qwen Team. Qwen3.6-27B: Flagship-level coding in a 27B dense model, April 2026. URL https: /","arxiv_match":true},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"om/index/ introducing-gpt-5-2/, December 2025. Accessed September 7, 2026. OpenAI. Introducing gpt-5.4 mini and nano. https://openai.com/index/ introducing-gpt-5-4-mini-and-nano/, 2026a. OpenAI. tiktoken: A fast bpe tokenizer for use with openai’s models. https://github.com/ openai/tiktoken, 2026b. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Joseph E Gonzalez. Memgpt: Towards llms as operating systems. arXiv preprint arXiv:2310.08560, 2023. Cheng Qian, Zuxin Liu, Akshara Prabhakar, Zhiwei Liu, Jianguo Zhang, Haolin Chen, Heng Ji, W","arxiv_match":true}],"id":"2310.08560","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":69},{"title":"Hellaswag: Can a machine really finish your sentence?","arxiv_id":"1905.07830","year":2019,"url":"https://arxiv.org/abs/1905.07830","citing_submissions":911,"explicit_arxiv_matches":125,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"g, et al. Big Bird: Transformers for longer sequences. Advances in Neural Information Processing Systems, 33:17283–17297, 2020. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. HellaSwag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4791–4800, 2019. Peitian Zhang, Zheng Liu, Shitao Xiao, Ninglu Shao, Qiwei Ye, and Zhicheng Dou. Long ","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"ipline multimodal understanding and reasoning benchmark for expert agi. In CVPR, 2024. Zhengrong Yue, Haiyu Zhang, Xiangyu Zeng, Boyu Chen, Chenting Wang, Shaobin Zhuang, Lu Dong, KunPeng Du, Yi Wang, Limin Wang, and Yali Wang. Uniflow: A unified pixel flow tokenizer for visual understanding and generation. In ICLR, 2026. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for languag","arxiv_match":true},{"id":"hAJ2b6a8w1","title":"CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning","forum_url":"https://openreview.net/forum?id=hAJ2b6a8w1","excerpt":"geon: Pruning weights that cancel one another in neural networks. In International Conference on Machine Learning (ICML), 2022. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. HellaSwag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019. Mingyang Zhang, Hao Chen, Chunhua Shen, Zhen Yang, Linlin Ou, Xinyi Yu, and Bohan Zhuang. LoRA","arxiv_match":false}],"id":"1905.07830","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":70},{"title":"WebArena: A realistic web environment for building autonomous agents","arxiv_id":"2307.13854","year":2023,"url":"https://arxiv.org/abs/2307.13854","citing_submissions":906,"explicit_arxiv_matches":263,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"n Neural Information Processing Systems, 38:105816–105879, 2026. Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, et al. Webarena: A realistic web environment for building autonomous agents. In International Conference on Learning Representations, volume 2024, pp. 15585–15606, 2024. Lianghui Zhu, Xinggang Wang, and Xinlong Wang. Judgelm: Fine-tuned large language models are scalable jud","arxiv_match":false},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"ce on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, et al. Webarena: A realistic web environment for building autonomous agents. In Proceedings of The 12th International Conference on Learning Representations, 2024. A D ETAILS OF B UDGET- CONSTRAINED S KILL M INING This appendix provides additional details for the budget-cons","arxiv_match":false},{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"2024. Hanzhang Zhou, Xu Zhang, Panrong Tong, Jianan Zhang, Liangyu Chen, Quyu Kong, Chenglin Cai, Chen Liu, Yue Wang, Jingren Zhou, et al. Mai-ui technical report: Real-world centric foundation gui agents. arXiv preprint arXiv:2512.22047, 2025. Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, et al. Webarena: A realistic web environment for building autonomous agents. arXiv preprint arXiv:2307.13854, 2023. Sibo Zhu, Wenyi Wu, Kun Zhou, Stephen Wang, and Biwei Huang. Hybrid self-evolving structured","arxiv_match":true}],"id":"2307.13854","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":71},{"title":"DINOv3","arxiv_id":"2508.10104","year":2025,"url":"https://arxiv.org/abs/2508.10104","citing_submissions":901,"explicit_arxiv_matches":723,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"23. Accessed: May, 2023. Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Hervé Jégou, Patrick Labatut, and Piotr Bojanowski. Dinov3. arXiv preprint arXiv:2508.10104, 2025. Przemysław Skurowski, Hassan Abdulameer, Jakub Błaszczyk, Tomasz Depta, Adam Kornacki, and P","arxiv_match":true},{"id":"v4zSwenn2E","title":"EviCal: Learning and Calibrating Evidence for Generalizable AI-Generated Image Detection","forum_url":"https://openreview.net/forum?id=v4zSwenn2E","excerpt":"eng Qin, Yuheng Ji, Renshuai Tao, Yuxuan Tian, Yuyang Liu, Yipu Wang, and Xiaolong Zheng. Scaling up ai-generated image detection with generator-aware prototypes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 43008–43017, 2026. Oriane Siméoni, Huy V Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, et al. Dinov3. arXiv preprint arXiv:2508.10104, 2025. Chunli Song, Jie Liu, Peiyang Wang, Ying Huang, Guixuan Zhang, Zhi Zeng, and Shuwu Zhang. Du","arxiv_match":true},{"id":"QiYD616GNr","title":"Fast-ZSAD: Patch-wise Adaptive Inference for Efficient Zero-Shot Anomaly Detection","forum_url":"https://openreview.net/forum?id=QiYD616GNr","excerpt":" Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Hervé Jégou, Patrick Labatut, and Piotr Bojanowski. Dinov3, 2025. URL https://arxiv.org/ abs/2508.10104. Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, and Nanning Zh","arxiv_match":true}],"id":"2508.10104","metadata_source":"Cached arXiv API metadata","rank":72},{"title":"Hotpotqa: A dataset for diverse, explainable multi-hop question answering","arxiv_id":"1809.09600","year":2018,"url":"https://arxiv.org/abs/1809.09600","citing_submissions":898,"explicit_arxiv_matches":66,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"ence on Language Modeling, 2024. URL https://openreview.net/forum? id=BAakY1hNKS. Zhilin Yang, Peng Qi, Saenz Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In Proceedings of EMNLP, 2018. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. In The Eleventh","arxiv_match":false},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"reerpo: Tree relative policy optimization. arXiv preprint arXiv:2506.05183, 2025b. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 conference on empirical methods in natural language processing, pp. 2369–2380, 2018. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan","arxiv_match":false},{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":"tional Conference on Learning Representations, volume 2026, pp. 88170–88210, 2026. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 conference on empirical methods in natural language processing, pp. 2369–2380, 2018. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan","arxiv_match":false}],"id":"1809.09600","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":73},{"title":"Rt-2: Vision-language-action models transfer web knowledge to robotic control","arxiv_id":"2307.15818","year":2023,"url":"https://arxiv.org/abs/2307.15818","citing_submissions":888,"explicit_arxiv_matches":325,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":"Finn, Kumar Avinava Dubey, Danny Driess, Tianli Ding, Krzysztof Marcin Choromanski, Xi Chen, Yevgen Chebotar, Justice Carbajal, Noah Brown, Anthony Brohan, Montserrat Gonzalez Arenas, and Kehang Han. Rt-2: Vision-language-action models transfer web knowledge to robotic control. In Jie Tan, Marc Toussaint, and Kourosh Darvish (eds.), Proceedings of The 7th Conference on Robot Learning, volume 229 of Proceedings of Machine Learning Research, pp. 2165–2183. PMLR, 06–09 Nov 20","arxiv_match":false},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":" large robotic datasets, 2025. URL https://arxiv.org/abs/2504.02792. Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. In Conference on Robot Learning, pp. 2165–2183. PMLR, 2023. ","arxiv_match":false},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"print arXiv:2608.25864, 2026. URL https://arxiv.org/abs/2608. 25864. Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. In Conference on Robot Learning, pp. 2165–2183. PMLR, 2023. A We first describe the conditions, atomic operations, and phase prompts. We then give the policy implementation, complete results, and sc","arxiv_match":false}],"id":"2307.15818","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":74},{"title":"Generative agents: Interactive simulacra of human behavior","arxiv_id":"2304.03442","year":2023,"url":"https://arxiv.org/abs/2304.03442","citing_submissions":870,"explicit_arxiv_matches":127,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"ing collusion in cooperative multi-agent systems. arXiv preprint arXiv:2602.15198, 2026. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pp. 1–22, 2023. doi: 10.1145/3586183.3606763. Zhe Ren, Yibo Yang, Yimeng Chen, Zijun Zhao, Benshuo Fu, Zhih","arxiv_match":false},{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":" M⋆ : Every task deserves its own memory harness. arXiv preprint arXiv:2604.11811, 2026. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pp. 1–22, 2023. Sidharth Pulipaka, Oliver Chen, Manas Sharma, Taaha Saleem Bajwa, Vyas Raina, and Ivaxi She","arxiv_match":false},{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"International Conference on Learning Representations, volume 2026, pp. 94327–94354, 2026. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pp. 1–22, 2023. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Jos","arxiv_match":false}],"id":"2304.03442","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":75},{"title":"Locating and editing factual associations in GPT","arxiv_id":"2202.05262","year":2022,"url":"https://arxiv.org/abs/2202.05262","citing_submissions":858,"explicit_arxiv_matches":133,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":": A task of fictitious unlearning for LLMs. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=B41hNBoWLo. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Guilherme Penedo, Hynek Kydlı́ček, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, Thomas Wolf, et al. The f","arxiv_match":false},{"id":"JwUvLVBgt7","title":"Do Vision-Language Models Use the Regions They Cite? A Step-Specific Intervention Test","forum_url":"https://openreview.net/forum?id=JwUvLVBgt7","excerpt":"o Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: Common objects in context. In European Conference on Computer Vision, 2014. Zujing Liu, Junwen Pan, Qi She, Yuan Gao, and Guisong Xia. On the faithfulness of visual thinking: Measurement and enhancement. arXiv preprint arXiv:2510.23482, 2025. Kevin Meng et al. Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems, 2022. arXiv:2202.05262. Bayar Menzat, Maximilian Süss, Ruizhi Wang, Benno Steinegger, Thomas Lukasiewicz, and OanaMaria Ca","arxiv_match":true},{"id":"W1xeiKanFu","title":"Do Correct Answers Have Stable Latent Knowledge Support?","forum_url":"https://openreview.net/forum?id=W1xeiKanFu","excerpt":" not to trust language models: Investigating effectiveness and limitations of parametric and non-parametric memories. arXiv preprint, 2022. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in gpt. Advances in neural information processing systems, 35:17359–17372, 2022. Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. Mass-editing memory in a transformer. In Intern","arxiv_match":false}],"id":"2202.05262","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":76},{"title":"Program Synthesis with Large Language Models","arxiv_id":"2108.07732","year":2021,"url":"https://arxiv.org/abs/2108.07732","citing_submissions":858,"explicit_arxiv_matches":809,"examples":[{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":" arXiv:2204.01691, 2022. Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, and Lihua Lei. Learn then test: Calibrating predictive algorithms to achieve risk control. arXiv preprint arXiv:2110.01052, 2021. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021. Jiechen Chen, Sangwoo Park, and Osvaldo Simeone. Knowing when to stop: Delay-adaptive spikin","arxiv_match":true},{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"3. Anthropic. Claude opus 4.6. https://www.anthropic.com/news/claude-opus-4-6, 2026a. Anthropic. Claude opus 4.7. https://www.anthropic.com/news/claude-opus-4-7, 2026b. Anthropic. Claude sonnet claude-sonnet-4-6, 2026c. 4.6. https://www.anthropic.com/news/ Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Z","arxiv_match":true},{"id":"sFUbqvhx3m","title":"SyntaxBPE: Learning Which Code Boundaries to Cross","forum_url":"https://openreview.net/forum?id=sFUbqvhx3m","excerpt":"ation for efficient LLM training. In Findings of the Association for Computational Linguistics: ACL 2026, pp. 41610–41621, 2026. doi: 10.18653/v1/ 2026.findings-acl.2068. URL https://aclanthology.org/2026.findings-acl. 2068/. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. Program synthesis with large language models, 2021. URL https://arxiv.org/abs/2108.07732. Kaj Bostrom and Greg Durrett. Byte pair encoding is suboptimal for language model pretraining. In ","arxiv_match":true}],"id":"2108.07732","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":77},{"title":"Gemma 3 Technical Report","arxiv_id":"2503.19786","year":2025,"url":"https://arxiv.org/abs/2503.19786","citing_submissions":819,"explicit_arxiv_matches":743,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"g Shan, Ran He, and Xing Sun. Video-MME: The first-ever comprehensive evaluation benchmark of multi-modal LLMs in video analysis. arXiv preprint arXiv:2405.21075, 2024. URL https://arxiv.org/abs/2405.21075. Gemini Robotics Team. Gemini robotics: Bringing AI into the physical world. arXiv preprint arXiv:2503.20020, 2025. URL https://arxiv.org/abs/2503.20020. Gemma Team. Gemma 3 Technical Report. arXiv preprint arXiv:2503.19786, 2025. URL https: //arxiv.org/abs/2503.19786. GLM-V Team. GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents. arXiv preprint a","arxiv_match":true},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"ofei Qiao, Huajun Chen, and Ningyu Zhang. Lightmem: Lightweight and efficient memory-augmented generation. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 98706–98729, 2026. URL https://proceedings.iclr.cc/paper_files/paper/2026/file/ a05b72653ec5b473732129829ae04195-Paper-Conference.pdf. Gemma Team. Gemma 3 technical report, 2025. URL https://arxiv.org/abs/2503. 19786. Gemma Team. Gemma 4 technical report, 2026. URL https://arxiv.org/abs/2607. 02770. GLM-5 Team. Glm","arxiv_match":true},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"n Senter, Eli Collins, Joelle Barral, Zoubin Ghahramani, Raia Hadsell, Yossi Matias, D. Sculley, Slav Petrov, Noah Fiedel, Noam Shazeer, Oriol Vinyals, Jeff Dean, Demis Hassabis, Koray Kavukcuoglu, Clement Farabet, Elena Buchatskaya, Jean-Baptiste Alayrac, Rohan Anil, Dmitry, Lepikhin, Sebastian Borgeaud, Olivier Bachem, Armand Joulin, Alek Andreev, Cassidy Hardin, Robert Dadashi, and Léonard Hussenot. Gemma 3 technical report, 2025. URL https://arxiv.org/abs/2503.19786. Qwen Team. Qwen3.5-omni technical report, 2026. URL https://arxiv.org/abs/2604. 15804. Jintao Tong","arxiv_match":true}],"id":"2503.19786","metadata_source":"Cached arXiv API metadata","rank":78},{"title":"LiveCodeBench: Holistic and contamination free evaluation of large language models for code","arxiv_id":"2403.07974","year":2024,"url":"https://arxiv.org/abs/2403.07974","citing_submissions":804,"explicit_arxiv_matches":289,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"26/file/ a15a2ece7f0663d1ba7db91103ac61c9-Paper-Conference.pdf. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida I. Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code. In Y. Yue, A. Garg, N. Peng, F. Sha, and R. Yu (eds.), International Conference on Learning Representations, volume 2025, pp. 58791–58831, 2025. URL https://proceedings.iclr.cc/paper_files/paper/202","arxiv_match":false},{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":", Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum ?id=6t0Kwf8-jrj. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. LiveCodeBench: Holistic and contamination free evaluation of large language models for code, 2024. URL https://arxiv.org/abs/2403 .07974v2. arXiv preprint, version 2. Yaxuan Li, Yuxin Zuo, Bingxiang He, Jinqian Zhang, Chaojun Xiao, Cheng ","arxiv_match":true},{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"26752, 2026. Dong Huang, Yuhao Qing, Weiyi Shang, Heming Cui, and Jie M Zhang. Effibench: Benchmarking the efficiency of automatically generated code. Advances in Neural Information Processing Systems, 37:11506–11544, 2024. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code. arXiv preprint arXiv:2403.07974, 2024. Graham Kendall, Andrew Parkes, and Kristian Spoerer. A survey of np-complete puzzles. ICGA j","arxiv_match":true}],"id":"2403.07974","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":79},{"title":"Lost in the middle: How language models use long contexts","arxiv_id":"2307.03172","year":2023,"url":"https://arxiv.org/abs/2307.03172","citing_submissions":798,"explicit_arxiv_matches":71,"examples":[{"id":"K39aqWW934","title":"When Does LLM Reasoning Compositionality Break and Recover?","forum_url":"https://openreview.net/forum?id=K39aqWW934","excerpt":"fy step by step. In The twelfth international conference on learning representations, 2023. Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. Transactions of the association for computational linguistics, 12:157–173, 2024. Elliot Meyerson and Xin Qiu. Position: Scaling llm agents requires asymptotic analysis with llm primitives. In Forty-","arxiv_match":false},{"id":"PEoM2nDAcy","title":"EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation","forum_url":"https://openreview.net/forum?id=PEoM2nDAcy","excerpt":"hr-based reasoning tasks via reinforcement learning. arXiv preprint arXiv:2505.24105, 2025. Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. Transactions of the association for computational linguistics, 12:157–173, 2024. De Rong Loh, Elliot D Hill, Nan Liu, Geraldine Dawson, and Matthew M Engelhard. Limitations of binary classification ","arxiv_match":false},{"id":"6Zkm41r219","title":"REx: Small Language Model Agents with Recursive Execution and Subtree Compression","forum_url":"https://openreview.net/forum?id=6Zkm41r219","excerpt":"ning, volume 235 of Proceedings of Machine Learning Research, pp. 24370–24391. PMLR, 2024. Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12:157–173, 2024a. doi: 10.1162/tacl a 00638. URL https://aclanthology.org/2024.tacl-1.9/. Zechun Liu, Changsheng Zhao, Forrest Iandola","arxiv_match":false}],"id":"2307.03172","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":80},{"title":"GPT-4o System Card","arxiv_id":"2410.21276","year":2024,"url":"https://arxiv.org/abs/2410.21276","citing_submissions":794,"explicit_arxiv_matches":647,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"hou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. In CVPR, 2025. Gemini Team Google. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv:2403.05530, 2024. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv:2410.21276, 2024. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, ","arxiv_match":true},{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"812, 2026a. Zihan Huang, Junda Wu, Tong Yu, Qianqi Yan, Rohan Surana, Uttaran Bhattacharya, Lina Yao, Xin Eric Wang, and Julian McAuley. Skill-cmib: Multimodal agent skill for consistent action via conditional multimodal information bottleneck. arXiv preprint arXiv:2605.08526, 2026b. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. Guanyu Jiang, Zhaochen Su, Xiaoye Qu, and Yi R Fung. Xskill: Continual learning from experie","arxiv_match":true},{"id":"do6Wfg04EK","title":"ATTEST: Evidence-Grounded Reinforcement Learning for Long-Audio Temporal Understanding","forum_url":"https://openreview.net/forum?id=do6Wfg04EK","excerpt":"In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE, 2025. Hokuto Munakata, Takehiro Imamura, Taichi Nishimura, and Tatsuya Komatsu. Castella: Long audio dataset with captions and temporal boundaries. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 15352–15356. IEEE, 2026. OpenAI et al. Gpt-4o system card, 2024. URL https://arxiv.org/abs/2410.21276. Karol J. Piczak. ESC: Dataset for Environmental Sound Classification. In Proceedings of the 23rd A","arxiv_match":true}],"id":"2410.21276","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":81},{"title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","arxiv_id":"2305.10601","year":2023,"url":"https://arxiv.org/abs/2305.10601","citing_submissions":785,"explicit_arxiv_matches":105,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":". React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in neural information processing systems, 36:11809–11822, 2023. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, et","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"hang, Markus J Buehler, et al. Recursive multi-agent systems. arXiv preprint arXiv:2604.25917, 2026. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in neural information processing systems, 36:11809–11822, 2023. Alex L Zhang, Tim Kraska, and Omar Khattab. Recursive language models. arXiv preprint arXiv:2512.24601, 2025. A R ELATED W OR","arxiv_match":false},{"id":"pmpPDsnDu8","title":"SteadyStop: Risk-Aware Early Stopping for Efficient Reasoning","forum_url":"https://openreview.net/forum?id=pmpPDsnDu8","excerpt":"International Conference on Machine Learning, 2026b. URL https: //icml.cc/virtual/2026/poster/66249. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. In Advances in Neural Information Processing Systems, volume 36, pp. 11809–11822, 2023. doi: 10.52202/075280-0517. URL https://proceedings.neurips.cc/paper_files/paper/2023/hash/2 71db9922b8d1f4dd7a","arxiv_match":false}],"id":"2305.10601","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":82},{"title":"Bert: Pre-training of deep bidirectional transformers for language understanding","arxiv_id":"1810.04805","year":2018,"url":"https://arxiv.org/abs/1810.04805","citing_submissions":784,"explicit_arxiv_matches":66,"examples":[{"id":"nSsaEp75xm","title":"EmbTracker: Traceable Black-box Watermarking for Federated Language Models","forum_url":"https://openreview.net/forum?id=nSsaEp75xm","excerpt":"ng pre-trained encoders. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp. 579–593, 2022. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 201","arxiv_match":false},{"id":"wslxAq80Kb","title":"Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models","forum_url":"https://openreview.net/forum?id=wslxAq80Kb","excerpt":"sarial weights for secure fine-tuning. In Findings of the Association for Computational Linguistics: ACL 2026, pp. 16540–16559, 2026. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp. 4171–4186, 20","arxiv_match":false},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"proper scoring rules. volume 59, pp. 77–93, Mar 2007. doi: 10.1007/ s10463-006-0099-8. URL https://doi.org/10.1007/s10463-006-0099-8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Jill Burstein, Christy Doran, and Thamar Solorio (eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo","arxiv_match":false}],"id":"1810.04805","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":83},{"title":"On calibration of modern neural networks","arxiv_id":"1706.04599","year":2017,"url":"https://arxiv.org/abs/1706.04599","citing_submissions":782,"explicit_arxiv_matches":32,"examples":[{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"437. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, and Others. The llama 3 herd of models. 2024. URL https://arxiv.org/abs/2407.21783. Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. In Doina Precup and Yee Whye Teh (eds.), Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 1321–1330. PMLR, 06–11 Aug 2","arxiv_match":false},{"id":"r1zsoUu97b","title":"Advancing the Calibration-Compute Frontier of LLM Classifiers via Asymmetric Duos","forum_url":"https://openreview.net/forum?id=r1zsoUu97b","excerpt":"Abhinav Jauhri, Aman Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, et al. The llama 3 herd of models. ArXiv, abs/2407.21783, 2024. Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. ArXiv, abs/1706.04599, 2017. Guande He, Jianfei Chen, and Jun Zhu. Preserving pre-trained features helps calibrate fine-tuned language models. In The Eleventh International Conference on Learning Re","arxiv_match":false},{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"lization. In Proceedings of the International Conference on Learning Representations, 2021. URL https:// openreview.net/forum?id=lQdXeXDoWtI. Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In Proceedings of the International Conference on Machine Learning, pp. 1321–1330, 2017. URL https://proceedings.mlr.press/v70/guo17a.html. Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, and Ga","arxiv_match":false}],"id":"1706.04599","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":84},{"title":"Pointer sentinel mixture models","arxiv_id":"1609.07843","year":2016,"url":"https://arxiv.org/abs/1609.07843","citing_submissions":776,"explicit_arxiv_matches":313,"examples":[{"id":"wslxAq80Kb","title":"Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models","forum_url":"https://openreview.net/forum?id=wslxAq80Kb","excerpt":"o erase in pre-trained language models. In Australasian Conference on Information Security and Privacy, pp. 265–284. Springer, 2026. Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. In ICLR, 2017. Vangelis Metsis, Ion Androutsopoulos, and Georgios Paliouras. Spam filtering with Naive Bayes— which Naive Bayes? In Proceedings of the Third Conference on Email and Anti-Spam, 2006. ","arxiv_match":false},{"id":"RqXP83lQJI","title":"Local Splits, Global Shifts: An Interaction Decomposition of Retokenization in Language Models","forum_url":"https://openreview.net/forum?id=RqXP83lQJI","excerpt":"rontiers in open language model post-training. arXiv preprint arXiv:2411.15124, 2024. Clara Meister. Tokeval: A tokenizer evaluation suite. arXiv preprint arXiv:2608.18062, 2026. Ruixue Liu and Art B Owen. Estimating mean dimensionality of analysis of variance decompositions. Journal of the American Statistical Association, 101(474):712–721, 2006. Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843, 2016. Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Shane Arora, Akshita Bhagia,","arxiv_match":true},{"id":"PCAODQ61Qw","title":"BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation","forum_url":"https://openreview.net/forum?id=PCAODQ61Qw","excerpt":"esis for llm kv cache compression at test time. Advances in Neural Information Processing Systems, 36:52342–52364, 2023. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. Kivi: A tuning-free asymmetric 2bit quantization for kv cache. arXiv preprint arXiv:2402.02750, 2024c. Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models, 2016. URL https://arxiv.org/abs/1609.07843. Leon Mirsky. Symmetric gauge functions and unitarily invariant norms. The quarterly journal of mat","arxiv_match":true}],"id":"1609.07843","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":85},{"title":"ExpeL: LLM agents are experiential learners","arxiv_id":"2308.10144","year":2023,"url":"https://arxiv.org/abs/2308.10144","citing_submissions":773,"explicit_arxiv_matches":129,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"proving language models. In International Conference on Learning Representations, volume 2026, pp. 86069–86100, 2026. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large languag","arxiv_match":false},{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"3 embedding: Advancing text embedding and reranking through foundation models. arXiv preprint arXiv:2506.05176, 2025. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Ziwei Zheng, Minghao Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, and Chao Shen. Deepeyes: ","arxiv_match":false},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"tic workflow generation. In Proceedings of The Thirteenth International Conference on Learning Representations, 2025. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou","arxiv_match":false}],"id":"2308.10144","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":86},{"title":"Exploring the limits of transfer learning with a unified text-to-text transformer","arxiv_id":"1910.10683","year":2019,"url":"https://arxiv.org/abs/1910.10683","citing_submissions":770,"explicit_arxiv_matches":48,"examples":[{"id":"R80bVPjG4H","title":"TDE: Target-conditioned Decision Evidence for Generative Recommendation","forum_url":"https://openreview.net/forum?id=R80bVPjG4H","excerpt":"international conference on web search and data mining, pp. 813–823, 2022. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020. Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samo","arxiv_match":false},{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"structured sparse gradients. CoRR, abs/2406.17660, 2024. URL https://doi.org/10.48550/arXiv.2406.17660. C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1–67, 2020. URL http://jmlr.org. S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He. Zero: Memory optimizations toward training trillion parameter models. ","arxiv_match":false},{"id":"41Axobxx7a","title":"Scaling Muon for Diffusion Transformers","forum_url":"https://openreview.net/forum?id=41Axobxx7a","excerpt":"n International conference on machine learning, pp. 8748–8763. PmLR, 2021. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020. Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly. Assessing generative models via precision and recall. Ad","arxiv_match":false}],"id":"1910.10683","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":87},{"title":"τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains","arxiv_id":"2406.12045","year":2024,"url":"https://arxiv.org/abs/2406.12045","citing_submissions":768,"explicit_arxiv_matches":455,"examples":[{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":"rXiv:2603.30016, 2026. Xiaomi MiMo Team. MiMo-V2.6 model overview. https://mimo.mi.com/docs/en-US/ quick-start/summary/model, 2026. Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik R Narasimhan. τ -bench: A benchmark for tool-agent-user interaction in real-world domains. In The Thirteenth International Conference on Learning Representations, 2025. Z.ai. GLM-5.3. https://docs.z.ai/guides/llm/glm-5.3, 2026. Zhexin Zhang, Shiyao Cui, Yida Lu, Jingzhuo Zhou, Junxiao Yan","arxiv_match":false},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"ions, 2023. Yuanqing Yu, Zhefan Wang, Weizhi Ma, Shuai Wang, Chuhan Wu, Zhiqiang Guo, and Min Zhang. Steptool: Enhancing multi-step tool usage in llms via step-grained reinforcement learning. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management, pp. 3952–3962, 2025. Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan. τ -bench: A benchmark for tool-agent-user interaction in real-world domains. arXiv preprint arXiv:2406.12045, 2024. Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, and Jie Tang. Agenttunin","arxiv_match":true},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":"ttps://proceedings.neurips.cc/paper_files/paper/2025/file/ 0d744742f6fac4d1134c019b7cef3c8a-Paper-Datasets_and_Benchmarks_ Track.pdf. Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan. τ -bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains. In Y. Yue, A. Garg, N. Peng, F. Sha, and R. Yu (eds.), International Conference on Learning Representations, volume 2025, pp. 9965– 10017, 2025. URL https://proceedings.iclr.cc/paper_files/paper/202","arxiv_match":false}],"id":"2406.12045","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":88},{"title":"Gemma 4 Technical Report","arxiv_id":"2607.02770","year":2026,"url":"https://arxiv.org/abs/2607.02770","citing_submissions":757,"explicit_arxiv_matches":698,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"eady ai agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36:28091–28114, 2023. Gemma Team. Gemma 4 technical report, 2026. URL https://arxiv.org/abs/2607. 02770. Hang Gao and Yongfeng Zhang. Memory sharing for large language model based agents. arXiv e-prints,","arxiv_match":true},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"2202/ 079017-3424. URL https://proceedings.neurips.cc/paper_files/paper/ 2024/file/c3177be226ee12e34d6ba3b5e6fe6a5b-Paper-Conference.pdf. Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller. Covariate shift adaptation by importance weighted cross validation. volume 8, pp. 985–1005. JMLR.org, December 2007. Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, and Others. Gemma 4 technical report. 2026. URL https://arxiv.org/abs/2607.02770. Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. volum","arxiv_match":true},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"y-augmented generation. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 98706–98729, 2026. URL https://proceedings.iclr.cc/paper_files/paper/2026/file/ a05b72653ec5b473732129829ae04195-Paper-Conference.pdf. Gemma Team. Gemma 3 technical report, 2025. URL https://arxiv.org/abs/2503. 19786. Gemma Team. Gemma 4 technical report, 2026. URL https://arxiv.org/abs/2607. 02770. GLM-5 Team. Glm-5: from vibe coding to agentic engineering, 2026. URL https://arxiv. org/abs/2602.","arxiv_match":true}],"id":"2607.02770","metadata_source":"Cached arXiv API metadata","rank":89},{"title":"Hybridflow: A flexible and efficient RLHF framework","arxiv_id":"2409.19256","year":2024,"url":"https://arxiv.org/abs/2409.19256","citing_submissions":757,"explicit_arxiv_matches":304,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"thematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. Hybridflow: A flexible and efficient rlhf framework. In Proceedings of the Twentieth European Conference on Computer Systems, EuroSys ’25, pp. 1279–1297. ACM, March 2025. doi: 10.1145/3689031.3696075. URL http://dx.doi.org/10.1145/3689031. 3696075. Yu","arxiv_match":false},{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":" preprint arXiv:2505.16135, 2025. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. Hybridflow: A flexible and efficient rlhf framework. arXiv preprint arXiv: 2409.19256, 2024. Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Cat","arxiv_match":true},{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"lizable r1-style large vision-language model. arXiv preprint arXiv:2504.07615, 2025. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. Hybridflow: A flexible and efficient rlhf framework. In Proceedings of the Twentieth European Conference on Computer Systems, pp. 1279–1297, 2025. Yaorui Shi, Yuxin Chen, Zhengxi Lu, Yuchun Miao, Shugui Liu, Qi Gu, Xunliang Cai, Xiang Wang, and An Zha","arxiv_match":false}],"id":"2409.19256","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":90},{"title":"Efficient streaming language models with attention sinks","arxiv_id":"2309.17453","year":2023,"url":"https://arxiv.org/abs/2309.17453","citing_submissions":750,"explicit_arxiv_matches":188,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"d Bin Cui. Training-free and adaptive sparse attention for efficient long video generation. arXiv preprint arXiv:2502.21079, 2025. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks, 2024. URL https://arxiv. org/abs/2309.17453, 1, 2024. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences","arxiv_match":false},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"ational Linguistics. ISBN 979-8-89176-251-0. doi: 10.18653/v1/2025.acl-long.529. URL https://aclanthology. org/2025.acl-long.529/. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks. In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 21875– 21895, 2024. URL https://proceedings.iclr.c","arxiv_match":false},{"id":"okGS6velwf","title":"Focus-KV: Head-Wise KV Cache Compression Focus On Reasoning Heads","forum_url":"https://openreview.net/forum?id=okGS6velwf","excerpt":" kv cache quantization, 2025. URL https://arxiv.org/abs/2503.24358. Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Junxian Guo, Shang Yang, Haotian Tang, Yao Fu, and Song Han. Duoattention: Efficient long-context llm inference with retrieval and streaming heads, 2024a. URL https://arxiv.org/abs/2410.10819. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks, 2024b. URL https://arxiv.org/abs/2309.17453. Xianglong Yan, Zhiteng Li, Tianao Zhang, Haotong Qin, Linghe Kong, Yulun Zhang, and Xiaokang Yang.","arxiv_match":true}],"id":"2309.17453","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":91},{"title":"A-MEM: Agentic Memory for LLM Agents","arxiv_id":"2502.12110","year":2025,"url":"https://arxiv.org/abs/2502.12110","citing_submissions":741,"explicit_arxiv_matches":239,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum? id=pZiyCaVuti. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-Mem: Agentic memory for LLM agents. In Advances in Neural Information Processing Systems, volume 38, pp. 17577–17604, 2025. B. Y. Yan, Chaofan Li, Hongjin Qian, Shuqi Lu, and Zheng Liu. General agentic memory via deep research. arXiv ","arxiv_match":false},{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":" teams or scaling time? memory enabled lifelong learning in llm multi-agent systems. arXiv preprint arXiv:2604.03295, 2026. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-mem: Agentic memory for llm agents. Advances in Neural Information Processing Systems, 38:17577–17604, 2026. Jingbo Yang, Guanyu Yao, Yang Zhang, Ramana Rao Kompella, Gaowen Liu, and Shiyu Chang. Federatedskill: Federated learning for","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":" Evoarena: Tracking memory evolution for robust llm agents in dynamic environments. arXiv preprint arXiv:2606.13681, 2026a. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-mem: Agentic memory for llm agents. Advances in Neural Information Processing Systems, 38:17577–17604, 2026b. Yingyi Zhang, Junyi Li, Wenlin Zhang, Pengyue Jia, Xianneng Li, Yichao Wang, Derong Xu, Yi Wen, Huifeng Guo, Yong Liu, and X","arxiv_match":false}],"id":"2502.12110","metadata_source":"Cached arXiv API metadata","rank":92},{"title":"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities","arxiv_id":"2507.06261","year":2025,"url":"https://arxiv.org/abs/2507.06261","citing_submissions":740,"explicit_arxiv_matches":673,"examples":[{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":", Haojie Wei, Xipin Wei, Zhifang Guo, Yichong Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, et al. Qwen2-audio technical report. arXiv preprint arXiv:2407.10759, 2024. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint arXiv:2507.06261, 2025. Han Fang, Pengfei Xiong, Luhui Xu, and Yu Chen. Clip2video: Mastering video-text retrieval v","arxiv_match":true},{"id":"VemSN4uC6Q","title":"Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs","forum_url":"https://openreview.net/forum?id=VemSN4uC6Q","excerpt":"en, Jiachen Li, Zhengzhong Tu, Zhangyang Wang, and Rakesh Ranjan. Vlm-3r: Vision-language models augmented with instruction-aligned 3d reconstruction. ArXiv, abs/2505.20279, 2025. Google Gemini Team. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. ArXiv, abs/2507.06261, 2025. Mary Hegarty. Mechanical reasoning by mental simulation. Trends in cognitive sciences, 2004. Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen","arxiv_match":false},{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"models and aligning for generic visuallinguistic tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 24185–24198, 2024b. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint arXiv:2507.06261, 2025. Jingcheng Deng, Liang Pang, Zihao Wei, Shicheng Xu, Zenghao Duan, Kun Xu, Yang Song, Huawei ","arxiv_match":true}],"id":"2507.06261","metadata_source":"Cached arXiv API metadata","rank":93},{"title":"Language Models Are Unsupervised Multitask Learners","arxiv_id":null,"year":2019,"url":null,"citing_submissions":732,"explicit_arxiv_matches":0,"examples":[{"id":"ROquSvIhjs","title":"Inverse Garment Design with Visual Agents","forum_url":"https://openreview.net/forum?id=ROquSvIhjs","excerpt":"am. Qwen3.6-27B: Flagship-level coding in a 27B dense model, April 2026. URL https: //qwen.ai/blog?id=qwen3.6-27b. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI Technical Report, 2019. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, a","arxiv_match":false},{"id":"xLkfjG1wVy","title":"Efficient Long-Horizon Learning for Learned Optimization","forum_url":"https://openreview.net/forum?id=xLkfjG1wVy","excerpt":"n autonomous land vehicle in a neural network. Advances in neural information processing systems, 1, 1988. [29] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. [30] Nived Rajaraman, Lin Yang, Jiantao Jiao, and Kannan Ramchandran. Toward the fundamental limits of imitation learning. Advances in Neural Information Processing System","arxiv_match":false},{"id":"W1xeiKanFu","title":"Do Correct Answers Have Stable Latent Knowledge Support?","forum_url":"https://openreview.net/forum?id=W1xeiKanFu","excerpt":" Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https://arxiv.org/abs/2412.15115. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. Technical report, OpenAI, 2019. URL https: //cdn.openai.com/better-language-models/language_models_are_unsu pervised_multitask_learners.pdf. Marco Scialanga, Thibault Laugel, Vincent Grari, and Marc","arxiv_match":false}],"id":"9cecc18eaeb376f6","metadata_source":"Curated bibliography catalog / publisher metadata","rank":94},{"title":"Mistral 7B","arxiv_id":"2310.06825","year":2023,"url":"https://arxiv.org/abs/2310.06825","citing_submissions":732,"explicit_arxiv_matches":561,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"eal context size of your long-context language models? arXiv preprint arXiv:2404.06654, 2024. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. Mistral 7b, 2023. URL https: //arxiv.org/abs/2310.06825. Donghyeon Joo, Helya Hosseini, Ramyad Hadidi, and Bahar Asgari. Mustafar: Promoting unstructured s","arxiv_match":true},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"essand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. Mistral 7b, 2023. URL https://arxiv. org/abs/2310.06825. Alex Kulesza and Ben Taskar. Determinantal point processes for machine learning. Foundations and Trends in Machine Learning, 5(2-3):123–286, 12 2012. ISS","arxiv_match":false},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":" Francis, 2008. doi: 10.1198/106186008X320456. URL https://doi.org/10.1198/ 106186008X320456. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. Mistral 7b. 2023. URL https: //arxiv.org/abs/2310.06825. Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kal","arxiv_match":true}],"id":"2310.06825","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":95},{"title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","arxiv_id":"2307.09288","year":2023,"url":"https://arxiv.org/abs/2307.09288","citing_submissions":730,"explicit_arxiv_matches":669,"examples":[{"id":"nSsaEp75xm","title":"EmbTracker: Traceable Black-box Watermarking for Federated Language Models","forum_url":"https://openreview.net/forum?id=nSsaEp75xm","excerpt":"ekgul, Yuxi Xia, Samuel Marchal, and N. Asokan. WAFFLE: watermarking in federated learning. In 40th International Symposium on Reliable Distributed Systems, SRDS 2021, Chicago, IL, USA, September 20-23, 2021, pp. 310–320. IEEE, 2021. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh. Embedding watermarks int","arxiv_match":true},{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"iv preprint arXiv:2105.01869, 2021. Mingjie Sun, Zhuang Liu, Anna Bair, and J. Zico Kolter. A simple and effective pruning approach for large language models. In The Twelfth International Conference on Learning Representations, 2024. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łuka","arxiv_match":true},{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"vril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom. Llama 2: Open foundation and fine-tuned chat models, 2023. URL https: //arxiv.org/abs/2307.09288. T. Vogels, S. P. Karimireddy, and M. Jaggi. Powersgd: Practical low-rank gradient compression for ","arxiv_match":true}],"id":"2307.09288","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":96},{"title":"VBench: Comprehensive benchmark suite for video generative models","arxiv_id":"2311.17982","year":2023,"url":"https://arxiv.org/abs/2311.17982","citing_submissions":715,"explicit_arxiv_matches":95,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"ances in neural information processing systems, 30, 2017. Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21807–21818, 2024. Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhu","arxiv_match":false},{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"g, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21807–21818, 2024. InSpatio Team, Donghui Shen, Guofeng Zhang, Haomin Liu, Haoyu Ji, Jialin Liu, Jing Guo, N","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":" gap in autoregressive video diffusion. In NeurIPS, 2025. Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In CVPR, 2024. Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyzing and improving the training dynamics of diffusion models. In CVPR, 2024. Diederik P.","arxiv_match":false}],"id":"2311.17982","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":97},{"title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","arxiv_id":"2312.00752","year":2023,"url":"https://arxiv.org/abs/2312.00752","citing_submissions":713,"explicit_arxiv_matches":390,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"erence. PyTorch Blog, October 2023. URL https://pytorch.org/blog/ flash-decoding/. Accessed: 2026-06-25. Chenlong Deng, Zhisong Zhang, Kelong Mao, Shuaiyi Li, Tianqing Fang, Hongming Zhang, Haitao Mi, Dong Yu, and Zhicheng Dou. Unigist: Towards general and hardware-aligned sequence-level long context compression. arXiv preprint arXiv:2509.15763, 2025. Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arxiv. arXiv preprint arXiv:2312.00752, 10, 2023. Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with stru","arxiv_match":true},{"id":"mlS6HwwCm6","title":"SCOPE: Spectral-Conditioned Omic-Pathology Encoder for Interpretable Multimodal Cancer Survival Analysis","forum_url":"https://openreview.net/forum?id=mlS6HwwCm6","excerpt":"on Theory, 36(5):961–1005, 1990. Marc Gillespie, Bijay Jassal, Ralf Stephan, and et al. The reactome pathway knowledgebase 2022. Nucleic Acids Research, 50(D1):D687–D692, 2022. Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. In First Conference on Language Modeling (COLM), 2024. Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. In International Conference on Le","arxiv_match":false},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"oorian, Denis Korzhenkov, and Amirhossein Habibian. Attention Surgery: An Efficient Recipe to Linearize Your Video Diffusion Transformer. arXiv preprint arXiv:2509.24899, 2025. Albert Gu and Tri Dao. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. In Conference on Language Modeling (COLM), 2024. Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, et al. LTX-Video: Realtime Video Latent Diffusion. arXiv preprint arXiv:2501.00103, 2024","arxiv_match":false}],"id":"2312.00752","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":98},{"title":"Blip-2: Bootstrapping language-image pretraining with frozen image encoders and large language models","arxiv_id":"2301.12597","year":2023,"url":"https://arxiv.org/abs/2301.12597","citing_submissions":710,"explicit_arxiv_matches":49,"examples":[{"id":"zoKRDAQpft","title":"AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval","forum_url":"https://openreview.net/forum?id=zoKRDAQpft","excerpt":"ified image classification, object detection, and visual relationship detection at scale. International journal of computer vision (IJCV), 2020. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning (ICML), 2023. Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, Sibo Song, Shuai Bai, Zhibo Yang, Pengjun Xie, An Yang, Dayiheng Liu, et al. Qwen3-","arxiv_match":false},{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":" Zhao, and Yuxin Peng. Dyfo: A training-free dynamic focus visual search for enhancing lmms in fine-grained visual understanding. In CVPR, 2025. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. In EMNLP, 2024. Huabin Liu,","arxiv_match":false},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"ls, 2025. URL https://arxiv.org/abs/2505.23830. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer, 2024. URL https://arxiv.org/abs/2408.03326. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023a. URL https:// arxiv.org/abs/2301.12597. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hal","arxiv_match":true}],"id":"2301.12597","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":99},{"title":"Overcoming catastrophic forgetting in neural networks","arxiv_id":"1612.00796","year":2016,"url":"https://arxiv.org/abs/1612.00796","citing_submissions":708,"explicit_arxiv_matches":42,"examples":[{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"abinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13):3521– 3526, 2017. doi: 10.1073/pnas.1611835114. URL https://www.pnas.org/doi/abs/ 10.1073/pnas.1611835114. Song Lai, Haohan Zhao, Rong Feng, ","arxiv_match":false},{"id":"LJ3tnmkITu","title":"Recurrent Domain Incremental Adaptation of Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=LJ3tnmkITu","excerpt":"7(9):1185–1194, 2000. James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13):3521–3526, 2017. Jennifer R Kwapisz, Gary M Weiss, and Samuel A Moore. Activity recognition using cell phone accelerometers. ACM SigKDD Explo","arxiv_match":false},{"id":"XvHr3TypV1","title":"Exemplar-free Cross-Category Incremental Keypoint Detection: When New Categories Bring Heterogeneous Schemas","forum_url":"https://openreview.net/forum?id=XvHr3TypV1","excerpt":"abinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13):3521–3526, 2017. Yanjie Li, Sen Yang, Peidong Liu, Shoukui Zhang, Yunxiao Wang, Zhicheng Wang, Wankou Yang, and Shu-Tao Xia. SimCC: A simple ","arxiv_match":false}],"id":"1612.00796","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":100},{"title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","arxiv_id":"1701.06538","year":2017,"url":"https://arxiv.org/abs/1701.06538","citing_submissions":697,"explicit_arxiv_matches":264,"examples":[{"id":"uwSMvBYqGq","title":"Auditing Optimization and Probabilistic Reliability in Frozen Vision-Language Representation Intervention","forum_url":"https://openreview.net/forum?id=uwSMvBYqGq","excerpt":"16) Single NVIDIA A100-SXM4-40GB Single NVIDIA A100-SXM4-40GB Single NVIDIA A100 (40 GB) Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In ICLR, 2017. Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Chunyi Li, Wenxiu Sun, Qiong Yan, Guangtao Zhai, and Weisi Lin. Q-bench: A benchmark for generalpurpose f","arxiv_match":false},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":"injia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, and Yuxiong He. DeepSpeed-MoE: Advancing mixture-of-experts inference and training to power next-generation AI scale. In International conference on machine learning, pp. 18332–18346. PMLR, 2022. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017. Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Cat","arxiv_match":true},{"id":"jjf3FKJp3k","title":"MetaBrainPro: A Graph Learning Approach for Enterprise Metadata Lineage Retrieval","forum_url":"https://openreview.net/forum?id=jjf3FKJp3k","excerpt":" arXiv:1907.01864, 2019. Sebastian Ruder, Bailey Cronin, and David Wingate. Learning many-to-many learning strategies. arXiv preprint arXiv:1907.01864, 2019. Noam Shazeer. Fast transformer decoding: One write-head is all you need. arXiv:1911.03854, 2019. arXiv preprint Noam Shazeer, Ann Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoff Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017. Yikang Shen et al. Heterogeneous graph neural networks for knowledge representation learning","arxiv_match":true}],"id":"1701.06538","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":101},{"title":"InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency","arxiv_id":"2508.18265","year":2025,"url":"https://arxiv.org/abs/2508.18265","citing_submissions":692,"explicit_arxiv_matches":653,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"04.07430. Agustin Tristan Lopez. The item discrimination index: Does it work? Rasch Measurement Transactions, 12(1):626, 1998. URL https://www.rasch.org/rmt/rmt121r.htm. Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, et al. InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency. arXiv preprint arXiv:2508.18265, 2025. URL https://arxiv.org/abs/2508.18265. V Team, Wenyi Hong, Wenmeng Yu, et al. GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimoda","arxiv_match":true},{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"ong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. InternVL Team. InternVL3.5: Advancing open-source multimodal models in versatility, reasoning, and efficiency, 2025. URL https://internvl.github.io/blog/ 2025-08-26-InternVL-3.5/. Amita Kamath, Jack Hessel, and Kai-Wei Chang. What’s “up” with vision-language models? investigating their struggle with spatial ","arxiv_match":false},{"id":"F0hAiHbpm3","title":"FigAct: Turning Scientific Figures into Active Canvases for Explanation","forum_url":"https://openreview.net/forum?id=F0hAiHbpm3","excerpt":"e, Afshin Dehghan, Meng Cao, and Ping Huang. Streambridge: Turning your offline video large language model into a proactive streaming assistant. Advances in Neural Information Processing Systems, 38:132332– 132359, 2026. Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, et al. Internvl3.5: Advancing open-source multimodal models in versatility, reasoning, and efficiency. arXiv preprint arXiv:2508.18265, 2025. Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang, Manling Li, Jiajun Wu, and Heng Ji.","arxiv_match":true}],"id":"2508.18265","metadata_source":"Cached arXiv API metadata","rank":102},{"title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","arxiv_id":null,"year":2023,"url":null,"citing_submissions":689,"explicit_arxiv_matches":0,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"image quality transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5148–5157, 2021. Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4), 2023. Kling AI. Kling Video 3.0. https://kling.ai/dev/model-release/video-3-omni, 2026. Lingdong Kong, Yu Yang, Jianbiao Mei, Youquan Liu, Ao Liang, Dekai Zhu, D","arxiv_match":false},{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":"eady 3d scene reconstruction from rgb-d scans. Advances in Neural Information Processing Systems, 38:162794–162827, 2026. Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis, et al. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1, 2023. Mukul Khanna, Yongsen Mao, Hanxiao Jiang, Sanjay Haresh, Brennan Shacklett, Dhruv Batra, Alexander Clegg, Eric Undersander, Angel X. Chang, and Manolis Savva. H","arxiv_match":false},{"id":"dmWiArTSoB","title":"Validation over Contexts: Robust Long-Context Inference for 3D Reconstruction Models","forum_url":"https://openreview.net/forum?id=dmWiArTSoB","excerpt":"anan, Sebastian Scherer, and Peter Kontschieder. MapAnything: Universal feed-forward metric 3D reconstruction. In 3DV, 2026. Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM TOG, 42(4):139–1, 2023. Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Marc Pollefeys, and Torsten Sattler. Wildgaussians: 3d gaussian splatting in the wild. NeurIPS, 2024. Vincent Leroy, Yohann","arxiv_match":false}],"id":"b9bf5c472fe33198","metadata_source":"Curated bibliography catalog / publisher metadata","rank":103},{"title":"ALFWorld: Aligning text and embodied environments for interactive learning","arxiv_id":"2010.03768","year":2020,"url":"https://arxiv.org/abs/2010.03768","citing_submissions":689,"explicit_arxiv_matches":338,"examples":[{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":"einforcement learning. In Advances in Neural Information Processing Systems, volume 36, 2023. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. ALFWorld: Aligning text and embodied environments for interactive learning. In International Conference on Learning Representations, 2021. Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang. AdaPlanner: Adaptive planning from feedback with language models. In ","arxiv_match":false},{"id":"1UUiXNNCyo","title":"Sparse Where, Dense Within: Contrastive Privileged Action Distillation for Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=1UUiXNNCyo","excerpt":"g Yu. From generic correlation to input-specific credit in on-policy self distillation, 2026. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. ALFWorld: Aligning text and embodied environments for interactive learning, 2021. Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Jie Wu, Zhengzhou Cai, Yueqing Sun, Ziang Ye, Linji Hao, Qi Gu, Xunliang Cai, Yongliang Shen, and Yujiu Yang. AgentOPSD: Recursive self-distil","arxiv_match":false},{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"on, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 10737–10746. IEEE, 2020a. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768, 2020b. Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, D","arxiv_match":true}],"id":"2010.03768","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":104},{"title":"PyTorch: An imperative style, high-performance deep learning library","arxiv_id":"1912.01703","year":2019,"url":"https://arxiv.org/abs/1912.01703","citing_submissions":687,"explicit_arxiv_matches":63,"examples":[{"id":"IlRPpE9Npn","title":"LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking","forum_url":"https://openreview.net/forum?id=IlRPpE9Npn","excerpt":" European Conference on Computer Vision, 2018. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, highperformance deep learning library. Advances in neural information processing systems, 32, 2019. Liang Peng, Junyuan Gao, Xinran Liu, Weihong Li, Shaohua Dong, Zhipeng Zhang, Heng Fan, and Libo Zhang. VastTrack: Vast category visual o","arxiv_match":false},{"id":"QiYD616GNr","title":"Fast-ZSAD: Patch-wise Adaptive Inference for Efficient Zero-Shot Anomaly Detection","forum_url":"https://openreview.net/forum?id=QiYD616GNr","excerpt":"/3439950. URL https://doi.org/10.1145/3439950. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library, 2019. URL https://arxiv.org/abs/1912.01703. Zhen Qu, Xian Tao, Xinyi Gong, ShiChen Qu, Qiyu Chen, Zhengtao Zhang, Xingang Wang, and Guiguang D","arxiv_match":true},{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":"lia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier ","arxiv_match":false}],"id":"1912.01703","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":105},{"title":"MMLU-Pro: A more robust and challenging multi-task language understanding benchmark","arxiv_id":"2406.01574","year":2024,"url":"https://arxiv.org/abs/2406.01574","citing_submissions":683,"explicit_arxiv_matches":166,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, and Wenhu Chen. MMLU-Pro: A more robust and challenging multi-task language understanding benchmark. In Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track, pp. 95266–95290, 2024. doi: 10.52202/079017-3018. URL https://proceedings.neurips.cc/paper_files","arxiv_match":false},{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"th International Conference on Learning Representations, 2023. Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, et al. Mmlu-pro: A more robust and challenging multitask language understanding benchmark. Advances in Neural Information Processing Systems, 37:95266–95290, 2024b. Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. Unleashing the emergent cognitive synergy in lar","arxiv_match":false},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"89031.3696075. URL http://dx.doi.org/10.1145/3689031. 3696075. Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, et al. Mmlu-pro: A more robust and challenging multitask language understanding benchmark. Advances in Neural Information Processing Systems, 37: 95266–95290, 2024. Ming Wen, Yuxuan Liu, Kun Yang, Yunhao Feng, Zhuoer Xu, Yuhao Sun, Shiwen Cui, Xiang Zheng, Yi Liu, Xingjun Ma, et al. Const","arxiv_match":false}],"id":"2406.01574","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":106},{"title":"WinoGrande: An adversarial winograd schema challenge at scale","arxiv_id":"1907.10641","year":2019,"url":"https://arxiv.org/abs/1907.10641","citing_submissions":683,"explicit_arxiv_matches":109,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"of Proceedings of Machine Learning Research, pp. 42558–42583. PMLR, 2024. URL https://proceedings.mlr.press/v235/ribar 24a.html. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. WinoGrande: An adversarial Winograd schema challenge at scale. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 8732–8740, 2020. Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, and Tri Dao. FlashAttention-3","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":": A conversational question answering challenge. Transactions of the Association for Computational Linguistics, 7:249–266, 2019. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106, 2021. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. Social iqa: Commonsense reasoning about social interactions. In Proceedings of ","arxiv_match":false},{"id":"hAJ2b6a8w1","title":"CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning","forum_url":"https://openreview.net/forum?id=hAJ2b6a8w1","excerpt":"In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 8722–8731, 2020. doi: 10.1609/aaai.v34i05.6398. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. WinoGrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106, 2021. Fabrizio Sandri, Elia Cunegatti, and Giovanni Iacca. 2SSP: A two-stage framework for structured pruning of LLMs. Transactions on Machine Learning Resea","arxiv_match":false}],"id":"1907.10641","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":107},{"title":"PIQA: Reasoning about physical commonsense in natural language","arxiv_id":"1911.11641","year":2019,"url":"https://arxiv.org/abs/1911.11641","citing_submissions":682,"explicit_arxiv_matches":97,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"ation. arXiv preprint arXiv:1308.3432, 2013. Dimitri P. Bertsekas. Nonlinear Programming. Athena Scientific, 2 edition, 1999. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. PIQA: Reasoning about physical commonsense in natural language. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 7432–7439, 2020. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004. Aydar B","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"Crandall, Linda Smith, and Chen Yu. Toddler-inspired visual object learning. Advances in neural information processing systems, 31, 2018. Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about physical commonsense in natural language. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp. 7432–7439, 2020. Garrick Brazil, Abhinav Kumar, Julian Straub, Nikhila Ravi, Justin Johnson, and Georgia Gkioxari. Om","arxiv_match":false},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":" Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. Pythia: A suite for analyzing large language models across training and scaling, 2023. URL https://arxiv.org/abs/2304.01373. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. Piqa: Reasoning about physical commonsense in natural language, 2019. URL https://arxiv.org/abs/1911. 11641. Alan Chen, Jack Merullo, Alessandro Stolfo, and Ellie Pavlick. Transferring linear features across","arxiv_match":true}],"id":"1911.11641","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":108},{"title":"OpenAI GPT-5 System Card","arxiv_id":"2601.03267","year":2025,"url":"https://arxiv.org/abs/2601.03267","citing_submissions":679,"explicit_arxiv_matches":488,"examples":[{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":" Neural Information Processing Systems, 37:75942–75985, 2024. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in neural information processing systems, 36:8634–8652, 2023. Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart, Adi Ganesh, Ahmed El-Kishky, Aidan McLaughlin, Aiden Low, AJ Ostrow, Akhila Ananthram, et al. Openai gpt-5 system card. arXiv preprint arXiv:2601.03267, 2026. Changzhi Sun, Xiangyu Chen, Jixiang Luo, Dell Zhang, and Xuelong Li. Beyond heuristics: A de","arxiv_match":true},{"id":"qOSFb0s2z4","title":"Do 3D Large Language Models Really Use 3D Evidence?","forum_url":"https://openreview.net/forum?id=qOSFb0s2z4","excerpt":"n Ma, Silong Yong, Zilong Zheng, Qing Li, Yitao Liang, Song-Chun Zhu, and Siyuan Huang. SQA3D: Situated question answering in 3D scenes. In International Conference on Learning Representations, 2023. OpenAI. GPT-5 system card. System card, August 2025. URL https://openai.com/index/ gpt-5-system-card/. Zhangyang Qi, Zhixiong Zhang, Ye Fang, Jiaqi Wang, and Hengshuang Zhao. GPT4Scene: Understand 3D scenes from videos with v","arxiv_match":false},{"id":"JmKCJSrhxX","title":"FlowCompile: An Optimizing Compiler for Structured LLM Workflows","forum_url":"https://openreview.net/forum?id=JmKCJSrhxX","excerpt":"ng compiler techniques for neural networks. arXiv preprint arXiv:1805.00907, 2018. QwenTeam. Qwq-32b: Embracing the power of reinforcement learning, March 2025. URL https: //qwenlm.github.io/blog/qwq-32b/. Amit Sabne. Xla : Compiling machine learning for peak performance, 2020. Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart, Adi Ganesh, Ahmed El-Kishky, Aidan McLaughlin, Aiden Low, AJ Ostrow, Akhila Ananthram, et al. Openai gpt-5 system card. arXiv preprint arXiv:2601.03267, 2026. Jinwei Su, Qizhen Lan, Yinghui Xia, Lifan Sun, Weiyou Tian, Tianyu Shi, Xinyuan Song, and Le","arxiv_match":true}],"id":"2601.03267","metadata_source":"Cached arXiv API metadata","rank":109},{"title":"Auto-Encoding Variational Bayes","arxiv_id":"1312.6114","year":2013,"url":"https://arxiv.org/abs/1312.6114","citing_submissions":678,"explicit_arxiv_matches":395,"examples":[{"id":"NzzGCMwHGU","title":"Hierarchical Symbol Coupling for Generative Modeling and Symbolic Inference","forum_url":"https://openreview.net/forum?id=NzzGCMwHGU","excerpt":"ng flows. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pp. 4615–4630, 2020. 5, 6 Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In International Conference on Learning Representations, 2014. 1, 8 Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. Semi-supervised learning with deep generative models.","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":" et al. Vbench: Comprehensive benchmark suite for video generative models. In CVPR, 2024. Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyzing and improving the training dynamics of diffusion models. In CVPR, 2024. Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014. URL https://arxiv.org/abs/1312.6114. Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu","arxiv_match":true},{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"age-action model. In CoRL, 2024. Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success. In RSS, 2025. Diederik P. Kingma and Max Welling. Auto-encoding variational Bayes. In ICLR, 2014. Albina Klepach, Alexander Nikulin, Ilya Zisman, Denis Tarasov, Alexander Derevyagin, Andrei Polubarov, Nikita Lyubaykin, Igor Kiselev, and Vladislav Kurenkov. Object-centric latent ac","arxiv_match":false}],"id":"1312.6114","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":110},{"title":"Minillm: On-policy distillation of large language models","arxiv_id":"2306.08543","year":2023,"url":"https://arxiv.org/abs/2306.08543","citing_submissions":678,"explicit_arxiv_matches":121,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"relli, Simone Scardapane, Fabrizio Silvestri, and Emanuele Rodolà. Task singular vectors: Reducing task interference in model merging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. URL https://arxiv.org/abs/2412.00081v3. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. MiniLLM: Knowledge distillation of large language models. In International Conference on Learning Representations, 2024. URL https: //arxiv.org/abs/2306.08543v4. Hugging Face H4. AIME 2024 dataset. Hugging Face dataset, 2025. URL https://huggin gface.co/datase","arxiv_match":true},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"de-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. Minillm: Knowledge distillation of large language models. In International Conference on Learning Representations, volume 2024, pp. 32694–32717, 2024. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong","arxiv_match":false},{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"l of Computer Vision, 129(6):1789–1819, March 2021. ISSN 1573-1405. doi: 10.1007/s11263-021-01453-z. URL http://dx.doi.org/10.1007/ s11263-021-01453-z. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. MiniLLM: Knowledge distillation of large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=5h0qf7IBZZ. Zihao Han, Tiangang Zhang, Huaibin Wang, and Yilun Sun. Adaptive teacher ex","arxiv_match":false}],"id":"2306.08543","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":111},{"title":"OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments","arxiv_id":"2404.07972","year":2024,"url":"https://arxiv.org/abs/2404.07972","citing_submissions":669,"explicit_arxiv_matches":139,"examples":[{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":"rXiv:2312.05269, 2023. Ziyang Wang et al. Egomemreason: A memory-driven reasoning benchmark for long-horizon egocentric video understanding. arXiv preprint arXiv:2605.09874, 2026b. Tianbao Xie et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37:52040–52094, 2024. Wujiang Xu et al. A-mem: Agentic memory for llm agents. Advances in Neural Information Processing Systems, 38:17577–17604, 20","arxiv_match":false},{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"mobile gui agents. arXiv preprint arXiv:2602.05832, 2026. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh J Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37:52040–52094, 2024. Haiyang Xu, Xi Zhang, Haowei Liu, Junyang Wang, Zhaozai Zhu, Shengjie Zhou, Xuhao Hu, Feiyu Gao, Junjie Cao, Zihua Wang, et a","arxiv_match":false},{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"nteractive memory. arXiv preprint arXiv:2410.10813, 2024. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh J Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, et al. OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37:52040–52094, 2024. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-Mem: Agentic memory for LLM agents. arXiv prepr","arxiv_match":false}],"id":"2404.07972","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":112},{"title":"AutoGen: Enabling next-gen LLM applications via multi-agent conversation","arxiv_id":"2308.08155","year":2023,"url":"https://arxiv.org/abs/2308.08155","citing_submissions":664,"explicit_arxiv_matches":290,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"ons, 2025. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W. White, Doug Burger, and Chi Wang. AutoGen: Enabling next-gen LLM applications via multi-agent conversation. In Proceedings of the First Conference on Language Modeling, 2024. URL https://openreview.net/forum? id=BAakY1hNKS. Zhilin Yang, Peng Qi, Saenz Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhu","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":". Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. Measuring multimodal mathematical reasoning with math-vision dataset. Advances in Neural Information Processing Systems, 37:95095–95169, 2024a. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al. Autogen: Enabling next-gen llm applications via multiagent conversation. arXiv preprint arXiv:2308.08155, 2023. Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, and Graham Neubig. Agent workflow memory. arXiv","arxiv_match":true},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"20985, 2025. URL https://proceedings.neurips.cc/paper_files/paper/2025/ hash/af043aee9cb137785d93195c6cf4cd96-Abstract-Conference.html. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W. White, Doug Burger, and Chi Wang. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv preprint arXiv:2308.08155, 2023. URL https://arxiv.org/abs/2308.08155. Tongzhou Wu, Yuhao Wang, Xinyu Ma, Xiuqiang He, Shuaiqiang Wang, Dawei Yin, and Xiangyu Zhao. Deep","arxiv_match":true}],"id":"2308.08155","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":113},{"title":"Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models","arxiv_id":"2601.18734","year":2026,"url":"https://arxiv.org/abs/2601.18734","citing_submissions":660,"explicit_arxiv_matches":486,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"at scale. Advances in Neural Information Processing Systems, 38:113222–113244, 2026. Lifan Yuan, Wendi Li, Huayu Chen, Ganqu Cui, Ning Ding, Kaiyan Zhang, Bowen Zhou, Zhiyuan Liu, and Hao Peng. Free process rewards without process labels. arXiv preprint arXiv:2412.01981, 2024. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover. Self-distilled reasoner: On-policy self-distillation for large language models. arXiv preprint arXiv:2601.18734, 2026. Chujie Zheng, Kai Dang, Bowen Yu, Mingze Li, Huiqiang Jiang, Junrong Lin, Yuqiong Liu, Hao L","arxiv_match":true},{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"s but rich feedback via reflection-enhanced self-distillation, 2026. URL https://arxiv.org/abs/ 2605.12741. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover. Self-distilled reasoner: On-policy self-distillation for large language models. In Forty-third International Conference on Machine Learning, 2026a. URL https://openreview.net/ forum?id=Jpxfof0EaS. Xuyang Zhao, Liting Zhang, Zichen Xu, Zhihu Wang, Xu Caiyue, Shiwan Zhao, and Qic","arxiv_match":false},{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":" memory for llm-based agents. arXiv preprint arXiv:2603.15634, 2026c. Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, and Kaixiang Lin. Do llms recognize your preferences? evaluating personalized preference following in llms, 2025. URL https: //arxiv.org/abs/2502.09597. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover. Self-distilled reasoner: On-policy self-distillation for large language models. arXiv preprint arXiv:2601.18734, 2026. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large l","arxiv_match":true}],"id":"2601.18734","metadata_source":"Cached arXiv API metadata","rank":114},{"title":"A time series is worth 64 words: Long-term forecasting with transformers","arxiv_id":"2211.14730","year":2022,"url":"https://arxiv.org/abs/2211.14730","citing_submissions":655,"explicit_arxiv_matches":160,"examples":[{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"ural networks with canonical correlation. In Proceedings of the Advances in neural information processing systems, volume 31, 2018. Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with transformers. In Proceedings of the International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id= Jbdc0vTOcol. Vardan Papyan, Xiao Y Han, and David L Donoho. Prevalence of neura","arxiv_match":false},{"id":"LJ3tnmkITu","title":"Recurrent Domain Incremental Adaptation of Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=LJ3tnmkITu","excerpt":" Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023. Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with transformers. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zh","arxiv_match":false},{"id":"Ytw4pWrGGW","title":"PhysioSkeptic: Skeptical Reasoning Grounded in Signal Quality for Multimodal Rhythm Analysis","forum_url":"https://openreview.net/forum?id=Ytw4pWrGGW","excerpt":"rdiorespiratory Analysis. PhysioNet, February 2026. doi: 10.13026/ nmwb-6h34. URL https://doi.org/10.13026/nmwb-6h34. Version 1.0.0. Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with transformers. In International Conference on Learning Representations, 2023. Jiapu Pan and Willis J Tompkins. A real-time qrs detection algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3):230–236, 1","arxiv_match":false}],"id":"2211.14730","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":115},{"title":"A reduction of imitation learning and structured prediction to no-regret online learning","arxiv_id":"1011.0686","year":2010,"url":"https://arxiv.org/abs/1011.0686","citing_submissions":648,"explicit_arxiv_matches":41,"examples":[{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":". GPQA: A graduate-level google-proof q&a benchmark. In First Conference on Language Modeling, 2024. URL https://openreview. net/forum?id=Ti67584b98. Stephane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Geoffrey Gordon, David Dunson, and Miroslav Dudı́k (eds.), Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, volume 15 of Proceedings of Machine Le","arxiv_match":false},{"id":"ylrbHy6YvK","title":"KI-VLA: Kinematics-Informed Action Decoding for Flow-Matching Robot Policies","forum_url":"https://openreview.net/forum?id=ylrbHy6YvK","excerpt":" 1965. S. W. Roberts. Control chart tests based on geometric moving averages. Technometrics, 1(3):239–250, 1959. doi: 10.1080/00401706.1959.10489860. Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 627–635. JMLR Workshop and Conference Proceedings, 2011. Dongwoo Son and Suhan Park. LiPo: A ","arxiv_match":false},{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"Schwarz, Timothy Lillicrap, and Gregory Wayne. Experience replay for continual learning. Advances in neural information processing systems, 32, 2019. Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 627–635. JMLR Workshop and Conference Proceedings, 2011. Manolis Savva, Abhishek Kadian, Olek","arxiv_match":false}],"id":"1011.0686","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":116},{"title":"GLM-5: from Vibe Coding to Agentic Engineering","arxiv_id":"2602.15763","year":2026,"url":"https://arxiv.org/abs/2602.15763","citing_submissions":648,"explicit_arxiv_matches":541,"examples":[{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"Paper-Conference.pdf. Gemma Team. Gemma 3 technical report, 2025. URL https://arxiv.org/abs/2503. 19786. Gemma Team. Gemma 4 technical report, 2026. URL https://arxiv.org/abs/2607. 02770. GLM-5 Team. Glm-5: from vibe coding to agentic engineering, 2026. URL https://arxiv. org/abs/2602.15763. Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspired long-term memory for large language mo","arxiv_match":false},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":" Yipeng Geng, Yong Yan, Yonglin Tan, Yuean Bi, Yuhan Shen, Yuhao Yang, Yujiang Li, Yunan Liu, Yunqing Wang, Yuntao Li, Yurong Wu, Yutao Zhang, Yuxi Duan, Yuxuan Zhang, Zezhen Liu, Zhengtao Jiang, Zhenhe Yan, Zheyu Zhang, Zhixiang Wei, Zhuo Chen, Zhuoer Feng, Zijun Yao, Ziwei Chai, Ziyuan Wang, Zuzhou Zhang, Bin Xu, Minlie Huang, Hongning Wang, Juanzi Li, Yuxiao Dong, and Jie Tang. GLM-5: from Vibe Coding to Agentic Engineering, 2026. URL https://arxiv.org/abs/2602.15763. Google DeepMind. Gemini 3.7 Flash: Model card, August 2026. URL https://deepmind. google/models/mo","arxiv_match":true},{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":" arXiv preprint arXiv:2607.22690, 2026a. Yi Yu, Liuyi Yao, Yuexiang Xie, Qingquan Tan, Jiaqi Feng, Yaliang Li, and Libing Wu. Agentic memory: Learning unified long-term and short-term memory management for large language model agents. arXiv preprint arXiv:2601.01885, 2026b. Aohan Zeng, Xin Lv, Zhenyu Hou, Zhengxiao Du, Qinkai Zheng, Bin Chen, Da Yin, Chendi Ge, Chenghua Huang, Chengxing Xie, et al. Glm-5: from vibe coding to agentic engineering. arXiv preprint arXiv:2602.15763, 2026. Guibin Zhang, Muxin Fu, and Shuicheng Yan. Memgen: Weaving generative latent memory for self","arxiv_match":true}],"id":"2602.15763","metadata_source":"Cached arXiv API metadata","rank":117},{"title":"VGGT: Visual Geometry Grounded Transformer","arxiv_id":"2503.11651","year":2025,"url":"https://arxiv.org/abs/2503.11651","citing_submissions":645,"explicit_arxiv_matches":44,"examples":[{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":"ao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. In European Conference on Computer Vision, pp. 439–457. Springer, 2024. Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer, 2025a. URL https://arxiv.org/ abs/2503.11651. Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A. Efros, and Angjoo Kanazawa. Continuous ","arxiv_match":true},{"id":"bMGDZqIh5O","title":"CoRF: Cross-Scene RF Synthesis by Learning Propagation and Preserving Array Physics","forum_url":"https://openreview.net/forum?id=bMGDZqIh5O","excerpt":"stimation Method for Array Processing. IEEE Transactions on Signal Processing, 59(2):629–638, 2011. Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. VGGT: Visual Geometry Grounded Transformer. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla,","arxiv_match":false},{"id":"dmWiArTSoB","title":"Validation over Contexts: Robust Long-Context Inference for 3D Reconstruction Models","forum_url":"https://openreview.net/forum?id=dmWiArTSoB","excerpt":"3–5293, 2025. Hengyi Wang and Lourdes Agapito. 3d reconstruction with spatial memory. In 3DV, 2025. Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In CVPR, pp. 5294–5306, 2025a. Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A Efros, and Angjoo Kanazawa. Continuous 3d perception model with persistent state. In CVPR, pp. 10510–10522, 2","arxiv_match":false}],"id":"2503.11651","metadata_source":"Cached arXiv API metadata","rank":118},{"title":"Large language models: A survey","arxiv_id":"2402.06196","year":2024,"url":"https://arxiv.org/abs/2402.06196","citing_submissions":644,"explicit_arxiv_matches":32,"examples":[{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"u need. Advances in neural information processing systems, 30, 2017. Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, et al. Efficient large language models: A survey. arXiv preprint arXiv:2312.03863, 2023. Samuel Williams, Andrew Waterman, and David Patterson. Roofline: An insightful visual performance model for multicore architectures. Communications of the ACM,","arxiv_match":false},{"id":"r1zsoUu97b","title":"Advancing the Calibration-Compute Frontier of LLM Classifiers via Asymmetric Duos","forum_url":"https://openreview.net/forum?id=r1zsoUu97b","excerpt":"g, pp. 2381–2391. Association for Computational Linguistics, October-November 2018. Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. Large language models: A survey. ArXiv, abs/2402.06196, 2024. OpenAI. Gpt-4 technical report. ArXiv, abs/2303.08774, 2023. Pouya Pezeshkpour and Estevam Hruschka. Large language models sensitivity to the order of options in multipl","arxiv_match":false},{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"nth International Conference on Learning Representations, 2025a. URL https://openreview.net/forum?id=ScI7IlKGdI. Junhao Zheng, Shengjie Qiu, Chengming Shi, and Qianli Ma. Towards lifelong learning of large language models: A survey. ACM Comput. Surv., 57(8), March 2025b. ISSN 0360-0300. doi: 10.1145/3716629. URL https://doi.org/10.1145/3716629. He Zhu, Junyou Su, Peng Lai, Ren Ma, Wenjia Zhang, Linyi Yang, and Guanhua Chen. Anc","arxiv_match":false}],"id":"2402.06196","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":119},{"title":"AgentBench: Evaluating LLMs as agents","arxiv_id":"2308.03688","year":2023,"url":"https://arxiv.org/abs/2308.03688","citing_submissions":642,"explicit_arxiv_matches":157,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. AgentBench: Evaluating LLMs as agents. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=zAdUB0aCTQ. Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhe","arxiv_match":false},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":" control. In Proceedings of 2023 IEEE International conference on robotics and automation (ICRA), pp. 9493–9500. IEEE, 2023. Grace Liu, Yuxiao Qu, Jeff Schneider, Aarti Singh, and Aviral Kumar. Cart: Teaching llm agents to know when they know enough. arXiv preprint arXiv:2510.08517, 2025. Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al. Agentbench: Evaluating llms as agents. arXiv preprint arXiv:2308.03688, 2023. Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Haoping Bai, Shuang Ma, ","arxiv_match":true},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":"Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. AgentBench: Evaluating LLMs as Agents. In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 52989–53046, 2024. URL https://proceedings.iclr.cc","arxiv_match":false}],"id":"2308.03688","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":120},{"title":"Diffusion models beat gans on image synthesis","arxiv_id":"2105.05233","year":2021,"url":"https://arxiv.org/abs/2105.05233","citing_submissions":635,"explicit_arxiv_matches":47,"examples":[{"id":"gsDsA2ZjkO","title":"PhysDEM: Physics-Defined Energy-Matching Diffusion for Spatiotemporal Field Generation under Scarce Measurements","forum_url":"https://openreview.net/forum?id=gsDsA2ZjkO","excerpt":" O Roberts, Andrew M Stuart, and David White. Mcmc methods for functions: modifying old algorithms to make them faster. Statistical Science, pp. 424–446, 2013. Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780–8794, 2021. Ivan G Graham, Frances Y Kuo, Dirk Nuyens, Robert Scheichl, and Ian H Sloan. Analysis of circulant embedding methods for sampli","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":", Zubin Aysola, and Justin Johnson. RedCaps: Web-curated image-text data created by the people, for the people. In NeurIPS Datasets and Benchmarks, 2021. Prafulla Dhariwal and Alexander Quinn Nichol. Diffusion models beat gans on image synthesis. In NeurIPS, 2021. Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow tra","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":", Zubin Aysola, and Justin Johnson. RedCaps: Web-curated image-text data created by the people, for the people. In NeurIPS Datasets and Benchmarks, 2021. Prafulla Dhariwal and Alexander Quinn Nichol. Diffusion models beat GANs on image synthesis. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan (eds.), NeurIPS, 2021. URL https://openreview.net/forum?id=AAWuCvzaVt. Patrick Esser, Robin Rombach, and Björn Ommer. Taming transfor","arxiv_match":false}],"id":"2105.05233","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":121},{"title":"Scaling Laws for Neural Language Models","arxiv_id":"2001.08361","year":2020,"url":"https://arxiv.org/abs/2001.08361","citing_submissions":635,"explicit_arxiv_matches":608,"examples":[{"id":"mZ3FfjH6Gm","title":"Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=mZ3FfjH6Gm","excerpt":"cient In-Context Learning for Tabular Prediction. arXiv:2608.01400, 2026. Sukriti Jaitly, Tanay Shah, Ashish Shugani, and Razik Singh Grewal. Towards better serialization of tabular data for few-shot classification with large language models. arXiv:2312.12464, 2023. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv:2001.08361, 2020. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and TieYan ","arxiv_match":true},{"id":"PxFICAgn1X","title":"Omnimodal Dataset Distillation via High-Order Proxy Alignment","forum_url":"https://openreview.net/forum?id=PxFICAgn1X","excerpt":"sactions on Pattern Analysis and Machine Intelligence, 47(3):2125–2139, 2024. Siddharth Joshi, Jiayi Ni, and Baharan Mirzasoleiman. Dataset distillation via knowledge distillation: towards efficient self-supervised pre-training of deep networks. In ICLR, 2025. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models, 2020. arXiv preprint arXiv:2001.08361. Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, JungWoo Ha, an","arxiv_match":true},{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"n experts. In International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=t7P5BUKcYv. Alexia Jolicoeur-Martineau. Less is more: Recursive reasoning with tiny networks, 2025. URL https://arxiv.org/abs/2510.04871. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models, 2020. URL https://arxiv.org/abs/2001.08361. Bo-Kyeong Kim, Geonmin Kim, Tae-Ho Kim, Thibault Castells, Shinkook Choi, Junho Shin, and Hyoung-K","arxiv_match":true}],"id":"2001.08361","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":122},{"title":"Emerging properties in self-supervised vision transformers","arxiv_id":"2104.14294","year":2021,"url":"https://arxiv.org/abs/2104.14294","citing_submissions":629,"explicit_arxiv_matches":40,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"ion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(5):1483–1498, 2021. Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9650–9660, October 2021. Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervis","arxiv_match":false},{"id":"QiYD616GNr","title":"Fast-ZSAD: Patch-wise Adaptive Inference for Efficient Zero-Shot Anomaly Detection","forum_url":"https://openreview.net/forum?id=QiYD616GNr","excerpt":"g Cao, Xiaohao Xu, Yuqi Cheng, Chen Sun, Zongwei Du, Liang Gao, and Weiming Shen. Personalizing vision-language models with hybrid prompts for zero-shot anomaly detection. IEEE Transactions on Cybernetics, 55(4):1917–1929, 2025. doi: 10.1109/TCYB.2025.3536165. Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers, 2021. URL https: //arxiv.org/abs/2104.14294. Mengzhao Chen, Wenqi Shao, Peng Xu, Mingbao Lin, Kaipeng Zhang, Fei Chao, Rongrong Ji, Yu Qiao, an","arxiv_match":true},{"id":"AzPpBFMquF","title":"Segment Anything In-Context via Visual Demonstrations","forum_url":"https://openreview.net/forum?id=AzPpBFMquF","excerpt":"ything with concepts. In International Conference on Learning Representations (ICLR), 2026. Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 9650–9660, 2021. Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision trans","arxiv_match":false}],"id":"2104.14294","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":123},{"title":"Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory","arxiv_id":"2504.19413","year":2025,"url":"https://arxiv.org/abs/2504.19413","citing_submissions":629,"explicit_arxiv_matches":418,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":" Ruike Cao, Fanyu Zhao, Fugen Yao, Liang Dong, Jian Xu, Guanjun Jiang, Yifei Zhao, Han Zhang, and Li Xiao. MemCalib: Benchmarking and optimizing memory use in LLM agents. arXiv preprint arXiv:2609.24259, 2026. Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready AI agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi Y","arxiv_match":true},{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"s through self-knowledge distillation, 2024. Yuxin Chen, Yi Zhang, Zhengzhou Cai, Yaorui Shi, Zhiyuan Yao, Chenhang Cui, Jingnan Zheng, Yaqi Huo, Xi Su, Qi Gu, et al. Vitabench 2.0: Evaluating personalized and proactive agents in long-term user interactions. arXiv preprint arXiv:2605.27141, 2026. Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026.","arxiv_match":true},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"a interactive environmental learning. Proceedings of Advances in Neural Information Processing Systems, 37:589–631, 2024. Wenjie Chen, Wenbin Li, Di Yao, Xuying Meng, Chang Gong, and Jingping Bi. Gtool: Graph enhanced tool planning with large language model. arXiv preprint arXiv:2508.12725, 2025. Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. Guanting Dong, Yifei Chen, Xiaoxi Li, Jiajie Jin, Hongjin Qian, Yutao Zhu, Hangyu Mao, Guoru","arxiv_match":true}],"id":"2504.19413","metadata_source":"Cached arXiv API metadata","rank":124},{"title":"DeepSeek-V3 Technical Report","arxiv_id":"2412.19437","year":2024,"url":"https://arxiv.org/abs/2412.19437","citing_submissions":625,"explicit_arxiv_matches":567,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"iang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Z. F. Wu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhipeng Xu, Zhiyu Wu, Zhongyu Zhang, Zhuoshu Li, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Ziyi Gao, and Zizheng Pan. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437, 2025. URL https://arxiv.org/abs/2412.19437. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hie","arxiv_match":true},{"id":"qOSFb0s2z4","title":"Do 3D Large Language Models Really Use 3D Evidence?","forum_url":"https://openreview.net/forum?id=qOSFb0s2z4","excerpt":"mputer Vision and Pattern Recognition, 2017. Thomas De Min, Subhankar Roy, Stéphane Lathuilière, Elisa Ricci, and Massimiliano Mancini. ProactiveBench: Benchmarking proactiveness in multimodal large language models. arXiv preprint arXiv:2603.19466, 2026. Jiayu Ding, Haoran Tang, Hongbo Jin, Wei Gao, and Ge Li. 3D instruction ambiguity detection. arXiv preprint arXiv:2601.05991, 2026. DeepSeek-AI. DeepSeek-V3 technical report, 2024. URL https://arxiv.org/abs/2412. 19437. Google DeepMind. Gemini 3.1 Pro model card. Model card, February 2026. URL https: //deepmind.googl","arxiv_match":true},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":"er Choy, Vinay Ramasesh, Albert Webson, and Reiner Pope. How to scale your model. Online, 2025. URL https://jax-ml.github.io/scaling-book/. Damai Dai, Chengqi Deng, Chenggang Zhao, R. X. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, et al. DeepSeekMoE: Towards ultimate expert specialization in mixture-ofexperts language models. arXiv preprint arXiv:2401.06066, 2024. DeepSeek-AI. DeepSeek-V3 technical report, 2025b. URL https://arxiv.org/abs/2412. 19437v2. DeepSeek-AI. DeepSeek-V3.2: Pushing the frontier of open large language models, 2025a. URL https:/","arxiv_match":true}],"id":"2412.19437","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":125},{"title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity","arxiv_id":"2101.03961","year":2021,"url":"https://arxiv.org/abs/2101.03961","citing_submissions":622,"explicit_arxiv_matches":59,"examples":[{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"lf-distillation for diffusion language models. CoRR, abs/2607.04428, 2026. doi: 10.48550/ARXIV.2607.04428. URL https://doi.org/10.48550/arXiv.2607.04428. William Fedus, Barret Zoph, and Noam Shazeer. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. J. Mach. Learn. Res., 23:120:1–120:39, 2022. URL https://jmlr.org/papers/v23/21-0998.html. Haoyu He, Katrin Renz, Yong Cao, and Andreas Geiger. MDPO: overcoming the training-inference divide of mask","arxiv_match":false},{"id":"uwSMvBYqGq","title":"Auditing Optimization and Probabilistic Reliability in Frozen Vision-Language Representation Intervention","forum_url":"https://openreview.net/forum?id=uwSMvBYqGq","excerpt":"ng, Bing Qin, and Chin-Yew Lin. Enhancing neural data-to-text generation models with external background knowledge. In EMNLP-IJCNLP, pp. 3020–3030, 2019. William Fedus, Barret Zoph, and Noam Shazeer. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. J. Mach. Learn. Res., 23:120:1–120:39, 2022. Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. On calibration of modern neural networks. In ICML, 2017. Geoffrey Hinton, Oriol Vinyals, and Je","arxiv_match":false},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":"iv.org/abs/2512.02556. DeepSeek-AI. DeepSeek-V4: Towards highly efficient million-token context intelligence, 2026. URL https://arxiv.org/abs/2606.19348. William Fedus, Barret Zoph, and Noam Shazeer. Switch Transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 23(120):1–39, 2022. Adithya Gangidi, Rui Miao, Shengbao Zheng, Sai Jayesh Bondu, Guilherme Goes, Hany Morsy, Rohit Puri, Mohammad Riftadi, Ashmitha Jeevaraj She","arxiv_match":false}],"id":"2101.03961","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":126},{"title":"V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning","arxiv_id":"2506.09985","year":2025,"url":"https://arxiv.org/abs/2506.09985","citing_submissions":617,"explicit_arxiv_matches":571,"examples":[{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"ili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, and Nicolas Ballas. V-JEPA 2: Self-supervised video models enable understanding, prediction and planning. arXiv preprint arXiv:2506.09985, 2025. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, W","arxiv_match":true},{"id":"YzI2kFui6F","title":"CoMo: Efficient Video Transformer via Codec Residual and Motion-Field Geometry","forum_url":"https://openreview.net/forum?id=YzI2kFui6F","excerpt":"Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 6836–6846, 2021. Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, et al. V-jepa 2: Self-supervised video models enable understanding, prediction and planning. arXiv preprint arXiv:2506.09985, 2025. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, W","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"ey, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, and Nicolas Ballas. V-jepa 2: Self-supervised video models enable understanding, prediction and planning, 2025. URL https://arxiv.org/abs/2506.09985. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Din","arxiv_match":true}],"id":"2506.09985","metadata_source":"Cached arXiv API metadata","rank":127},{"title":"Graph attention networks","arxiv_id":"1710.10903","year":2017,"url":"https://arxiv.org/abs/1710.10903","citing_submissions":612,"explicit_arxiv_matches":121,"examples":[{"id":"z4rdoeoTB9","title":"Context-Conditioned Anomalous Subgraph Representation Learning for Brain Disease Detection","forum_url":"https://openreview.net/forum?id=z4rdoeoTB9","excerpt":"rk measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3):1059–1069, 2010. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations (ICLR), pp. 1–12, 2018. Fuli Wang, Karelia Pena-Pena, Wei Qian, and Gonzalo R. Arce. T-HyperGNNs: Hypergraph neural networks via tensor repres","arxiv_match":false},{"id":"jjf3FKJp3k","title":"MetaBrainPro: A Graph Learning Approach for Enterprise Metadata Lineage Retrieval","forum_url":"https://openreview.net/forum?id=jjf3FKJp3k","excerpt":"on graphs: A survey. arXiv preprint Yue Sun, Zhanpeng Zhang, and Chongxuan Li. Gradient flow analysis of neural networks with multiplicative gates. arXiv preprint arXiv:2302.14152, 2023. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Petar Velickovic et al. Graph attention networks. arXiv preprint arXiv:1710.10903, 2018. Nitika Verma, Zhanpeng Zhang, Yifei Wang, et al. Prediction of molecular properties with gra","arxiv_match":true},{"id":"xVqGCq3X2z","title":"SRAM: Learning Spectral Relation-Aware Representations for Environmental Sound Deepfake Detection","forum_url":"https://openreview.net/forum?id=xVqGCq3X2z","excerpt":"n ICASSP, 2025. Jee-weon Jung, Hee-Soo Heo, Hemlata Tak, Hye-jin Shim, Joon Son Chung, Bong-Jin Lee, Ha-Jin Yu, and Nicholas W. D. Evans. AASIST: audio anti-spoofing using integrated spectro-temporal graph attention networks. In ICASSP, 2022. Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Nikhil Priyatam Pattisapu, Arindam Bhattacharya, Shailendra Agarwal, an","arxiv_match":false}],"id":"1710.10903","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":128},{"title":"Kimi K3: Open Frontier Intelligence","arxiv_id":"2607.24653","year":2026,"url":"https://arxiv.org/abs/2607.24653","citing_submissions":611,"explicit_arxiv_matches":507,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"me 229 of Proceedings of Machine Learning Research, pp. 540–562, 2023. URL https://proceedings.mlr.press/v229/ huang23b.html. Baoxiong Jia, Ting Lei, Song-Chun Zhu, and Siyuan Huang. EgoTaskQA: Understanding human tasks in egocentric videos. In Advances in Neural Information Processing Systems, volume 35, 2022. URL https://arxiv.org/abs/2210.03929. Kimi Team. Kimi K3: Open Frontier Intelligence. arXiv preprint arXiv:2607.24653, 2026. URL https://arxiv.org/abs/2607.24653. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping L","arxiv_match":true},{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"ai Zhao, Wenshuo Zhao, Xiangyu Zhao, Xuanle Zhao, Yikai Zhao, Zijia Zhao, Haozhi Zheng, Huabin Zheng, Ruihan Zheng, Shaojie Zheng, Tengyang Zheng, Haofeng Zhong, Lei Zhong, Longguang Zhong, M. Zhou, Qiankang Zhou, Runjie Zhou, Ruozhang Zhou, Xinyu Zhou, Yiqiao Zhou, Zaida Zhou, Jinguo Zhu, Liya Zhu, Xinhao Zhu, Yangjunfeng Zhu, Yuxuan Zhu, Zhen Zhu, Chen Zhuang, Weiyu Zhuang, and Xinxing Zu. Kimi k3: Open frontier intelligence, 2026. URL https://arxiv.org/abs/2607.24653. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dus","arxiv_match":true},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"RNNs: Fast Autoregressive Transformers with Linear Attention. In ICML, 2020. Kimi Team. Kimi Linear: An Expressive, Efficient Attention Architecture. arXiv preprint arXiv:2510.26692, 2025. Kimi Team. Kimi K3: Open Frontier Intelligence. Moonshot AI technical blog, https://www.kimi.com/blog/kimi-k3, 2026a. Kimi Team. Attention Residuals. arXiv preprint arXiv:2603.15031, 2026b. Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, et al. Huny","arxiv_match":false}],"id":"2607.24653","metadata_source":"Cached arXiv API metadata","rank":129},{"title":"RULER: What’s the real context size of your long-context language models?","arxiv_id":"2404.06654","year":2024,"url":"https://arxiv.org/abs/2404.06654","citing_submissions":611,"explicit_arxiv_matches":332,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"oper, Sehoon Kim, Hiva Mohammadzadeh, Michael W Mahoney, Yakun S Shao, Kurt Keutzer, and Amir Gholami. Kvquant: Towards 10 million context length llm inference with kv cache quantization. Advances in Neural Information Processing Systems, 37:1270–1303, 2024. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. Ruler: What’s the real context size of your long-context language models? arXiv preprint arXiv:2404.06654, 2024. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplo","arxiv_match":true},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"neurips.cc/paper_files/paper/ 2024/file/028fcbcf85435d39a40c4d61b42c99a4-Paper-Conference.pdf. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, and Boris Ginsburg. RULER: What’s the real context size of your long-context language models? In First Conference on Language Modeling, 2024. URL https://openreview.net/forum? id=kIoBbc76Sy. Yuxiang Huang, Binhang Yuan, Xu Han, Chaojun Xiao, and Zhiyuan Liu. Locret: Enhancing eviction in lon","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"ium Series, volume 2, pp. 277–280, 2023. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. Ruler: What’s the real context size of your long-context language models? arXiv preprint arXiv:2404.06654, 2024. Yuanzhe Hu, Yu Wang, and Julian McAuley. Evaluating memory in llm agents via incremental mul","arxiv_match":true}],"id":"2404.06654","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":130},{"title":"Representation Learning with Contrastive Predictive Coding","arxiv_id":"1807.03748","year":2018,"url":"https://arxiv.org/abs/1807.03748","citing_submissions":610,"explicit_arxiv_matches":562,"examples":[{"id":"zoKRDAQpft","title":"AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval","forum_url":"https://openreview.net/forum?id=zoKRDAQpft","excerpt":"Semih Yavuz. VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents. arXiv preprint arXiv:2507.04590, 2025. Irene Martin Morato and Annamaria Mesaros. Diversity and bias in audio captioning datasets. In Proceedings of the 6th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2021. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. Roi Pony, Adi Raz Goldfarb, Oshri Naparstek, Idan Friedman, Udi Barzelay, and Eli Schwartz. ","arxiv_match":true},{"id":"iPvVOfpJOk","title":"RAMP: Reliability Adaptive Masking for Pretraining Noisy Multimodal Physiological Signals","forum_url":"https://openreview.net/forum?id=iPvVOfpJOk","excerpt":"ng. ECG-FM: An open electrocardiogram foundation model. Jamia Open, 8(5):ooaf122, 2025. doi: 10.1093/jamiaopen/ ooaf122. Yeongyeon Na, Minje Park, Yunwon Tae, and Sunghoon Joo. Guiding masked representation learning to capture spatio-temporal relationship of electrocardiogram. In International Conference on Learning Representations, 2024. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. Junyung Park, Hyeon Seok Seok, Sang-Su Kim, and Hangsik Shin. Photoplethysmogram analysis an","arxiv_match":true},{"id":"aKgBcssxN7","title":"Behavior-Aligned Neural Supervision for Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=aKgBcssxN7","excerpt":"k modes with one stone. In Advances in Neural Information Processing Systems, volume 35, 2022. The Brainmu Authors. Brainmu project, 2026. BAAI-Brain-Inspired-Group/Brainmu. URL https://github.com/ Emanuel Todorov and Michael I. Jordan. Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5(11):1226–1235, 2002. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. Caiyue Xu, Hongrui Sang, Changming Zhang, Yanmin Zhou, Zhipeng Wang, Xin Li, and Bin He. Neu","arxiv_match":true}],"id":"1807.03748","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":131},{"title":"Sentence-BERT: Sentence embeddings using Siamese BERTnetworks","arxiv_id":"1908.10084","year":2019,"url":"https://arxiv.org/abs/1908.10084","citing_submissions":609,"explicit_arxiv_matches":69,"examples":[{"id":"Y15G7fdvPv","title":"S$^2$Aligner: Pair-Efficient Transferable Pre-Training for Sparse Text-Attributed Graphs","forum_url":"https://openreview.net/forum?id=Y15G7fdvPv","excerpt":", Guy Wolf, and Dominique Beaini. Recipe for a general, powerful, scalable graph transformer. Advances in Neural Information Processing Systems, 35:14501–14515, 2022. Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bertnetworks. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp. 3982–3992, 201","arxiv_match":false},{"id":"do6Wfg04EK","title":"ATTEST: Evidence-Grounded Reinforcement Learning for Long-Audio Temporal Understanding","forum_url":"https://openreview.net/forum?id=do6Wfg04EK","excerpt":"cLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. In International conference on machine learning, pp. 28492–28518. PMLR, 2023. Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bertnetworks. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp. 3982–3992, 201","arxiv_match":false},{"id":"uwSMvBYqGq","title":"Auditing Optimization and Probabilistic Reliability in Frozen Vision-Language Representation Intervention","forum_url":"https://openreview.net/forum?id=uwSMvBYqGq","excerpt":"614–1623, 2016. Vinay Venkatesh Ramasesh, Ethan Dyer, and Maithra Raghu. Anatomy of catastrophic forgetting: Hidden representations and task semantics. In ICLR, 2021. Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bertnetworks. In EMNLP-IJCNLP, 2019. Table 8: Experimental Setup and Baseline Specifications Model / Baseline Backbone Precision & Quantization Hardware Configuration VEFT-Family & Weight-Space LoReFT-Style Basel","arxiv_match":false}],"id":"1908.10084","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":132},{"title":"LLaMA: Open and Efficient Foundation Language Models","arxiv_id":"2302.13971","year":2023,"url":"https://arxiv.org/abs/2302.13971","citing_submissions":600,"explicit_arxiv_matches":514,"examples":[{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"er Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. CoRR, abs/2302.13971, 2023. Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. Quip#: Even better LLM quantization with hadamard incoherence and lattice codebooks. In ","arxiv_match":false},{"id":"6SzEZJld4O","title":"Watermarking Continuous Diffusion Language Models","forum_url":"https://openreview.net/forum?id=6SzEZJld4O","excerpt":"rves. SIGGRAPH Comput. Graph., 19(3): 245–254, July 1985. ISSN 0097-8930. doi: 10.1145/325165.325242. URL https://doi. org/10.1145/325165.325242. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. URL https://arxiv.org/abs/2302.13971. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kai","arxiv_match":true},{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":" Jones, and Cem Anil. Beyond data filtering: Knowledge localization for capability removal in llms, 2025. URL https://arxiv.org/abs/2512.05648. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. URL https://arxiv.org/abs/2302.13971. Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S ","arxiv_match":true}],"id":"2302.13971","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":133},{"title":"Steering llama 2 via contrastive activation addition","arxiv_id":"2312.06681","year":2023,"url":"https://arxiv.org/abs/2312.06681","citing_submissions":600,"explicit_arxiv_matches":84,"examples":[{"id":"ZhkFWRMi9r","title":"The Task-Bearing Tail of Activation Steering","forum_url":"https://openreview.net/forum?id=ZhkFWRMi9r","excerpt":"tor Veitch. The information geometry of softmax: Probing and steering. arXiv preprint arXiv:2602.15293, 2026. Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. Steering llama 2 via contrastive activation addition. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15504–15522, 2024. Daniel Scalena, Gabriele Sarti, and Malvina Nissim. Multi-p","arxiv_match":false},{"id":"hNkuYHn7Ty","title":"SCOPE: Sparse Coordinate Suppression Complements Activation Steering in Vision-Language Models","forum_url":"https://openreview.net/forum?id=hNkuYHn7Ty","excerpt":"ependent Steering for Multimodal LLMs. In Advances in Neural Information Processing Systems, volume 38, 2025. Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. Steering Llama 2 via Contrastive Activation Addition. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15504–15522, 2024. doi: 10.18653/v1/2024.acl-long.828. Youngji Roh, Hyunjin Ch","arxiv_match":false},{"id":"x6oIXJYZ7g","title":"LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs","forum_url":"https://openreview.net/forum?id=x6oIXJYZ7g","excerpt":"of the Association for Computational Linguistics: Human Language Technologies, Volume 1, pp. 1314–1324, 2018. Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. Steering llama 2 via contrastive activation addition. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15504–15522, 2024. Zhenning Shi, Yijia Zhu, Junhan Shi, Xun Zhang, Lei Wang, a","arxiv_match":false}],"id":"2312.06681","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":134},{"title":"gpt-oss-120b & gpt-oss-20b Model Card","arxiv_id":"2508.10925","year":2025,"url":"https://arxiv.org/abs/2508.10925","citing_submissions":597,"explicit_arxiv_matches":529,"examples":[{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"rips.cc/paper_files/paper/2018/ file/3ea2db50e62ceefceaf70a9d9a56a6f4-Paper.pdf. Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. Long-tail learning via logit adjustment. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=37nvvqkCo5. OpenAI, :, Sandhini Agarwal, Lama Ahmad, and Others. gpt-oss-120b & gpt-oss-20b model card. 2025. URL https://arxiv.org/abs/2508.10925. Maxime Oquab, Timothée Darcet, Théo Moutakanni, and Others. DINOv2: Learning robust visual featu","arxiv_match":true},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"ndal Simon, Karan Singhal, Yang Song, Dane Stuckey, Zhiqing Sun, Philippe Tillet, Sam Toizer, Foivos Tsimpourlas, Nikhil Vyas, Eric Wallace, Xin Wang, Miles Wang, Olivia Watkins, Kevin Weil, Amy Wendling, Kevin Whinnery, Cedric Whitney, Hannah Wong, Lin Yang, Yu Yang, Michihiro Yasunaga, Kristen Ying, Wojciech Zaremba, Wenting Zhan, Cyril Zhang, Brian Zhang, Eddie Zhang, and Shengjia Zhao. gpt-oss-120b & gpt-oss-20b model card, 2025. URL https://arxiv.org/abs/2508.10925. Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayihen","arxiv_match":true},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":" Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom. GAIA: a benchmark for General AI Assistants. In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 9025–9049, 2024. URL https://proceedings.iclr.cc/paper_files/paper/2024/file/ 25ae35b5b1738d80f1f03a8713e405ec-Paper-Conference.pdf. OpenAI. gpt-oss-120b & gpt-oss-20b Model Card, 2025. URL https://arxiv.org/abs/ 2508.10925. OpenAI. GPT-5.6 System Card. Technical report, OpenAI, July 2026. deploymentsafety.openai.com/gpt-","arxiv_match":true}],"id":"2508.10925","metadata_source":"Cached arXiv API metadata","rank":135},{"title":"Representation Engineering: A Top-Down Approach to AI Transparency","arxiv_id":"2310.01405","year":2023,"url":"https://arxiv.org/abs/2310.01405","citing_submissions":595,"explicit_arxiv_matches":553,"examples":[{"id":"hNkuYHn7Ty","title":"SCOPE: Sparse Coordinate Suppression Complements Activation Steering in Vision-Language Models","forum_url":"https://openreview.net/forum?id=hNkuYHn7Ty","excerpt":"he 41st International Conference on Machine Learning, pp. 62867–62891, 2024. Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, and Dan Hendrycks. Representation Engineering: A Top-Down Approach to AI Transparency. arXiv preprint arXiv:2310.01405, 2023a. Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. Un","arxiv_match":true},{"id":"x6oIXJYZ7g","title":"LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs","forum_url":"https://openreview.net/forum?id=x6oIXJYZ7g","excerpt":"guage models. In International Conference on Learning Representations, 2023. Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, and Dan Hendrycks. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405, 2023. Appendix Overview This appendix provides a complete and implementation-aligned extension of ","arxiv_match":true},{"id":"QLMHtA7hoi","title":"Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection","forum_url":"https://openreview.net/forum?id=QLMHtA7hoi","excerpt":"ng Zhou, Junqing Yu, and Wei Yang. Dual memory units with uncertainty regulation for weakly supervised video anomaly detection. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 3769–3777, 2023. [35] Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405, 2023. A I MPLEMENTATION AND E XPERIMENTAL D ETAILS A.1 BACKBONE C ONFIGURATIONS AND L O RA S ETUP ","arxiv_match":true}],"id":"2310.01405","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":136},{"title":"Sigmoid loss for language image pre-training","arxiv_id":"2303.15343","year":2023,"url":"https://arxiv.org/abs/2303.15343","citing_submissions":591,"explicit_arxiv_matches":51,"examples":[{"id":"zoKRDAQpft","title":"AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval","forum_url":"https://openreview.net/forum?id=zoKRDAQpft","excerpt":"metrics for semantic inference over event descriptions. Transactions of the association for computational linguistics (TACL), 2014. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In","arxiv_match":false},{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":" task-conditioned retrieval for long-horizon multi-modal robot memory. IEEE Robotics and Automation Letters, 11(5):5994–6001, 2026. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 11941–11952. IEEE, 2023. Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis Gudovskiy, Tomoyuk","arxiv_match":false},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"fication and detection dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 11941–11952, 2023. doi: 10.1109/ICCV51070.2023.01100. Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, and Jian Sun. Distri","arxiv_match":false}],"id":"2303.15343","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":137},{"title":"Instruction-Following Evaluation for Large Language Models","arxiv_id":"2311.07911","year":2023,"url":"https://arxiv.org/abs/2311.07911","citing_submissions":585,"explicit_arxiv_matches":550,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"25/hash/a4277440d50f1f15d2cb4c14f7e0c0d2-Abstract-Con ference.html. Chaohao Yuan, Chenghao Xiao, Yu Rong, Hong Cheng, and Long-Kai Huang. Decouple before integration: Test-time synthesis of SFT and RLVR task vectors, 2026. URL https://arxiv. org/abs/2605.00610v1. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. Instruction-following evaluation for large language models, 2023. URL https: //arxiv.org/abs/2311.07911v1. Jingang Zhou, Haiyang Guo, Yuan Ma, Han Zhu, and Xu-Yao Zhang. CAMFT: Conflict-aware mergeable fin","arxiv_match":true},{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"ai Zhang, Zihao Wang, Lin Gui, Swarnashree Mysore Sathyendra, Jaehwan Jeong, Victor Veitch, Wei Wang, Yunzhong He, Bing Liu, and Lifeng Jin. Chasing the Tail: Effective Rubric-based Reward Modeling for Large Language Model Post-Training, 2025. URL https://arxiv.org/ abs/2509.21500. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911, 2023. A ADDITIONAL EXPERIMENTAL DETAILS A.1 TRAINING AND EVALUATION CONFIGURATION Training budget ","arxiv_match":true},{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":"rthy NLP (TrustNLP 2026), pp. 402–412, 2026. Yonghui Yang, Wenjian Tao, Jilong Liu, Xingyu Zhu, Junfeng Fang, Weibiao Huang, Le Wu, Richang Hong, and Tat-Sent Chua. Revisiting robustness for llm safety alignment via selective geometry control. arXiv preprint arXiv:2602.07340, 2026. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911, 2023. Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, Rowan Wa","arxiv_match":true}],"id":"2311.07911","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":138},{"title":"Semi-supervised classification with graph convolutional networks","arxiv_id":"1609.02907","year":2016,"url":"https://arxiv.org/abs/1609.02907","citing_submissions":585,"explicit_arxiv_matches":153,"examples":[{"id":"z4rdoeoTB9","title":"Context-Conditioned Anomalous Subgraph Representation Learning for Brain Disease Detection","forum_url":"https://openreview.net/forum?id=z4rdoeoTB9","excerpt":"i, Hejie Cui, Zilong Zhang, Ying Guo, and Carl Yang. Brain network transformer. In Advances in Neural Information Processing Systems, volume 35, pp. 25586–25599, 2022. Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR), pp. 1–14, 2017. Devin Kreuzer, Dominique Beaini, William Hamilton, Vincent Létourneau, and Prudencio Tossou. Rethinking graph transfo","arxiv_match":false},{"id":"jjf3FKJp3k","title":"MetaBrainPro: A Graph Learning Approach for Enterprise Metadata Lineage Retrieval","forum_url":"https://openreview.net/forum?id=jjf3FKJp3k","excerpt":"earning for image recognition. arXiv preprint arXiv:1512.03385, 2016. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. Qiang Li, Zeming Han, and Xiao-Ming Meng. Deeper insights into graph convolutional networks ","arxiv_match":true},{"id":"tndHur3QK0","title":"FedN-TAGC: Federated Node-Level Text-Attributed Graph Clustering","forum_url":"https://openreview.net/forum?id=tndHur3QK0","excerpt":"l Conference on Learning Representations, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/hash/ 83b17fb3369b1effa97ca5409526b02e-Abstract-Conference.html. Thomas N. Kipf and Max Welling. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2016. Thomas N. Kipf and Max Welling. Graph Auto-Encoders. In Proceedings of the International Conference on Learni","arxiv_match":false}],"id":"1609.02907","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":139},{"title":"SAM 3: Segment Anything with Concepts","arxiv_id":"2511.16719","year":2025,"url":"https://arxiv.org/abs/2511.16719","citing_submissions":584,"explicit_arxiv_matches":250,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":" Markus Marks, Joseph Greer, Meng Wang, Peize Sun, Roman Rädle, Triantafyllos Afouras, Effrosyni Mavroudi, Katherine Xu, Tsung-Han Wu, Yu Zhou, Liliane Momeni, Rishi Hazra, Shuangrui Ding, Sagar Vaze, Francois Porcher, Feng Li, Siyuan Li, Aishwarya Kamath, Ho Kei Cheng, Piotr Dollár, Nikhila Ravi, Kate Saenko, Pengchuan Zhang, and Christoph Feichtenhofer. SAM 3: Segment anything with concepts. arXiv preprint arXiv:2511.16719, 2025. URL https://arxiv.org/abs/2511.16719. Hanlin Chen, Jiaxin Wei, Xibin Song, Yifu Wang, Steve Wang, Hongdong Li, Pan Ji, and Gim Hee Lee. ","arxiv_match":true},{"id":"AzPpBFMquF","title":"Segment Anything In-Context via Visual Demonstrations","forum_url":"https://openreview.net/forum?id=AzPpBFMquF","excerpt":"on, pp. 213–229. Springer, 2020. Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, Shoubhik Debnath, Ronghang Hu, Didac Suris, Chaitanya Ryali, Kalyan Vasudev Alwala, Haitham Khedr, Andrew Huang, et al. Sam 3: Segment anything with concepts. In International Conference on Learning Representations (ICLR), 2026. Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties ","arxiv_match":false},{"id":"Z4dlVT9XRr","title":"VastMAT: A Large-Scale Multi-Category Benchmark for Multi-Animal Tracking","forum_url":"https://openreview.net/forum?id=Z4dlVT9XRr","excerpt":"enchmark for generic multiple object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6719–6728, 2021. Keni Bernardin and Rainer Stiefelhagen. Evaluating multiple object tracking performance: The CLEAR MOT metrics. EURASIP Journal on Image and Video Processing, pp. 246309, 2008. doi: 10.1155/2008/246309. Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, et al. SAM 3: Segment anything with concepts. arXiv preprint arXiv:2511.16719, 2025. Jun Chen, Ming Hu, Darren J. Coker, Michael L. Berumen, Blair Costelloe, Sara Beery, Anna Ro","arxiv_match":true}],"id":"2511.16719","metadata_source":"Cached arXiv API metadata","rank":140},{"title":"Self-supervised learning from images with a joint-embedding predictive architecture","arxiv_id":"2301.08243","year":2023,"url":"https://arxiv.org/abs/2301.08243","citing_submissions":582,"explicit_arxiv_matches":85,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":" Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15619–15629, June 2023a. Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent","arxiv_match":false},{"id":"mlVqTuV7b8","title":"Closing the Loop on Masking Difficulty for Joint-Embedding Predictive Learning","forum_url":"https://openreview.net/forum?id=mlVqTuV7b8","excerpt":" ’22, Red Hook, NY, USA, 2022. Curran Associates Inc. ISBN 9781713871088. Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15619–15629, June 2023. Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami, Mehdi Nikkhah, Motila","arxiv_match":false},{"id":"iPvVOfpJOk","title":"RAMP: Reliability Adaptive Masking for Pretraining Noisy Multimodal Physiological Signals","forum_url":"https://openreview.net/forum?id=iPvVOfpJOk","excerpt":"orks, Computational Intelligence and Machine Learning, pp. 437–442, 2013. Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 15619–15629, 2023. doi: 10.1109/CVPR52729.2023.01499. Yoshua Bengio, Jérôme Louradour, Ronan Collobert, an","arxiv_match":false}],"id":"2301.08243","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":141},{"title":"Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning","arxiv_id":"2503.09516","year":2025,"url":"https://arxiv.org/abs/2503.09516","citing_submissions":581,"explicit_arxiv_matches":402,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"g Papers), pp. 12355–12369, 2025. Yuxiang Ji, Ziyu Ma, Yong Wang, Guanhua Chen, Xiangxiang Chu, and Liaoni Wu. Tree search for llm agent reinforcement learning. In International Conference on Learning Representations, volume 2026, pp. 87362–87388, 2026. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.09516, 2025. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi C","arxiv_match":true},{"id":"9N1uPPmr4W","title":"Shared Resources Distort Credit Assignment in Group-Relative Agent Reinforcement Learning","forum_url":"https://openreview.net/forum?id=9N1uPPmr4W","excerpt":"atistical Association, 103(482):832–842, 2008. Yuxiang Ji, Ziyu Ma, Yong Wang, Guanhua Chen, Xiangxiang Chu, and Liaoni Wu. Tree search for LLM agent reinforcement learning. In International Conference on Learning Representations, pp. 87362–87388, 2026. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. Search-R1: Training LLMs to reason and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.09516, 2025. Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min ","arxiv_match":true},{"id":"ROD8EjYEgO","title":"One Answer to Grade Them All? Reference-Based Evaluation and Its Optimization Effects in Search Agents","forum_url":"https://openreview.net/forum?id=ROD8EjYEgO","excerpt":", Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems, 2021. URL https://arxiv. org/abs/2110.14168. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning, 2025. URL https://arxiv.org/abs/2503.09516. Ehsan Kamalloo, Nouha Dziri, Charles Clarke, and Davood Rafiei. Evaluating open-domain question an","arxiv_match":true}],"id":"2503.09516","metadata_source":"Cached arXiv API metadata","rank":142},{"title":"Segment Anything","arxiv_id":null,"year":2023,"url":null,"citing_submissions":577,"explicit_arxiv_matches":0,"examples":[{"id":"xb6eQlQ8CB","title":"Light SAM3 Agent: Efficient Agentic Segmentation via Latent NPs Extraction and Action Space Slimming","forum_url":"https://openreview.net/forum?id=xb6eQlQ8CB","excerpt":"or parallel function calling. arXiv, 2023. Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In 2023 IEEE/CVF international conference on computer vision (ICCV), pp. 3992–4003. IEEE, 2023. Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia. Lisa: Reasoning seg","arxiv_match":false},{"id":"BGtau6E80m","title":"Image Editing without Typing","forum_url":"https://openreview.net/forum?id=BGtau6E80m","excerpt":"bs/2510.12798. Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. Segment Anything. arXiv:2304.02643, 2023. Boris Kravtsov. hand-drawn-shape-simulation. https://github.com/boriskravtsov/h and-drawn-shape-simulation, 2025. Accessed: 2026-09-22. Shiyu Liu, Yucheng Han, Peng Xing, Fuk","arxiv_match":false},{"id":"e4gvA0ZGY5","title":"ReasonRSOS: Reasoning Salient Object Segmentation in Remote Sensing Images","forum_url":"https://openreview.net/forum?id=e4gvA0ZGY5","excerpt":".org/D14-1086. Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. Segment anything. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3992–4003, 2023. doi: 10.1109/ICCV51070.2023.00371. Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and J","arxiv_match":false}],"id":"cea42d914233a4c0","metadata_source":"Curated bibliography catalog / publisher metadata","rank":143},{"title":"MetaGPT: Meta programming for a multi-agent collaborative framework","arxiv_id":"2308.00352","year":2023,"url":"https://arxiv.org/abs/2308.00352","citing_submissions":575,"explicit_arxiv_matches":111,"examples":[{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"eural information processing systems, 37:59532–59569, 2024. Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Steven Yau, Zijuan Lin, Liyang Zhou, et al. Metagpt: Meta programming for a multiagent collaborative framework. In International Conference on Learning Representations, volume 2024, pp. 23247–23275, 2024. Jovana Kondic, Pengyuan Li, Dhiraj Joshi, Isaac Sanchez, Ben Wiesel, Shafiq Abedin, Amit Alfassy, Eli Sch","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"n web agents. arXiv preprint arXiv:2603.27490, 2026. Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, et al. Metagpt: Meta programming for a multiagent collaborative framework. In The twelfth international conference on learning representations, 2023. Bochen Han and Songmao Zhang. Exploring advanced llm multi-agent systems based on blackboard architecture. arXiv preprint a","arxiv_match":false},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"ti-Agent Research System. Anthropic, 2025. URL https://www. anthropic.com/engineering/multi-agent-research-system. Sirui Hong, Mingchen Zhuge, Jiaqi Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. MetaGPT: Meta programming for a multi-agent collaborative framework. arXiv preprint arXiv:2308.00352, 2023. URL https://arxiv.org/ abs/2308.00352. Thomas Hubert, Rishi Mehta, Laurent Sartran, et al. Olympiad-level formal mathematical reasoning w","arxiv_match":true}],"id":"2308.00352","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":144},{"title":"Understanding R1-Zero-Like Training: A Critical Perspective","arxiv_id":"2503.20783","year":2025,"url":"https://arxiv.org/abs/2503.20783","citing_submissions":571,"explicit_arxiv_matches":369,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"hen, Hongxu Yin, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Yejin Choi, Jan Kautz, and Pavlo Molchanov. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multireward RL Optimization, 2026. URL https://arxiv.org/abs/2601.05242. Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Understanding R1-Zero-like training: A critical perspective. arXiv preprint arXiv:2503.20783, 2025. URL https://arxiv.org/abs/2503.20783. Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel L","arxiv_match":true},{"id":"9N1uPPmr4W","title":"Shared Resources Distort Credit Assignment in Group-Relative Agent Reinforcement Learning","forum_url":"https://openreview.net/forum?id=9N1uPPmr4W","excerpt":"reason and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.09516, 2025. Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Understanding R1-Zero-like training: A critical perspective. In Second Conference on Language Modeling, 2025. Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, and Ruoming Pang","arxiv_match":false},{"id":"cbEkVPsGSJ","title":"DriveBeyond: Rethinking Group Advantage for Driving Policy Optimization","forum_url":"https://openreview.net/forum?id=cbEkVPsGSJ","excerpt":"i: phi-1.5 technical report. arXiv preprint arXiv:2309.05463, 2023. Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, and Zhi-Quan Luo. ReMax: A simple, effective, and efficient reinforcement learning method for aligning large language models. In ICML, pp. 29128–29163, 2024. Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Understanding R1-Zero-like training: A critical perspective. arXiv preprint arXiv:2503.20783, 2025. Yiren Lu, Xin Ye, Jiaming Liu, Philip Jacobson, Jin Yao, Yi-chung Chen, Liam Merino, Dhruva ","arxiv_match":true}],"id":"2503.20783","metadata_source":"Cached arXiv API metadata","rank":145},{"title":"GR00T N1: An Open Foundation Model for Generalist Humanoid Robots","arxiv_id":"2503.14734","year":2025,"url":"https://arxiv.org/abs/2503.14734","citing_submissions":568,"explicit_arxiv_matches":516,"examples":[{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"ed, You Liang Tan, Guanzhi Wang, Zu Wang, Jing Wang, Qi Wang, Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhenjia Xu, Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, and Yuke Zhu. Gr00t n1: An open foundation model for generalist humanoid robots, 2025. Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action mo","arxiv_match":false},{"id":"fKq8qGzBwD","title":"Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning","forum_url":"https://openreview.net/forum?id=fKq8qGzBwD","excerpt":", Kaushil Kundalia, Lawrence Lao, Zhiqi Li, Zongyu Lin, Kevin Lin, Guilin Liu, Edith Llontop, Loic Magne, Ajay Mandlekar, Avnish Narayan, Soroush Nasiriany, Scott Reed, You Liang Tan, Guanzhi Wang, Zu Wang, Jing Wang, Qi Wang, Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhenjia Xu, Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, and Yuke Zhu. Gr00t n1: An open foundation model for generalist humanoid robots, 2025. URL https://arxiv.org/abs/2503.14734. NVIDIA, :, Aditi, Niket Agarwal, Arslan Ali, Jon Allen, Martin Antolini, Adeline Aubame, Alisson A","arxiv_match":true},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":" Kaushil Kundalia, Lawrence Lao, Zhiqi Li, Zongyu Lin, Kevin Lin, Guilin Liu, Edith Llontop, Loic Magne, Ajay Mandlekar, Avnish Narayan, Soroush Nasiriany, Scott Reed, You Liang Tan, Guanzhi Wang, Zu Wang, Jing Wang, Qi Wang, Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhenjia Xu, Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, and Yuke Zhu. Gr00t n1: An open foundation model for generalist humanoid robots, 2025. URL https: //arxiv.org/abs/2503.14734. Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. Kosmos-2: ","arxiv_match":true}],"id":"2503.14734","metadata_source":"Cached arXiv API metadata","rank":146},{"title":"Similarity of neural network representations revisited","arxiv_id":"1905.00414","year":2019,"url":"https://arxiv.org/abs/1905.00414","citing_submissions":567,"explicit_arxiv_matches":46,"examples":[{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"ngs of the International Conference on Machine Learning, pp. 5637–5664, 2021. URL https://proceedings.mlr.press/ v139/koh21a.html. Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In Proceedings of the International Conference on Machine Learning, pp. 3519–3529, 2019. URL https://proceedings.mlr.press/v97/ kornblith19a.html. Nikolaus Kriegeskorte, Marieke Mur, and Peter A Ban","arxiv_match":false},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"oceedings of Machine Learning Research, pp. 24872–24895. PMLR, 21–27 Jul 2024. URL https: //proceedings.mlr.press/v235/ko24c.html. Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In International conference on machine learning, pp. 3519– 3529. PMlR, 2019. Chanhyuk Lee, Jiho Choi, Chanryeol Lee, Donggyun Kim, and Seunghoon Hong. Adarank: Adaptive rank pruning for enhanced mod","arxiv_match":false},{"id":"VxTxDgRvRP","title":"Uncovering Understanding-Generation Synergy in Native Unified Multimodal Models: From Representation, Task to System","forum_url":"https://openreview.net/forum?id=VxTxDgRvRP","excerpt":"ligence. arXiv preprint arXiv:2607.24653, 2026. Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014. Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In ICML, 2019. Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023. ","arxiv_match":false}],"id":"1905.00414","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":147},{"title":"Qwen-Image Technical Report","arxiv_id":"2508.02324","year":2025,"url":"https://arxiv.org/abs/2508.02324","citing_submissions":565,"explicit_arxiv_matches":520,"examples":[{"id":"BGtau6E80m","title":"Image Editing without Typing","forum_url":"https://openreview.net/forum?id=BGtau6E80m","excerpt":"yuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun Wen, Wensen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang, Yongqiang Zhu, Yujia Wu, Yuxuan Cai, and Zenan Liu. Qwen-Image Technical Report, 2025. URL https://arxiv.org/ab s/2508.02324. Bin Xia, Bohao Peng, Jiyang Liu, Sitong Wu, Jingyao Li, Junjia Huang, Xu Zhao, Yitong Wang, Ruihang Chu, Bei Yu, et al. DreamOmni3: Scribble-based Editin","arxiv_match":false},{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"yuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun Wen, Wensen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang, Yongqiang Zhu, Yujia Wu, Yuxuan Cai, and Zenan Liu. Qwen-image technical report. CoRR, abs/2508.02324, 2025. doi: 10.48550/ ARXIV.2508.02324. URL https://doi.org/10.48550/arXiv.2508.02324. Chengyue Wu, Shiyi Lan, Yonggan Fu, Sensen Gao, Jin Wang, Jincheng Yu, Jose M. Alvarez, Pa","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"ai, Xiao Xu, Yilei Chen, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhengyi Wang, An Yang, Bowen Yu, Chen Cheng, Dayiheng Liu, Deqing Li, Hang Zhang, Hao Meng, Hu Wei, Jingyuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun Wen, Wensen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang, Yongqiang Zhu, Yujia Wu, Yuxuan Cai, and Zenan Liu. Qwen-image technical report. arXiv preprint arXiv:2508.02324, 2025. URL https://arxiv.org/abs/2508.02324. Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human pre","arxiv_match":true}],"id":"2508.02324","metadata_source":"Cached arXiv API metadata","rank":148},{"title":"Improved baselines with visual instruction tuning","arxiv_id":"2310.03744","year":2023,"url":"https://arxiv.org/abs/2310.03744","citing_submissions":561,"explicit_arxiv_matches":64,"examples":[{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"ng, and Zhongyu Wei. Hardcorelogic: Challenging large reasoning models with long-tail logic puzzle games. arXiv preprint arXiv:2510.12563, 2025. Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 26296–26306, 2024. Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. Is your code generated by","arxiv_match":false},{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":" Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36:34892–34916, 2023. Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 26296–26306, 2024. Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, and Ji","arxiv_match":false},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":". Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 34892– 34916. Curran Associates, Inc., 2023b. doi: 10.52202/075280-1516. URL https://proceedings.neurips.cc/paper_files/paper/2023/file/ 6dcf277ea32ce3288914faf369fe6de0-Paper-Conference.pdf. Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning, 2024. URL https://arxiv.org/abs/2310.03744. Wufei Ma, Haoyu Chen, Guofeng Zhang, Yu-Cheng Chou, Jieneng Chen, Celso de Melo, and Alan Yuille. ","arxiv_match":true}],"id":"2310.03744","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":149},{"title":"Masked autoencoders are scalable vision learners","arxiv_id":"2111.06377","year":2021,"url":"https://arxiv.org/abs/2111.06377","citing_submissions":561,"explicit_arxiv_matches":45,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"nd salient object detection. IEEE Transactions on Image Processing, 34:608–622, 2025. doi: 10.1109/TIP.2025.3528347. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16000–16009, June 2022a. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Gir","arxiv_match":false},{"id":"gvhGB5w5GZ","title":"BrainSaber: Reconstruction-Based Information Decomposition for Functional Brain Atlas Construction","forum_url":"https://openreview.net/forum?id=gvhGB5w5GZ","excerpt":"en. A multi-modal parcellation of human cerebral cortex. Nature, 536(7615):171–178, 2016. doi: 10.1038/nature18933. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009, 2022. URL https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_ Autoencoders_Are_Scala","arxiv_match":false},{"id":"AzPpBFMquF","title":"Segment Anything In-Context via Visual Demonstrations","forum_url":"https://openreview.net/forum?id=AzPpBFMquF","excerpt":"g. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9729–9738, 2020. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16000–16009, 2022. Peter Johan Heiberg Engel. Tacit knowledge and visual expertise in medical diagnos","arxiv_match":false}],"id":"2111.06377","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":150},{"title":"Elucidating the design space of diffusionbased generative models","arxiv_id":"2206.00364","year":2022,"url":"https://arxiv.org/abs/2206.00364","citing_submissions":551,"explicit_arxiv_matches":51,"examples":[{"id":"zPfUFbzLtK","title":"Numerical Calibration for Physics-Informed Diffusion Posterior Sampling","forum_url":"https://openreview.net/forum?id=zPfUFbzLtK","excerpt":"t and conditioned score-based generative models for forward and inverse problems. SIAM Journal on Scientific Computing, 47(2):C399–C425, 2025. Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusionbased generative models. Advances in neural information processing systems, 35:26565–26577, 2022. Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. Denoising diffusion restoration models. Advances in neural infor","arxiv_match":false},{"id":"D2lvweC7MC","title":"When Are Diffusion Recovery Clocks Identifiable? A Spectral Measurement Protocol for Teacher-Forced Denoising","forum_url":"https://openreview.net/forum?id=D2lvweC7MC","excerpt":"y Yang, and Steven Hoi. An empirical study of training pixel-space text-to-image diffusion models, 2026. URL https://arxiv.org/abs/2608.16887. Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusionbased generative models. Advances in neural information processing systems, 35:26565–26577, 2022. Haeil Lee, Hansang Lee, Seoyeon Gye, and Junmo Kim. Beta sampling is all you need: Efficient image generation strategy for di","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"er Newhouse, and Jeremy Bernstein. Muon: An optimizer for hidden layers in neural networks. https: //kellerjordan.github.io/posts/muon/, 2024. Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusionbased generative models. In NeurIPS, 2022. Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyzing and improving the training dynamics of diffusion models. In CVPR, 2024. Diederik","arxiv_match":false}],"id":"2206.00364","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":151},{"title":"GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning","arxiv_id":"2507.19457","year":2025,"url":"https://arxiv.org/abs/2507.19457","citing_submissions":547,"explicit_arxiv_matches":187,"examples":[{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"ms, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J. Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alex Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, and Omar Khattab. GEPA: reflective prompt evolution can outperform reinforcement learning. In The Fourteenth International Conference on Learning Representations, ICLR 2026, Rio de Janeiro, Brazil, April 23-27, 2026. proceedings.iclr.cc, 2026. Victor Barres, Honghua Dong, Soham Ray, Xujie","arxiv_match":false},{"id":"a0TQ3mSSY5","title":"NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents","forum_url":"https://openreview.net/forum?id=a0TQ3mSSY5","excerpt":" Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J. Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alexandros G. Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, and Omar Khattab. GEPA: Reflective prompt evolution can outperform reinforcement learning. In International Conference on Learning Representations, 2026. URL https://proceedings.iclr.cc/paper_files/paper/2026/hash/ 0e9e708b6f48e14fd0ac29e167413f76-Abstract-Conference.html. Ding Chen, Simi","arxiv_match":false},{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":" Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J. Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alexandros G. Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, and Omar Khattab. GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum? id=RQm2KQTM5r. Rahul K. Arora, Jason Wei, Rebecca Soskin Hicks, Preston Bowman, Joaqui","arxiv_match":false}],"id":"2507.19457","metadata_source":"Cached arXiv API metadata","rank":152},{"title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","arxiv_id":"2307.15043","year":2023,"url":"https://arxiv.org/abs/2307.15043","citing_submissions":544,"explicit_arxiv_matches":396,"examples":[{"id":"mVJzfFoRun","title":"Checking Leakage Witnesses versus Certifying Bounded Non-Leakage","forum_url":"https://openreview.net/forum?id=mVJzfFoRun","excerpt":"hierarchy. Theoretical Computer Science, 3 (1):23–33, 1976. Hirotoshi Yasuoka and Tachio Terauchi. On bounding problems of quantitative information flow. Journal of Computer Security, 19(6):1029–1082, 2011. doi: 10.3233/JCS-2011-0437. Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. Universal and transferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043, 2023. URL https://arxiv.org/abs/2307.15043. ","arxiv_match":true},{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":"ISBN 979-889176-390-6. doi: 10.18653/v1/2026.acl-long.2156. URL https://aclanthology.org/ 2026.acl-long.2156/. Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. Universal and transferable adversarial attacks on aligned language models, 2023. URL https://arxiv. org/abs/2307.15043. Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, Biqing Qi, Youbang Sun, Zhiyuan Ma, ","arxiv_match":false},{"id":"yECLVPwR3R","title":"Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation","forum_url":"https://openreview.net/forum?id=yECLVPwR3R","excerpt":" Vision Large Language Models. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 62867–62891. PMLR, 2024. URL https://proceedings. mlr.press/v235/zong24a.html. Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. Universal and Transferable Adversarial Attacks on Aligned Language Models. Technical report, arXiv, 2023. URL https://arxiv.org/abs/2307.15043. A P ERCEPT G UARD A NALYSIS AND I MPLEMENTATION A.1 E FFECTS OF COMPOSITIONAL RISK PERCEPTION SUPE","arxiv_match":true}],"id":"2307.15043","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":153},{"title":"Toolllm: Facilitating large language models to master 16000+ real-world apis","arxiv_id":"2307.16789","year":2023,"url":"https://arxiv.org/abs/2307.16789","citing_submissions":543,"explicit_arxiv_matches":142,"examples":[{"id":"o7ODDwTMgU","title":"Valid but Wrong: Decomposing Language-Model Errors on Closed Vocabularies","forum_url":"https://openreview.net/forum?id=o7ODDwTMgU","excerpt":"tic evaluation of large language models. In Proceedings of the 42nd International Conference on Machine Learning, 2025. Gustavo Correa Publio and José Emilio Labra Gayo. xpshacl: Explainable shacl validation using retrieval-augmented generation and large language models, 2025. URL https:// arxiv.org/abs/2507.08432. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv preprint arXiv:2307.16789, 2023. Avinash Reddy, Thayne T. Walker, James S. Ide, and Amrit Singh Bedi. The hidden cost of stru","arxiv_match":true},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"ng Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. Toolllm: Facilitating large language models to master 16000+ real-world apis. In Proceedings of The Twelfth International Conference on Learning Representations, 2024. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer,","arxiv_match":false},{"id":"CFVCzCNC1l","title":"Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=CFVCzCNC1l","excerpt":"ng Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. ToolLLM: Facilitating large language models to master 16000+ real-world APIs. In The Twelfth International Conference on Learning Representations, 2024b. Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, and Ji-rong Wen. Tool learning with l","arxiv_match":false}],"id":"2307.16789","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":154},{"title":"H2o: Heavyhitter oracle for efficient generative inference of large language models","arxiv_id":"2306.14048","year":2023,"url":"https://arxiv.org/abs/2306.14048","citing_submissions":541,"explicit_arxiv_matches":82,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"token carving. arXiv preprint arXiv:2505.16864, 2025b. Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, et al. H2o: Heavy-hitter oracle for efficient generative inference of large language models. Advances in Neural Information Processing Systems, 36:34661–34710, 2023. Tianchen Zhao, Ke Hong, Xinhao Yang, Xuefeng Xiao, Huixia Li, Feng Ling, Ruiqi Xie, Siqi Chen, Hongyu Zhu, Zhang Yichong, et ","arxiv_match":false},{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"g Representations, volume 2024, pp. 50490–50508, 2024. Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, et al. H2o: Heavy-hitter oracle for efficient generative inference of large language models. Advances in neural information processing systems, 36:34661–34710, 2023. Lancheng Zou, Shuo Yin, Zehua Pei, Tsung-Yi Ho, Farzan Farnia, and Bei Yu. Permllm: Learnable channel permutation for n: M sp","arxiv_match":false},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"forum?id= LCTmppB165. Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, Zhangyang ”Atlas” Wang, and Beidi Chen. H2o: Heavy-hitter oracle for efficient generative inference of large language models. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 34661–34710. Curran Associates, Inc., 2023. doi: 10.5","arxiv_match":false}],"id":"2306.14048","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":155},{"title":"Simple and effective masked diffusion language models","arxiv_id":"2406.07524","year":2024,"url":"https://arxiv.org/abs/2406.07524","citing_submissions":541,"explicit_arxiv_matches":82,"examples":[{"id":"6SzEZJld4O","title":"Watermarking Continuous Diffusion Language Models","forum_url":"https://openreview.net/forum?id=6SzEZJld4O","excerpt":"2020. URL http://jmlr.org/papers/v21/20-074.html. Subham Sekhar Sahoo, Marianne Arriola, Aaron Gokaslan, Edgar Mariano Marroquin, Alexander M Rush, Yair Schiff, Justin T Chiu, and Volodymyr Kuleshov. Simple and effective masked diffusion language models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. Tom Sander, Hongyan Chang, Tomáš Souček, Tuan Tran, Valeriu Lacatusu, Sylvestre-Alvise Rebuffi, Alexandre Mour","arxiv_match":false},{"id":"UtSpCnqHk8","title":"Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving","forum_url":"https://openreview.net/forum?id=UtSpCnqHk8","excerpt":"52K. ShareGPT52K, 2023. https://huggingface.co/datasets/RyokoAI/ Subham S Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, and Volodymyr Kuleshov. Simple and effective masked diffusion language models. Advances in Neural Information Processing Systems, 37:130136–130184, 2024. Rana Shahout, Eran Malach, Chunwei Liu, Weifan Jiang, Minlan Yu, and Michael Mitzenmacher. Don’t stop me now: Embedding bas","arxiv_match":false},{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"ine Learning, 2026. URL https: //arxiv.org/abs/2605.09603. Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T. Chiu, Alexander Rush, and Volodymyr Kuleshov. Simple and effective masked diffusion language models. In Advances in Neural Information Processing Systems, volume 37, 2024. URL https://proceedings.neurips.cc/paper_files/paper/2024/ hash/eb0b13cc515724ab8015bc978fdde0ad-Abstract-Conference.html. Yash","arxiv_match":false}],"id":"2406.07524","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":156},{"title":"Dream to control: Learning behaviors by latent imagination","arxiv_id":"1912.01603","year":2019,"url":"https://arxiv.org/abs/1912.01603","citing_submissions":538,"explicit_arxiv_matches":212,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"g world models in visual representation learning. arXiv preprint arXiv:2403.00504, 2024. David Ha and Jürgen Schmidhuber. Recurrent world models facilitate policy evolution. Advances in neural information processing systems, 31, 2018a. David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018b. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019. Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. Mastering atari with disc","arxiv_match":true},{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"latent dynamics for planning from pixels. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 2555–2565. PMLR, 2019. URL https://proceedings.mlr.press/v97/hafner19a.html. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. International Conference on Learning Representations, 2020. URL https://arxiv.org/abs/1912.01603. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse control tasks","arxiv_match":true},{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":"latent dynamics for planning from pixels. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 2555–2565. PMLR, 2019. URL https://proceedings.mlr.press/v97/hafner19a.html. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. International Conference on Learning Representations, 2020. URL https://arxiv.org/abs/1912.01603. Nicklas Hansen, Hao Su, and Xiaolong Wang. TD-MPC2: Scalable, robust world models for continuous c","arxiv_match":true}],"id":"1912.01603","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":157},{"title":"Improved Large Language Diffusion Models","arxiv_id":"2606.25331","year":2026,"url":"https://arxiv.org/abs/2606.25331","citing_submissions":538,"explicit_arxiv_matches":24,"examples":[{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"_files/paper/ 2025/file/720f9f5dc751eb56952ae4fee2398f73-Paper-Conference.pdf. Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models. CoRR, abs/2502.09992, 2025. doi: 10.48550/ARXIV.2502.09992. URL https://doi.org/10.48550/arXiv.2502. 09992. Daisuke Oba, Hiroki Furuta, and Naoaki Okazaki. Diffusion-state policy optimization for ma","arxiv_match":false},{"id":"oq0BXtA19v","title":"Test-Time Compositional Generalization in Diffusion Models via Prototype Discovery","forum_url":"https://openreview.net/forum?id=oq0BXtA19v","excerpt":"ing submodular set functions—I. Mathematical Programming, 14(1):265–294, 1978. Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, JUN ZHOU, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview. net/forum?id=KnqiC0znVF. Gabriel Raya and Luca Ambrogioni. Spontaneous symmetry breaking","arxiv_match":false},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"dex: Multimodal code generation for robotic behavior synthesis. In ICML, 2024. Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models, 2025. URL https:// arxiv.org/abs/2502.09992. Dantong Niu, Yuvan Sharma, Giscard Biamby, Jerome Quenum, Yutong Bai, Baifeng Shi, Trevor Darrell, and Roei Herzig. Llarva: Vision-action instruction tun","arxiv_match":false}],"id":"2606.25331","metadata_source":"Cached arXiv API metadata","rank":158},{"title":"Large Language Diffusion Models","arxiv_id":"2502.09992","year":2025,"url":"https://arxiv.org/abs/2502.09992","citing_submissions":538,"explicit_arxiv_matches":221,"examples":[{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"_files/paper/ 2025/file/720f9f5dc751eb56952ae4fee2398f73-Paper-Conference.pdf. Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models. CoRR, abs/2502.09992, 2025. doi: 10.48550/ARXIV.2502.09992. URL https://doi.org/10.48550/arXiv.2502. 09992. Daisuke Oba, Hiroki Furuta, and Naoaki Okazaki. Diffusion-state policy optimization for ma","arxiv_match":false},{"id":"oq0BXtA19v","title":"Test-Time Compositional Generalization in Diffusion Models via Prototype Discovery","forum_url":"https://openreview.net/forum?id=oq0BXtA19v","excerpt":"ing submodular set functions—I. Mathematical Programming, 14(1):265–294, 1978. Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, JUN ZHOU, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview. net/forum?id=KnqiC0znVF. Gabriel Raya and Luca Ambrogioni. Spontaneous symmetry breaking","arxiv_match":false},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":", Mosharaf Chowdhury, Yuke Zhu, Linxi Fan, and Guanzhi Wang. ASPIRE: Agentic skills discovery for robotics. arXiv preprint arXiv:2607.00272, 2026. Yao Mu, Junting Chen, Qinglong Zhang, et al. Robocodex: Multimodal code generation for robotic behavior synthesis. In ICML, 2024. Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models, 2025. URL https:// arxiv.org/abs/2502.09992. Dantong Niu, Yuvan Sharma, Giscard Biamby, Jerome Quenum, Yutong Bai, Baifeng Shi, Trevor Darrell,","arxiv_match":true}],"id":"2502.09992","metadata_source":"Cached arXiv API metadata","rank":159},{"title":"LongMemEval: Benchmarking chat assistants on long-term interactive memory","arxiv_id":"2410.10813","year":2024,"url":"https://arxiv.org/abs/2410.10813","citing_submissions":538,"explicit_arxiv_matches":218,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"u Zhou. O-Mem: Omni memory system for personalized, long horizon, self-evolving agents. arXiv preprint arXiv:2511.13593, 2025. Di Wu, Hongwei Wang, Wenhao Yu, Yuwei Zhang, Kai-Wei Chang, and Dong Yu. LongMemEval: Benchmarking chat assistants on long-term interactive memory. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum? id=pZiyCaVuti. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zh","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"ns of tokens of context, 2024. URL https://arxiv.org/abs/2403.05530. Yu Wang and Xi Chen. Mirix: Multi-agent memory system for llm-based agents. arXiv preprint arXiv:2507.07957, 2025. David Whitlock et al. Genxword: A crossword generator written in python. https://github. com/riverrun/genxword, 2022. Di Wu, Hongwei Wang, Wenhao Yu, Yuwei Zhang, Kai-Wei Chang, and Dong Yu. Longmemeval: Benchmarking chat assistants on long-term interactive memory. arXiv preprint arXiv:2410.10813, 2024. xAI. Grok 4.3 Beta. https://grok.com/release-notes/apr-17-2026, April 2026. Accessed Septemb","arxiv_match":true},{"id":"EUiwYKQQVt","title":"When External Memory Becomes Agent State: Recorded Lineage and Controlled Reuse in LLM Agents","forum_url":"https://openreview.net/forum?id=EUiwYKQQVt","excerpt":"aoFeng Wang. A-MemGuard: A proactive defense framework for LLM-based agent memory, 2025. URL https://arxiv.org/abs/2510.02373. Di Wu, Hongwei Wang, Wenhao Yu, Yuwei Zhang, Kai-Wei Chang, and Dong Yu. LongMemEval: Benchmarking chat assistants on long-term interactive memory. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum? id=pZiyCaVuti. Weiwei Xie, Shaoxiong Guo, Fan Zhang, Tian Xia, Xue Yang, Lizhuang Ma, ","arxiv_match":false}],"id":"2410.10813","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":160},{"title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","arxiv_id":"2205.14135","year":2022,"url":"https://arxiv.org/abs/2205.14135","citing_submissions":537,"explicit_arxiv_matches":87,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"reprint arXiv:2504.19413, 2025. Rewon Child. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509, 2019. Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and memoryefficient exact attention with io-awareness. Advances in neural information processing systems, 35: 16344–16359, 2022. Tri Dao, Daniel Haziza, Francisco Massa, and Grigory Sizov. Flash-decoding for longcontext inference. PyTorch Blog, October ","arxiv_match":false},{"id":"ZQOtaCagPN","title":"EviQ: Hierarchical Query-Conditioned Evidence Selection for Efficient VideoLLMs","forum_url":"https://openreview.net/forum?id=ZQOtaCagPN","excerpt":"r attention with better parallelism and work partitioning. In International Conference on Learning Representations (ICLR), 2024. Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. FlashAttention: Fast and memory-efficient exact attention with IO-awareness. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Ziyang Fan, Keyu Chen, Ruilong Xing, Yulin Li, Li Jiang, and Zhuotao Tian. Flashvid: Efficient video large language models via t","arxiv_match":false},{"id":"r9EgYOUmRo","title":"Saevia: Spatiotemporal Token Compression with Attention Compensation for Efficient Video Understanding","forum_url":"https://openreview.net/forum?id=r9EgYOUmRo","excerpt":"owards general-purpose visionlanguage models with instruction tuning. In NeurIPS, 2023. URL https://arxiv.org/ abs/2305.06500. Tri Dao. FlashAttention-2: Faster attention with better parallelism and work partitioning. In ICLR, 2024. URL https://arxiv.org/abs/2307.08691. Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. FlashAttention: Fast and memory-efficient exact attention with IO-Awareness. In NeurIPS, 2022. URL https: //arxiv.org/abs/2205.14135. Rong Fan, Kaiyan Xiao, Minghao Zhu, Liuyi Wang, Kai Dai, and Zhao Yang. GroundVTS: Visual token sa","arxiv_match":true}],"id":"2205.14135","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":161},{"title":"Musique: Multihop questions via single-hop question composition","arxiv_id":"2108.00573","year":2021,"url":"https://arxiv.org/abs/2108.00573","citing_submissions":532,"explicit_arxiv_matches":35,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":". Trace: Turn-level reward assignment via credit estimation for long-horizon agents. arXiv preprint arXiv:2607.13988, 2026. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. musique: Multihop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 10:539–554, 2022. Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving,","arxiv_match":false},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"/2511. 21689. Weiwei Sun, Miao Lu, Zhan Ling, Kang Liu, Xuesong Yao, Yiming Yang, and Jiecao Chen. Scaling long-horizon LLM agent via context-folding. arXiv preprint arXiv:2510.11967, 2025. URL https://arxiv.org/abs/2510.11967. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. MuSiQue: Multihop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 2022. URL https://arxiv.org/abs/2108.00573. Hanlin Wang, Chak Tou Leong, Jiashuo Wang, Jian Wang, and Wenjie Li. SPA-RL: Reinforcing LLM Agent","arxiv_match":true},{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":"27, 2026, pp. 19380–19388. AAAI Press, 2026. doi: 10.1609/AAAI.V40I23.39015. URL https://doi.org/10.1609/aaai.v40i23.39015. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Musique: Multihop questions via single-hop question composition. Trans. Assoc. Comput. Linguistics, 10:539– 554, 2022. doi: 10.1162/TACL\\_A\\_00475. URL https://doi.org/10.1162/tacl_a_ 00475. Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, ","arxiv_match":false}],"id":"2108.00573","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":162},{"title":"The FineWeb datasets: Decanting the web for the finest text data at scale","arxiv_id":"2406.17557","year":2024,"url":"https://arxiv.org/abs/2406.17557","citing_submissions":530,"explicit_arxiv_matches":134,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"h_E Gonzalez. Memgpt: towards llms as operating systems. 2023. Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf. The fineweb datasets: Decanting the web for the finest text data at scale. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2024. URL https://openreview.net/forum? id=n6SCkn2QaG. Bo Peng, Eric Alcaide, Quentin Anthony, ","arxiv_match":false},{"id":"iksnFm6vFW","title":"Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage","forum_url":"https://openreview.net/forum?id=iksnFm6vFW","excerpt":"ge Processing, pp. 20275–20321, Suzhou, China, November 2025. Association for Computational Linguistics. ISBN 979-8-89176-332-6. doi: 10.18653/v1/2025.emnlp-main.1025. URL https://aclanthology.org/2025.emnlp-main.1025/. Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf. The fineweb datasets: Decanting the web for the finest text data at scale, 2024. URL https://arxiv.org/abs/2406.17557. Qwen Team. Qwen3.5: Towards native multimodal agents, February 2026. URL https://qwen.ai/blog ?id=","arxiv_match":true},{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"n GPT. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Guilherme Penedo, Hynek Kydlı́ček, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, Thomas Wolf, et al. The fineweb datasets: Decanting the web for the finest text data at scale. Advances in Neural Information Processing Systems, 37:30811–30849, 2024. Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, and Rah","arxiv_match":false}],"id":"2406.17557","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":163},{"title":"AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents","arxiv_id":"2406.13352","year":2024,"url":"https://arxiv.org/abs/2406.13352","citing_submissions":528,"explicit_arxiv_matches":108,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"ing malicious propagation in multi-agent systems, 2026. URL https://arxiv.org/abs/2607.25255. Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents. In Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track, pp. 82895–82920, 2024. doi: 10.52202/079017-2636. DeepSeek. Deepseek api documentation: Model up","arxiv_match":false},{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":"21. Paula J Dalley. A theory of agency law. University of Pittsburgh law review, 72(3), 2011. Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents. In Advances in Neural Information Processing Systems, 2024. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the w","arxiv_match":false},{"id":"EUiwYKQQVt","title":"When External Memory Becomes Agent State: Recorded Lineage and Controlled Reuse in LLM Agents","forum_url":"https://openreview.net/forum?id=EUiwYKQQVt","excerpt":"ttps://arxiv.org/abs/2606.04329. Second Workshop on Agents in the Wild: Safety, Security, and Beyond (AIWILD), ICML. Edoardo Debenedetti, Jie Zhang, Mislav Balunović, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents. In Advances in Neural Information Processing Systems, volume 37, pp. 82895–82920, 2024. doi: 10.52202/079017-2636. URL https://arxiv.org/abs/2406.13352. Shen Dong, Shaochen Xu, Pengfei He, Yige Li, Jiliang Tang, Tianming Liu, Hui Liu, and Zhen Xiang. ","arxiv_match":true}],"id":"2406.13352","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":164},{"title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","arxiv_id":"2506.05176","year":2025,"url":"https://arxiv.org/abs/2506.05176","citing_submissions":527,"explicit_arxiv_matches":493,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":" agents. arXiv preprint arXiv:2605.13527, 2026a. Xiaoying Zhang, Zichen Liu, Yipeng Zhang, Xia Hu, and Wenqi Shao. Retroagent: From solving to evolving via retrospective dual intrinsic feedback. arXiv preprint arXiv:2603.08561, 2026b. Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, et al. Qwen3 embedding: Advancing text embedding and reranking through foundation models. arXiv preprint arXiv:2506.05176, 2025. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm","arxiv_match":true},{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":" Tian Gao, Werner Geyer, Chao Huang, Pin-Yu Chen, Nitesh V. Chawla, and Xiangliang Zhang. Justice or prejudice? quantifying biases in LLM-as-a-judge. In International Conference on Learning Representations, 2025. Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, Fei Huang, and Jingren Zhou. Qwen3 embedding: Advancing text embedding and reranking through foundation models. arXiv preprint arXiv:2506.05176, 2025. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Li","arxiv_match":true},{"id":"tZff3HQL01","title":"AC-FDE: Towards Accurate and Compact Fixed-Dimensional Encodings for Multi-Vector Retrieval","forum_url":"https://openreview.net/forum?id=tZff3HQL01","excerpt":"Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. [45] Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, et al. Qwen3 embedding: Advancing text embedding and reranking through foundation models. arXiv.org, 2025. doi: 10.48550/arXiv.2506.05176. A FDE Processing (R Repetitions) Repetition i = 1 Step 2: In-bucket Aggregation Step 1: Random Partitioning (SimHash 𝝓) Bucket b=3 Bucket b=2 (10) (0","arxiv_match":false}],"id":"2506.05176","metadata_source":"Cached arXiv API metadata","rank":165},{"title":"Structured denoising diffusion models in discrete state-spaces","arxiv_id":"2107.03006","year":2021,"url":"https://arxiv.org/abs/2107.03006","citing_submissions":527,"explicit_arxiv_matches":62,"examples":[{"id":"6SzEZJld4O","title":"Watermarking Continuous Diffusion Language Models","forum_url":"https://openreview.net/forum?id=6SzEZJld4O","excerpt":" Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. Structured denoising diffusion models in discrete state-spaces. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan (eds.), Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=h7-XixPCAL. Ruibo Chen, Yihan Wu,","arxiv_match":false},{"id":"UtSpCnqHk8","title":"Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving","forum_url":"https://openreview.net/forum?id=UtSpCnqHk8","excerpt":"sion language models. In International Conference on Learning Representations, volume 2025, pp. 50726–50753, 2025. Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in neural information processing systems, 34:17981–17993, 2021. Tiwei Bie, Maosong Cao, Kun Chen, Lun Du, Mingliang Gong, Zhuochen Gong, Yanmei Gu, Jiaqi Hu, Zenan Huang, Zhenzhong Lan, et ","arxiv_match":false},{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"s. In International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=tyEyYT267x. Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. Structured denoising diffusion models in discrete state-spaces. In Advances in Neural Information Processing Systems, volume 34, 2021a. URL https://proceedings.neurips.cc/paper/ 2021/hash/958c530554f78bcd8e97125b70e6973d-Abstract.html. Jacob Austin, Augustus Ode","arxiv_match":false}],"id":"2107.03006","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":166},{"title":"Webshop: Towards scalable real-world web interaction with grounded language agents","arxiv_id":"2207.01206","year":2022,"url":"https://arxiv.org/abs/2207.01206","citing_submissions":527,"explicit_arxiv_matches":77,"examples":[{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":"2018, pp. 2369–2380. Association for Computational Linguistics, 2018. doi: 10.18653/V1/D18-1259. URL https://doi.org/10.18653/v1/ d18-1259. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems","arxiv_match":false},{"id":"1UUiXNNCyo","title":"Sparse Where, Dense Within: Contrastive Privileged Action Distillation for Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=1UUiXNNCyo","excerpt":" Zhang, Haoran Luo, Zheng Lian, Zhengqi Wen, and Jianhua Tao. OPID: On-policy skill distillation for agentic reinforcement learning, 2026b. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. WebShop: Towards scalable real-world web interaction with grounded language agents, 2023. Yanfei Zhang, Xu Lin, and Chenglin Wu. StepOPSD: Step-aware online preference self-distillation for agent reinforcement learning, 2026. A Algorithm 1 summarizes CPAD training. ROLLOUT samples ","arxiv_match":false},{"id":"0XTyMGNG5R","title":"ReLay: Are We Ready for Latent Routing for Multi-Agent Systems?","forum_url":"https://openreview.net/forum?id=0XTyMGNG5R","excerpt":"en Wang, Cong Chen, Xander Xu, and Ying-Cong Chen. Streaming communication in multi-agent reasoning. arXiv preprint arXiv:2606.05158, 2026. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents. Advances in Neural Information Processing Systems, 35:20744–20757, 2022. Hancheng Ye, Zhengqi Gao, Mingyuan Ma, Qinsi Wang, Yuzhe Fu, Ming-Yu Chung, Yueqian Lin, Zhijian Liu, Jianyi Zhang, Danyang Z","arxiv_match":false}],"id":"2207.01206","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":167},{"title":"Consistency Models","arxiv_id":"2303.01469","year":2023,"url":"https://arxiv.org/abs/2303.01469","citing_submissions":526,"explicit_arxiv_matches":129,"examples":[{"id":"goQTywWWdx","title":"VestEdit: Inversion-Free Video Editing via Spatio-Temporal Transport Stabilization","forum_url":"https://openreview.net/forum?id=goQTywWWdx","excerpt":" 15932–15942, 2023. Litu Rout, Yujia Chen, Nataniel Ruiz, Constantine Caramanis, Sanjay Shakkottai, and Wen-Sheng Chu. Semantic image inversion and editing using rectified stochastic differential equations. arXiv preprint arXiv:2410.10792, 2024. Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. arXiv preprint arXiv:2303.01469, 2023. Shuyuan Tu, Qi Dai, Zihao Zhang, Sicheng Xie, Zhi-Qi Cheng, Chong Luo, Xintong Han, Zuxuan W","arxiv_match":true},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. Cheng Lu and Yang Song. Simplifying, stabilizing and scaling continuous-time consistency models. In International Conference on Learning Representations, 2025. Yanzuo Lu, Yuxi Ren, Xin Xia, Shanchuan Lin, Xing Wang, Xuefeng Xiao, Andy J. Ma, Xiaohua Xie, and Jian-Huang Lai. Adversarial distribu","arxiv_match":false},{"id":"AivYEuElOk","title":"sCM-RNA: Preference-Aligned Consistency Models for RNA Inverse Folding","forum_url":"https://openreview.net/forum?id=AivYEuElOk","excerpt":"stoph Flamm, Peter F Stadler, and Ivo L Hofacker. Viennarna package 2.0. Algorithms for molecular biology, 6 (1):26, 2011. Cheng Lu and Yang Song. Simplifying, stabilizing and scaling continuous-time consistency models. In The Thirteenth International Conference on Learning Representations, 2025. Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution ima","arxiv_match":false}],"id":"2303.01469","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":168},{"title":"Octo: An open-source generalist robot policy","arxiv_id":"2405.12213","year":2024,"url":"https://arxiv.org/abs/2405.12213","citing_submissions":523,"explicit_arxiv_matches":238,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":"Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Quan Vuong, Ted Xiao, Pannag R Sanketi, Dorsa Sadigh, Chelsea Finn, and Sergey Levine. Octo: An Open-Source Generalist Robot Policy. In Proceedings of Robotics: Science and Systems, Delft, Netherlands, July 2024. doi: 10.15607/RSS.2024.XX.090. Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman. Relay poli","arxiv_match":false},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"iufeng Song, Zhenfei Yin, Xiaohong Liu, Xihui Liu, Ruimao Zhang, and Lei Bai. Robofactory: Exploring embodied agent collaboration with compositional constraints. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10075–10085, 2025. Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al. Octo: An open-source generalist robot policy. arXiv preprint arXiv:2405.12213, 2024. Yi Ru Wang, Carter Ung, Grant Tannert, Jiafei Duan, Josephine Li, Amy Le, Rishabh Oswal, Mar","arxiv_match":true},{"id":"ylrbHy6YvK","title":"KI-VLA: Kinematics-Informed Action Decoding for Flow-Matching Robot Policies","forum_url":"https://openreview.net/forum?id=ylrbHy6YvK","excerpt":" International Conference on Learning Representations, 2025. Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Charles Xu, Jianlan Luo, Tobias Kreiman, et al. Octo: An open-source generalist robot policy. In Robotics: Science and Systems XX, 2024. doi: 10.15607/RSS.2024.XX.090. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processi","arxiv_match":false}],"id":"2405.12213","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":169},{"title":"Evaluating object hallucination in large vision-language models","arxiv_id":"2305.10355","year":2023,"url":"https://arxiv.org/abs/2305.10355","citing_submissions":521,"explicit_arxiv_matches":67,"examples":[{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"d Chunyuan Li. Llava-onevision: Easy visual task transfer, 2024. URL https://arxiv.org/abs/2408.03326. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023a. URL https:// arxiv.org/abs/2301.12597. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023b. Bin Lin, Zhenyu Tang, Yang Ye, Jinfa Huang, Junwu Zhang, Yatian Pang, Peng Jin, Munan Ning,","arxiv_match":true},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":" frozen image encoders and large language models. In International conference on machine learning, pp. 19730–19742. PMLR, 2023a. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. In Proceedings of the 2023 conference on empirical methods in natural language processing, pp. 292–305, 2023b. Haotong Lin, Sili Chen, Junhao Liew, Donny Y Chen, Zhenyu Li, Guang Shi, Jiashi Feng, a","arxiv_match":false},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"ong, and Ted Xiao. Evaluating real-world robot manipulation policies in simulation. arXiv preprint arXiv:2405.05941, 2024. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023b. URL https://openreview.net/forum?id= xozJw0kZXF. Zhiqi Li, Guo Chen, Shilong Liu, Shihao Wang, Vibashan VS, Yishen ","arxiv_match":false}],"id":"2305.10355","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":170},{"title":"Steering language models with activation engineering","arxiv_id":"2308.10248","year":2023,"url":"https://arxiv.org/abs/2308.10248","citing_submissions":521,"explicit_arxiv_matches":483,"examples":[{"id":"ZhkFWRMi9r","title":"The Task-Bearing Tail of Activation Steering","forum_url":"https://openreview.net/forum?id=ZhkFWRMi9r","excerpt":"activation composition. In Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 577–603, 2024. Magamed Taimeskhanov, Samuel Vaiter, and Damien Garreau. Towards understanding steering strength. arXiv preprint arXiv:2602.02712, 2026. Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J Vazquez, Ulisse Mini, and Monte MacDiarmid. Steering language models with activation engineering. arXiv preprint arXiv:2308.10248, 2023. Sihan Wang, Jiayi Zhao, Qingyan Cao, Hongbo Yao, and Lin Shu. Fishback: Pullback fisher geom","arxiv_match":true},{"id":"iksnFm6vFW","title":"Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage","forum_url":"https://openreview.net/forum?id=iksnFm6vFW","excerpt":", Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan. Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet. Transformer Circuits Thread, 2024. URL https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html. Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J Vazquez, Ulisse Mini, and Monte MacDiarmid. Steering language models with activation engineering. arXiv preprint arXiv:2308.10248, 2023. Chenlong Wang, Yuanning Feng, Dongping Chen, Zhaoyang Chu, Ranjay Krishna, and Tianyi Zhou. ","arxiv_match":true},{"id":"zEvJmHCOUL","title":"SteerProbe: Learning to Bypass Safety Steering in Vision–Language Models","forum_url":"https://openreview.net/forum?id=zEvJmHCOUL","excerpt":"oi: 10.18653/v1/ 2025.emnlp-main.312. Jiaxin Song, Yixu Wang, Jie Li, Xuan Tong, Rui Yu, Yan Teng, Xingjun Ma, and Yingchun Wang. JailBound: Jailbreaking internal safety boundaries of vision-language models. In Advances in Neural Information Processing Systems, volume 38, 2025. Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J. Vazquez, Ulisse Mini, and Monte MacDiarmid. Steering language models with activation engineering. arXiv preprint arXiv:2308.10248, 2023. Han Wang, Gang Wang, and Huan Zhang. Steering away from harm: An adaptive approach to defend","arxiv_match":true}],"id":"2308.10248","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":171},{"title":"LLaVA-OneVision: Easy visual task transfer","arxiv_id":"2408.03326","year":2024,"url":"https://arxiv.org/abs/2408.03326","citing_submissions":520,"explicit_arxiv_matches":367,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"Unlocking multimodal understanding across millions of tokens of context. arXiv:2403.05530, 2024. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv:2410.21276, 2024. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv:2408.03326, 2024. Geng Li, Jinglin Xu, Yunzhen Zhao, and Yuxin Peng. Dyfo: A training-free dynamic focus visua","arxiv_match":true},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"CVPR.2019.00686. Linglin Jing, Yuting Gao, Zhigang Wang, Wang Lan, Yiwen Tang, Wenhai Wang, Kaipeng Zhang, and Qingpei Guo. Evomoe: Expert evolution in mixture of experts for multimodal large language models, 2025. URL https://arxiv.org/abs/2505.23830. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer, 2024. URL https://arxiv.org/abs/2408.03326. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-tr","arxiv_match":true},{"id":"w8aZY88Jbh","title":"ReVA: A New Scene-Centric Dataset for Remote Sensing Video Question Answering","forum_url":"https://openreview.net/forum?id=w8aZY88Jbh","excerpt":"rtik Kuckreja, Muhammad Sohail Danish, Muzammal Naseer, Abhijit Das, Salman Khan, and Fahad Shahbaz Khan. Geochat: Grounded large vision-language model for remote sensing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 27831–27840, 2024. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024a. Kun Li, George Vosselman, and Michael Ying Yang. Hrvqa: A visual question answering benchma","arxiv_match":true}],"id":"2408.03326","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":172},{"title":"Fourier neural operator for parametric partial differential equations","arxiv_id":"2010.08895","year":2020,"url":"https://arxiv.org/abs/2010.08895","citing_submissions":517,"explicit_arxiv_matches":163,"examples":[{"id":"AFB7bSu4ee","title":"Gradient–Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks","forum_url":"https://openreview.net/forum?id=AFB7bSu4ee","excerpt":" for the 2d navier–stokes equations. Entropy, 24(9):1254, 2022. Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=c8P9NQVtmnO. Kaizhao Liang, Lizhang Chen, Bo Liu, and Qiang Liu. Cautious optimizers: Improving tra","arxiv_match":false},{"id":"XXNQ4STUz3","title":"CEGINO: CONTINUOUS EQUIVARIANT GEOMETRY- INFORMED NEURAL OPERATORS FOR 3D PDES","forum_url":"https://openreview.net/forum?id=XXNQ4STUz3","excerpt":"ns. Advances in Neural Information Processing Systems, 33:6755–6766, 2020. Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. In International Conference on Learning Representations, 2021. URL https: //openreview.net/forum?id=c8P9NQVtmnO. Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, and Anima Anandkumar. Fourier neural o","arxiv_match":false},{"id":"Nvi697GmO0","title":"More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting","forum_url":"https://openreview.net/forum?id=Nvi697GmO0","excerpt":"ion: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data, 17(1):1–21, 2022. doi: 10.1145/3532611. URL https://dl.acm.org/doi/pdf/10.1145/3532611. Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895, 2020. doi: 10.48550/arxiv.2010.08895. URL https://arxiv.org/abs/2010.08895. Zongyi Li, Nikola Kovachki, Chris Choy, Boyi Li, Jean Kossaifi, Shourya Otta, Mohammad Amin Nabian","arxiv_match":true}],"id":"2010.08895","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":173},{"title":"Film: Visual reasoning with a general conditioning layer","arxiv_id":"1709.07871","year":2017,"url":"https://arxiv.org/abs/1709.07871","citing_submissions":514,"explicit_arxiv_matches":48,"examples":[{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"s with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4195–4205, 2023. Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville. FiLM: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018. Peijia Qin and Jianguo Zhang. Learning to quantize for training vector-quantized networks. In Proceedings of the International","arxiv_match":false},{"id":"bMGDZqIh5O","title":"CoRF: Cross-Scene RF Synthesis by Learning Propagation and Preserving Array Physics","forum_url":"https://openreview.net/forum?id=bMGDZqIh5O","excerpt":"rrays for Positioning with 5G Networks. IEEE Transactions on Microwave Theory and Techniques, 71(10):4600–4613, 2023. Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville. FiLM: Visual Reasoning with a General Conditioning Layer. In AAAI Conference on Artificial Intelligence (AAAI), 2018. Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lučić, Daniel Duckworth, Ale","arxiv_match":false},{"id":"mlS6HwwCm6","title":"SCOPE: Spectral-Conditioned Omic-Pathology Encoder for Interpretable Multimodal Cancer Survival Analysis","forum_url":"https://openreview.net/forum?id=mlS6HwwCm6","excerpt":"ng at single nucleotide resolution. In Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS), 2023. Ethan Perez, Florian Strub, Harm de Vries, and et al. Film: visual reasoning with a general conditioning layer. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI) and Thirtieth Innovative Applications of Artificial Intelligence Conference (IAAI) and Eighth AAAI Symposium on ","arxiv_match":false}],"id":"1709.07871","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":174},{"title":"Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution","arxiv_id":"2409.12191","year":2024,"url":"https://arxiv.org/abs/2409.12191","citing_submissions":513,"explicit_arxiv_matches":479,"examples":[{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"atial reasoning for vision language models. In The Thirty-Eighth Annual Conference on Neural Information Processing Systems, 2024a. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024b. Tongzhou Wang and Phillip Isola. Understanding contrastive representation learning through ","arxiv_match":true},{"id":"ZQOtaCagPN","title":"EviQ: Hierarchical Query-Conditioned Evidence Selection for Efficient VideoLLMs","forum_url":"https://openreview.net/forum?id=ZQOtaCagPN","excerpt":", 2025. Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang. Dycoke: Dynamic compression of tokens for fast video large language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 18992–19001, 2025. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-context","arxiv_match":true},{"id":"RvjzmvFBuQ","title":"Geometry-Aware Memory Coordination for Spatial Understanding from Long Videos","forum_url":"https://openreview.net/forum?id=RvjzmvFBuQ","excerpt":"anyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 5294–5306, 2025b. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. Diankun Wu, Fangfu Liu, Yi-Hsin Hung, and Yueqi Duan. Spatial-MLLM: Boosting MLLM capabiliti","arxiv_match":true}],"id":"2409.12191","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":175},{"title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","arxiv_id":"2504.10479","year":2025,"url":"https://arxiv.org/abs/2504.10479","citing_submissions":511,"explicit_arxiv_matches":478,"examples":[{"id":"w8aZY88Jbh","title":"ReVA: A New Scene-Centric Dataset for Remote Sensing Video Question Answering","forum_url":"https://openreview.net/forum?id=w8aZY88Jbh","excerpt":"nsing, 60:1–14, 2021. Hongjie Zhou, Shiqin Wang, Haoyang Chen, Haonan Guo, Di Wang, Juhua Liu, Fu Lin, and Yong Luo. Rsvideo: Are your vision-language models ready for remote sensing videos? arXiv preprint arXiv:2608.02039, 2026. Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025. Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Heng Fan, Qinghua Hu, and Haibin Ling. Detect","arxiv_match":true},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"n (ECCV), pp. 803–818, 2018a. Luowei Zhou, Chenliang Xu, and Jason Corso. Towards automatic learning of procedures from web instructional videos. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018b. Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025. Linchao Zhu and Yi Yang. Actbert: Learning global-local video-text representations. In Proce","arxiv_match":true},{"id":"EZ7FN3iHms","title":"ConfluxCount: Forming and Enumerating Semantic Confluences for Text-Guided Object Counting","forum_url":"https://openreview.net/forum?id=EZ7FN3iHms","excerpt":"u Guo, Zheng Wang, Xian Zhong, and Shengfeng He. Zero-Shot Object Counting With Good Exemplars. In Eur. Conf. Comput. Vis., pp. 368–385, 2024. doi: 10.1007/978-3-031-72652-1_22. Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, et al. InternVL3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025. URL https: //arxiv.org/abs/2504.10479. A E XPERIMENTAL P ROTOCOL AND R EPRODUCIBILITY A.1 E VALUATION P ROTOCOL Benchmark prompt interfac","arxiv_match":true}],"id":"2504.10479","metadata_source":"Cached arXiv API metadata","rank":176},{"title":"itransformer: Inverted transformers are effective for time series forecasting","arxiv_id":"2310.06625","year":2023,"url":"https://arxiv.org/abs/2310.06625","citing_submissions":511,"explicit_arxiv_matches":72,"examples":[{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"onal Conference on Advances in Geographic Information Systems, pp. 173–185, 2025. doi: 10.1145/3748636.3762721. Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. itransformer: Inverted transformers are effective for time series forecasting. In Proceedings of the International Conference on Learning Representations, 2024. URL https://openreview. net/forum?id=JePfAI8fah. Ari Morcos, Maithra Raghu, and Samy Bengio. Insights on representat","arxiv_match":false},{"id":"qpcwuOWDCv","title":"TimeSelect: Variable-Wise Historical Context Selection for Time Series Forecasting","forum_url":"https://openreview.net/forum?id=qpcwuOWDCv","excerpt":"with sample convolution and interaction. Advances in neural information processing systems, 35:5816–5828, 2022. Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. iTransformer: Inverted transformers are effective for time series forecasting. In International Conference on Learning Representations, 2024a. Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. itransformer: Inverted transformers are effect","arxiv_match":false},{"id":"shAJwVMuKt","title":"SHIFT: Progressive Feedback for Time Series Forecasting","forum_url":"https://openreview.net/forum?id=shAJwVMuKt","excerpt":"s forecasting via local cross-variate modeling. In International Conference on Learning Representations, 2026b. Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. iTransformer: Inverted transformers are effective for time series forecasting. In International Conference on Learning Representations, 2024. Ziqi Liu. FreqMoE: Enhancing time series forecasting through frequency decomposition mixture of experts. In Proceedings of the 28th Int","arxiv_match":false}],"id":"2310.06625","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":177},{"title":"Training compute-optimal large language models","arxiv_id":"2203.15556","year":2022,"url":"https://arxiv.org/abs/2203.15556","citing_submissions":510,"explicit_arxiv_matches":322,"examples":[{"id":"xLkfjG1wVy","title":"Efficient Long-Horizon Learning for Learned Optimization","forum_url":"https://openreview.net/forum?id=xLkfjG1wVy","excerpt":"p. 3223–3230, 2018. [12] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. Training compute-optimal large language models, 2022. URL https://arxiv.org/ abs/2203.15556. [13] Keller Jordan, Yuchen Jin, Vlado Boza, Jiacheng You, Franz Cesista, Laker Newhouse, and Jerem","arxiv_match":true},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Michael Hanna and Emmanuel Ameisen. Latent planning emerges with scale. arXiv preprint arXiv:2604.12493, 2026. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022. Hugging Face H4. Aime 2024. Hugging Face Dataset, 2025. URL https://huggingface. co/datasets","arxiv_match":true},{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"idan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. Training compute-optimal large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems, pp. 30016– 30030, 2022. URL https://proceedings.neurips.cc/paper_files/paper/ 2022/file/c1e2faff6f588870","arxiv_match":false}],"id":"2203.15556","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":178},{"title":"Is your code generated by ChatGPT really correct? Rigorous evaluation of large language models for code generation","arxiv_id":"2305.01210","year":2023,"url":"https://arxiv.org/abs/2305.01210","citing_submissions":507,"explicit_arxiv_matches":82,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"ed training of LLMs. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=ymHDVBwmta. Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and LINGMING ZHANG. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 21558–21572. Curran Associates, Inc., 2023. doi: 10.5","arxiv_match":false},{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"visual instruction tuning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 26296–26306, 2024. Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. Advances in neural information processing systems, 36:21558–21572, 2023. Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan.","arxiv_match":false},{"id":"sFUbqvhx3m","title":"SyntaxBPE: Learning Which Code Boundaries to Cross","forum_url":"https://openreview.net/forum?id=sFUbqvhx3m","excerpt":"hoi. SuperBPE: Space travel for language models. In Second Conference on Language Modeling, 2025. URL https://arxiv.org/abs/2503.13423. Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. Is your code generated by ChatGPT really correct? Rigorous evaluation of large language models for code generation. In Advances in Neural Information Processing Systems, volume 36, pp. 21558–21572, 2023. Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmyt","arxiv_match":false}],"id":"2305.01210","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":179},{"title":"Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension","arxiv_id":"1705.03551","year":2017,"url":"https://arxiv.org/abs/1705.03551","citing_submissions":507,"explicit_arxiv_matches":67,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"lmlingua: Accelerating and enhancing llms in long context scenarios via prompt compression, 2024. URL https://arxiv.org/abs/2310.06839. Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1601– 1611, 2017. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and Fra","arxiv_match":false},{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":"ypothesis-tree refinement. CoRR, abs/2606.11926, 2026. doi: 10.48550/ARXIV.2606.11926. URL https: //doi.org/10.48550/arXiv.2606.11926. Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In Regina Barzilay and Min-Yen Kan (eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Pa","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"ence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR, 2017. Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1601– 1611, 2017. K2.5. Kimi k2.5: Visual agentic intelligence. arXiv preprint","arxiv_match":false}],"id":"1705.03551","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":180},{"title":"SAM 2: Segment anything in images and videos","arxiv_id":"2408.00714","year":2024,"url":"https://arxiv.org/abs/2408.00714","citing_submissions":506,"explicit_arxiv_matches":177,"examples":[{"id":"xb6eQlQ8CB","title":"Light SAM3 Agent: Efficient Agentic Segmentation via Latent NPs Extraction and Action Space Slimming","forum_url":"https://openreview.net/forum?id=xb6eQlQ8CB","excerpt":"ognition (CVPR), pp. 13009–13018. IEEE, 2024. Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. In International Conference on Learning Representations, volume 2025, pp. 28085– 28128, 2025. Yiqing Shen, Chenjia Li, Fei Xiong, Jeong-O Jeong, Tianpeng Wang, Michael Latman, and Mathias Unberath. ","arxiv_match":false},{"id":"e4gvA0ZGY5","title":"ReasonRSOS: Reasoning Salient Object Segmentation in Remote Sensing Images","forum_url":"https://openreview.net/forum?id=e4gvA0ZGY5","excerpt":" Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos, 2024. URL https://arxiv. org/abs/2408.00714. Zhongwei Ren, Zhicheng Huang, Yunchao Wei, Yao Zhao, Dongmei Fu, Jiashi Feng, and Xiaojie Jin. Pixellm: Pixel reasoning with large multimodal model, 2024","arxiv_match":false},{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":"priors. arXiv preprint arXiv:2306.17843, 2023. Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. In International Conference on Learning Representations, volume 2025, pp. 28085–28128, 2025. Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ We","arxiv_match":false}],"id":"2408.00714","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":181},{"title":"CogVideoX: Text-to-video diffusion models with an expert transformer","arxiv_id":"2408.06072","year":2024,"url":"https://arxiv.org/abs/2408.06072","citing_submissions":505,"explicit_arxiv_matches":195,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"i, Yilong Zhao, Muyang Li, Jintao Zhang, Han Cai, Yujun Lin, Xiuyu Li, Chenfeng Xu, Kelly Peng, et al. Sparse videogen2: Accelerate video generation with sparse attention via semantic-aware permutation. arXiv preprint arXiv:2505.18875, 2025. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024. Jintao Zhang, Jia Wei, Haofeng Huang, Pengle Zhang, Jun Zhu, and Jianfei Chen. Sageattention","arxiv_match":true},{"id":"goQTywWWdx","title":"VestEdit: Inversion-Free Video Editing via Spatio-Temporal Transport Stabilization","forum_url":"https://openreview.net/forum?id=goQTywWWdx","excerpt":"Conference Papers, pp. 1–11, 2023. Xiangpeng Yang, Linchao Zhu, Hehe Fan, and Yi Yang. Videograin: Modulating space-time attention for multi-grained video editing. In The Thirteenth International Conference on Learning Representations, 2025. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024. Danah Yatim, Rafail Fridman, Omer Bar-Tal, Yoni Kasten, and Tali Dekel. Space-time diffusion","arxiv_match":true},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":":2502.15681, 2025. Shuai Yang, Wei Huang, Ruihang Chu, Yicheng Xiao, Yuyang Zhao, Xianbang Wang, Muyang Li, Enze Xie, Yingcong Chen, Yao Lu, et al. Longlive: Real-time interactive long video generation. arXiv preprint arXiv:2509.22622, 2025. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024. Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and ","arxiv_match":true}],"id":"2408.06072","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":182},{"title":"Video-MME: The first-ever comprehensive evaluation benchmark of multi-modal LLMs in video analysis","arxiv_id":"2405.21075","year":2024,"url":"https://arxiv.org/abs/2405.21075","citing_submissions":502,"explicit_arxiv_matches":81,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":" Video-r1: Reinforcing video reasoning in mllms. In NeurIPS, 2025. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. In CVPR, 2025. Gemini Team Google. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv:2403.05530, 2024. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman","arxiv_match":false},{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"2024. URL https://arxiv. org/abs/2406.14515. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Caifeng Shan, Ran He, and Xing Sun. Video-MME: The first-ever comprehensive evaluation benchmark of multi-modal LLMs in video analysis. arXiv preprint arXiv:2405.21075, 2024. URL https://arxiv.org/abs/2405.21075. Gemini Robotics Team. Gemini robotics: Bringing AI into the physical world. arXiv preprint arXiv:2","arxiv_match":true},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":" international conference on computer vision, pp. 6202–6211, 2019. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 24108–24118, 2025. Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Li","arxiv_match":false}],"id":"2405.21075","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":183},{"title":"Evaluating very long-term conversational memory of LLM agents","arxiv_id":"2402.17753","year":2024,"url":"https://arxiv.org/abs/2402.17753","citing_submissions":501,"explicit_arxiv_matches":73,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"ang He. MemPro: Agentic memory systems as evolvable programs. arXiv preprint arXiv:2606.00619, 2026c. Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. Evaluating very long-term conversational memory of LLM agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, pp. 13851–13870, 2024. NVIDIA. NVIDIA A100 Tensor Core GPU. https://www.nvidia.com/en-us/ data-center/a100","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"ities. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 1160–1183, 2025. Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. Evaluating very long-term conversational memory of llm agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13851–13870, 2024. OpenAI. Introducing GPT-5.2. https://openai.com/index/ intr","arxiv_match":false},{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":"2020. Lingni Ma et al. Nymeria: A massive collection of multimodal egocentric daily motion in the wild. In European Conference on Computer Vision, pp. 445–465. Springer, 2024. Adyasha Maharana et al. Evaluating very long-term conversational memory of llm agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13851–13870, 2024. Karttikeya Mangalam et al. Egoschema: A diagnostic benchmar","arxiv_match":false}],"id":"2402.17753","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":184},{"title":"Neural ordinary differential equations","arxiv_id":"1806.07366","year":2018,"url":"https://arxiv.org/abs/1806.07366","citing_submissions":501,"explicit_arxiv_matches":34,"examples":[{"id":"XXNQ4STUz3","title":"CEGINO: CONTINUOUS EQUIVARIANT GEOMETRY- INFORMED NEURAL OPERATORS FOR 3D PDES","forum_url":"https://openreview.net/forum?id=XXNQ4STUz3","excerpt":"Shuhao Cao. Choose a transformer: Fourier or Galerkin. Advances in Neural Information Processing Systems, 34:24924–24940, 2021. Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary differential equations. Advances in Neural Information Processing Systems, 31, 2018. Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. SE(3)-transformers: 3D rototranslation equivariant attention networks. Adv","arxiv_match":false},{"id":"jJbc2wpXoo","title":"Scaling Online Optimal Transport Minibatches for Flow Matching","forum_url":"https://openreview.net/forum?id=jJbc2wpXoo","excerpt":"g Zhang. SICNN: Sparsity-induced input convex neural network for optimal transport. In Optimization for Machine Learning, 2024. Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary differential equations. In NeurIPS, 2018. Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. In NeurIPS, 2013. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A ","arxiv_match":false},{"id":"EmlATySoKy","title":"Falsify, Repair, Repeat: Auditable Local Repair of Typed Dynamical Equations","forum_url":"https://openreview.net/forum?id=EmlATySoKy","excerpt":"symbolic regression for catalytic organic reaction kinetics. In International Conference on Learning Representations, 2025. Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K. Duvenaud. Neural ordinary differential equations. In Advances in Neural Information Processing Systems, volume 31, 2018. Miles Cranmer. Interpretable machine learning for science with PySR and SymbolicRegression.jl. arXiv preprint arXiv:2305.01582,","arxiv_match":false}],"id":"1806.07366","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":185},{"title":"Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces","arxiv_id":"2601.11868","year":2026,"url":"https://arxiv.org/abs/2601.11868","citing_submissions":500,"explicit_arxiv_matches":237,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"Yang, Nabil Omi, Negin Raoof, Shanda Li, Terry Yue Zhuo, Wuwei Lin, Yiwei Dai, Yuxin Wang, Wenhao Chai, Shang Zhou, Dariush Wahdany, Ziyu She, Jiaming Hu, Zhikang Dong, Yuxuan Zhu, Sasha Cui, Ahson Saiyed, Arinbjörn Kolbeinsson, Jesse Hu, Christopher Michael Rytting, Ryan Marten, Yixin Wang, Alex Dimakis, Andy Konwinski, and Ludwig Schmidt. Terminalbench: Benchmarking agents on hard, realistic tasks in command line interfaces, 2026. URL https://arxiv.org/abs/2601.11868. Jiayi Pan, Yichi Zhang, Nicholas Tomlin, Yifei Zhou, Sergey Levine, and Alane Suhr. Autonomous eva","arxiv_match":true},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"ee evolution. arXiv preprint arXiv:2604.07725, 2026. Mike Merrill, Alexander Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Shin, Thomas Walshe, E Kelly Buchanan, et al. Terminal-bench: Benchmarking agents on hard, realistic tasks in command line interfaces. In International Conference on Learning Representations, volume 2026, pp. 40903–40986, 2026. H Penny Nii. Part one: The blackboard model of problem solving and the evolution of blackboard architectu","arxiv_match":false},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"iaming Hu, Zhikang Dong, Yuxuan Zhu, Sasha Cui, Ahson Saiyed, Arinbjörn Kolbeinsson, Jesse Hu, Christopher Michael Rytting, Ryan Marten, Yixin Wang, Alex Dimakis, Andy Konwinski, and Ludwig Schmidt. Terminal-bench: Benchmarking agents on hard, realistic tasks in command line interfaces. CoRR, abs/2601.11868, 2026. doi: 10.48550/ARXIV.2601.11868. URL https://doi.org/10. 48550/arXiv.2601.11868. Alexander Novikov, Ngân Vu, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt ","arxiv_match":false}],"id":"2601.11868","metadata_source":"Cached arXiv API metadata","rank":186},{"title":"Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success","arxiv_id":"2502.19645","year":2025,"url":"https://arxiv.org/abs/2502.19645","citing_submissions":498,"explicit_arxiv_matches":321,"examples":[{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"nce on empirical methods in natural language processing (EMNLP), pp. 787–798, 2014. Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. OpenVLA: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246, 2024. Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success. arXiv preprint arXiv:2502.19645, 2025. Fuhao Li, Wenxuan Song, Han Zhao, Jingbo Wang, Pengxiang Ding, Donglin Wang, Long Zeng, and ","arxiv_match":true},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"kiadaki. 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations. In Proceedings of The 8th Conference on Robot Learning, volume 270 of Proceedings of Machine Learning Research, pp. 1949–1974. PMLR, 2025. URL https://proceedings.mlr.press/v270/ke25a.html. Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success. In Proceedings of Robotics: Science and Systems, 2025. URL https://arxiv.org/abs/2502.19645. Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin, Yen-Chen Lin, Yunhao Ge, Grace Lam, Percy Liang, Shuran Son","arxiv_match":true},{"id":"fKq8qGzBwD","title":"Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning","forum_url":"https://openreview.net/forum?id=fKq8qGzBwD","excerpt":"benchmark for imitation learning with human demonstrations. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=6pPYRXKPpw. Alex Kendall and Yarin Gal. What uncertainties do we need in bayesian deep learning for computer vision?, 2017. URL https://arxiv.org/abs/1703.04977. Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success. arXiv preprint arXiv:2502.19645, 2025. Kenneth L. Lange, Roderick J. A. Little, and Jeremy M. G. Taylor. Robust statistical modelin","arxiv_match":true}],"id":"2502.19645","metadata_source":"Cached arXiv API metadata","rank":187},{"title":"STaR: Bootstrapping reasoning with reasoning","arxiv_id":"2203.14465","year":2022,"url":"https://arxiv.org/abs/2203.14465","citing_submissions":496,"explicit_arxiv_matches":109,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"ntion. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 23078–23097, 2025. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. Star: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems, 35:15476–15488, 2022. Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen. Repocoder: Re","arxiv_match":false},{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"el communication. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023. https://aclanthology.org/2023.emnlp-main.936/. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. STaR: Bootstrapping reasoning with reasoning. In Conference on Neural Information Processing Systems (NeurIPS), 2022. https://proceedings.neurips.cc/paper_files/paper/2022/hash/ 639a9a172c044fbb64175b5fad42e9a5-Abstract-Conference.html. Aohan Z","arxiv_match":false},{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"ngyin Du, and Jiecao Chen. Agent-r: Training language model agents to reflect via iterative self-training. arXiv preprint arXiv:2501.11425, 2025. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. Star: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems, 35:15476–15488, 2022. Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B Tenenbaum, Tianmin Shu, and Chuang Gan. Building coo","arxiv_match":false}],"id":"2203.14465","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":188},{"title":"Inferencetime intervention: Eliciting truthful answers from a language model","arxiv_id":"2306.03341","year":2023,"url":"https://arxiv.org/abs/2306.03341","citing_submissions":491,"explicit_arxiv_matches":62,"examples":[{"id":"ZhkFWRMi9r","title":"The Task-Bearing Tail of Activation Steering","forum_url":"https://openreview.net/forum?id=ZhkFWRMi9r","excerpt":"instruction following of llms via activation steering with dynamic rejection. arXiv preprint arXiv:2603.06745, 2026. Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Inference-time intervention: Eliciting truthful answers from a language model. Advances in neural information processing systems, 36:41451–41530, 2023. Varvara Logacheva, Daryna Dementieva, Sergey Ustyantsev, Daniil Moskovskiy, David Dale, Irina Krotova, Nikita Semenov, and Al","arxiv_match":false},{"id":"x6oIXJYZ7g","title":"LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs","forum_url":"https://openreview.net/forum?id=x6oIXJYZ7g","excerpt":"challenge. Transactions of the Association for Computational Linguistics, 6:317–328, 2018. doi: 10.1162/tacl_a_00023. Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Inference-time intervention: Eliciting truthful answers from a language model. In Advances in Neural Information Processing Systems, volume 36, pp. 41451–41530, 2023. URL https://openreview.net/ forum?id=aLLuYpn83y. Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, ","arxiv_match":false},{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"e your agent security defenses deployable in real-world dynamic environments? arXiv preprint arXiv:2602.03117, 2026a. Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Inference-time intervention: Eliciting truthful answers from a language model. NeurIPS, 2023. Yanhang Li, Zhichao Fan, and Zexin Zhuang. When AUC 0.998 is not enough: A candidate evaluation protocol for hidden-state probes of indirect prompt injection in multimodal computer-us","arxiv_match":false}],"id":"2306.03341","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":189},{"title":"MemoryBank: Enhancing large language models with long-term memory","arxiv_id":"2305.10250","year":2023,"url":"https://arxiv.org/abs/2305.10250","citing_submissions":491,"explicit_arxiv_matches":64,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":" and action for personalized LLM agents. In Findings of the Association for Computational Linguistics: ACL 2026, pp. 26421–26439, 2026b. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. MemoryBank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19724–19731, 2024. Yue Zhou, Xiaobo Guo, Belhassen Bayar, and Srinivasan H. Sengamedu. Amory: Building coherent narra","arxiv_match":false},{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"lm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pp. 19724–19731, 2024. A I MPLEMENTATION AND E VALUATION P ROTOCOLS This appendix details the benchmark adaptations and e","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"prompting with memory for computer control. In International Conference on Learning Representations, volume 2024, pp. 19036–19066, 2024. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pp. 19724–19731, 2024. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. Language agent tre","arxiv_match":false}],"id":"2305.10250","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":190},{"title":"LongBench: A bilingual, multitask benchmark for long context understanding","arxiv_id":"2308.14508","year":2023,"url":"https://arxiv.org/abs/2308.14508","citing_submissions":488,"explicit_arxiv_matches":63,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":" et al. Phi-4 technical report. arXiv preprint arXiv:2412.08905, 2024. Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 3119–3137, 2024. Zefan Cai, Yichi Zhang, Bofei Gao, Yuliang Liu, Yucheng Li, T","arxiv_match":false},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":" Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 3119–3137, 2024. URL https: //aclanthology.org/2024.acl-long.172/. Yushi Bai, ","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"e on Machine Learning, pp. 22–31, 2017. Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. LongBench: A bilingual, multitask benchmark for long context understanding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3119–3137, 2024. Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang","arxiv_match":false}],"id":"2308.14508","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":191},{"title":"Learning latent dynamics for planning from pixels","arxiv_id":"1811.04551","year":2018,"url":"https://arxiv.org/abs/1811.04551","citing_submissions":486,"explicit_arxiv_matches":47,"examples":[{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"3-5/. David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018. Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. Learning latent dynamics for planning from pixels. In Proceedings of the 36th International Conference on Machine Learning, volume 97, pp. 2555–2565. PMLR, 2019. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse co","arxiv_match":false},{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"eural Information Processing Systems, volume 32, 2019. URL https://arxiv.org/abs/1906.05253. Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. Learning latent dynamics for planning from pixels. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 2555–2565. PMLR, 2019. URL https://proceedings.mlr.press/v97/hafne","arxiv_match":false},{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":". World models. arXiv preprint arXiv:1803.10122, 2018. URL https://arxiv.org/abs/1803.10122. Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. Learning latent dynamics for planning from pixels. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 2555–2565. PMLR, 2019. URL https://proceedings.mlr.press/v97/hafne","arxiv_match":false}],"id":"1811.04551","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":192},{"title":"Refusal in language models is mediated by a single direction","arxiv_id":"2406.11717","year":2024,"url":"https://arxiv.org/abs/2406.11717","citing_submissions":485,"explicit_arxiv_matches":90,"examples":[{"id":"y01WnZj2L0","title":"Seeing More, Refusing Less: Two Mechanisms of Safety Erosion in Video Large Language Models","forum_url":"https://openreview.net/forum?id=y01WnZj2L0","excerpt":"g, Cheng Yuan, et al. Ai flow: Perspectives, scenarios, and approaches. Vicinagearth, 3(1):1, 2026. Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. Advances in Neural Information Processing Systems, 37:136037–136083, 2024. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang G","arxiv_match":false},{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":" Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. Advances in Neural Information Processing Systems, 37:136037–136083, 2024. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mir","arxiv_match":false},{"id":"zEvJmHCOUL","title":"SteerProbe: Learning to Bypass Safety Steering in Vision–Language Models","forum_url":"https://openreview.net/forum?id=zEvJmHCOUL","excerpt":" Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. In Advances in Neural Information Processing Systems, volume 37, 2024. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, e","arxiv_match":false}],"id":"2406.11717","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":193},{"title":"Language Models (Mostly) Know What They Know","arxiv_id":"2207.05221","year":2022,"url":"https://arxiv.org/abs/2207.05221","citing_submissions":484,"explicit_arxiv_matches":451,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"a88fb84-Paper-Conference.pdf. Jaehun Jung, Faeze Brahman, and Yejin Choi. Trust or escalate: Llm judges with provable guarantees for human agreement. In International Conference on Learning Representations, volume 2025, pp. 3101–3125, 2025. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221, 2022. Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham","arxiv_match":true},{"id":"r2ccgSQzyH","title":"Supervising the Visible State: Separating Representation from View Selection in Streaming Video","forum_url":"https://openreview.net/forum?id=r2ccgSQzyH","excerpt":"Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221, 2022. Amita Kamath, Robin Jia, and Percy Liang. Selective question answering under domain shift. I","arxiv_match":true},{"id":"hlquBnFSq0","title":"A Multidimensional View of LLM Uncertainty Quantification via Scenario Interventions","forum_url":"https://openreview.net/forum?id=hlquBnFSq0","excerpt":"f the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1601–1611, Vancouver, Canada, 2017. Association for Computational Linguistics. doi: 10.18653/v1/P17-1147. URL https://aclanthology.org/P17-1147/. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. Language models (mostly) know what they know, 2022. URL https://arxiv.org/abs/2207.05221. Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth A. Malik, and Yarin Gal. Semanti","arxiv_match":true}],"id":"2207.05221","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":194},{"title":"Improving factuality and reasoning in language models through multiagent debate","arxiv_id":"2305.14325","year":2023,"url":"https://arxiv.org/abs/2305.14325","citing_submissions":482,"explicit_arxiv_matches":145,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"ates. API documentation, 2026. URL https:// api-docs.deepseek.com/. Documents the deepseek-v4-flash deployment; accessed August 20, 2026. Liming Dong, Qinghua Lu, and Liming Zhu. AgentOps: Enabling observability of LLM agents, 2024. URL https://arxiv.org/abs/2411.05285. Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate, 2023. URL https: //arxiv.org/abs/2305.14325. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. ","arxiv_match":true},{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"d. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018. Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. In Forty-first International Conference on Machine Learning, 2024. Andrew Estornell and Yang Liu. Multi-llm debate: Framework, principals, and interventions. Advances in Neural Information Processin","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"ikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. DeepSeek-AI. Deepseek harness: Everything is a plugin. deepseek-ai/deepseek-harness, 2026. https://github.com/ Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325, 2023. Sicheng Feng, Song Wang, Shuyi Ouyang, Lingdong Kong, Zikai Song, Jianke Zhu, Huan Wang, and","arxiv_match":true}],"id":"2305.14325","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":195},{"title":"OlympiadBench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems","arxiv_id":"2402.14008","year":2024,"url":"https://arxiv.org/abs/2402.14008","citing_submissions":481,"explicit_arxiv_matches":106,"examples":[{"id":"BTTuPGcoJL","title":"Learning from Correctness Trends: Sample-Wise Historical Advantage Shaping for Mathematical Reasoning","forum_url":"https://openreview.net/forum?id=BTTuPGcoJL","excerpt":" llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, et al. Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3828–3850, 2024. Xixiang He, Qiyao Sun, Ao Cheng, Xingming Li, Xuanyu Ji, Hail","arxiv_match":false},{"id":"UcFbcNhyQO","title":"PhysAlign: A Benchmark for Evidence-Grounded Role Alignment in Multimodal Physics Reasoning","forum_url":"https://openreview.net/forum?id=UcFbcNhyQO","excerpt":"uter Vision and Pattern Recognition (CVPR), pp. 14375–14385. IEEE, 2024. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, et al. Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3828–3850, 2024. Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kallio","arxiv_match":false},{"id":"1TfY9hAQoJ","title":"Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening","forum_url":"https://openreview.net/forum?id=1TfY9hAQoJ","excerpt":"redit assignment in RL for large language models. In Advances in Neural Information Processing Systems, 2025. URL https://arxiv.org/abs/2505.23564. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, et al. OlympiadBench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems. arXiv preprint arXiv:2402.14008, 2024. URL https://arxiv.org/abs/2402.14008. Haonan He, Haodi Lei, Yun Luo, Haoran Zhang, Shunkai Zhang, Yizhuo Li, Shengji Tang, Zhilin Wang, ","arxiv_match":true}],"id":"2402.14008","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":196},{"title":"Awq: Activation-aware weight quantization for llm compression and acceleration","arxiv_id":"2306.00978","year":2023,"url":"https://arxiv.org/abs/2306.00978","citing_submissions":479,"explicit_arxiv_matches":71,"examples":[{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"nd Pattern Recognition, pp. 1909–1918, 2020. Hyunseung Lee, Jihoon Hong, Soosung Kim, Seung Yul Lee, and Jae W Lee. A memory-efficient edge inference accelerator with xor-based model compression. In 2023 60th ACM/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE, 2023. J Lin, J Tang, H Tang, S Yang, WM Chen, WC Wang, G Xiao, X Dang, C Gan, and S Han. Awq: Activation-aware weight quantization for llm compression and acceleration. arxiv 2024. arXiv preprint arXiv:2306.00978, 2024. Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnam","arxiv_match":true},{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"on by block reconstruction. arXiv preprint arXiv:2102.05426, 2021. Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. AWQ: activation-aware weight quantization for on-device LLM compression and acceleration. In Proceedings of the Seventh Annual Conference on Machine Learning and Systems, MLSys 2024, Santa Clara, CA, USA, May 13-16, 2024. mlsys.org, 2024. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Ma","arxiv_match":false},{"id":"moFf9vJeAN","title":"WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning","forum_url":"https://openreview.net/forum?id=moFf9vJeAN","excerpt":"e Language Models. https://arxiv.org/abs/2506.11116v1, June 2025b. Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration. Proceedings of Machine Learning and Systems, 6:87–100, May 2024. James Liu, Pragaash Ponnusamy, Tianle Cai, Han Guo, Yoon Kim, and Ben Athiwaratkun. TrainingFree Activation Sparsity in Large Languag","arxiv_match":false}],"id":"2306.00978","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":197},{"title":"SGLang: Efficient execution of structured language model programs","arxiv_id":"2312.07104","year":2023,"url":"https://arxiv.org/abs/2312.07104","citing_submissions":478,"explicit_arxiv_matches":71,"examples":[{"id":"e9RG7RBDSX","title":"Cadence: Regulating the Speculation Externality in LLM Serving","forum_url":"https://openreview.net/forum?id=e9RG7RBDSX","excerpt":"pear. arXiv:2510.22876. Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng. SGLang: Efficient execution of structured language model programs. In Advances in Neural Information Processing Systems, 2024. Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, and Hao Zhang. DistServe: Disaggregating prefill and dec","arxiv_match":false},{"id":"x9qK9ehqNi","title":"Coordinated Mixed-Precision KV Cache Quantization for Efficient LLM-based Multi-agent Systems","forum_url":"https://openreview.net/forum?id=x9qK9ehqNi","excerpt":"ormation Processing Systems, 36:34661–34710, 2023. Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody H Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E Gonzalez, et al. Sglang: Efficient execution of structured language model programs. Advances in neural information processing systems, 37:62557–62583, 2024. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber. Gptswarm: Language a","arxiv_match":false},{"id":"moFf9vJeAN","title":"WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning","forum_url":"https://openreview.net/forum?id=moFf9vJeAN","excerpt":", Fanghua Ye, Yingqi Fan, Junlong Tong, Jing Xiong, Zhiwei Fei, Hui Su, and Xiaoyu Shen. SkipGPT: Each Token is One of a Kind. In Forty-Second International Conference on Machine Learning, June 2025. Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng. SGLang: Efficient Execution of Structured Language Model Programs. http://arxiv.org/abs/2312.07104, June 2024. Longguang Zhong, Fanqi Wan, Ruijun Chen, Xiaojun Quan, and Liangzhi Li. BlockPruner: Fi","arxiv_match":true}],"id":"2312.07104","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":198},{"title":"Qwen3-Omni Technical Report","arxiv_id":"2509.17765","year":2025,"url":"https://arxiv.org/abs/2509.17765","citing_submissions":476,"explicit_arxiv_matches":299,"examples":[{"id":"do6Wfg04EK","title":"ATTEST: Evidence-Grounded Reinforcement Learning for Long-Audio Temporal Understanding","forum_url":"https://openreview.net/forum?id=do6Wfg04EK","excerpt":"SSP), pp. 1–5, 2025. doi: 10.1109/ ICASSP49660.2025.10889879. Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, and Junyang Lin. Qwen2.5-omni technical report, 2025a. URL https://arxiv.org/abs/2503.20215. Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting He, Xinfa Zhu, et al. Qwen3-omni technical report. arXiv preprint arXiv:2509.17765, 2025b. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao","arxiv_match":true},{"id":"eeqPXcVTnJ","title":"MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy","forum_url":"https://openreview.net/forum?id=eeqPXcVTnJ","excerpt":"grui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting He, Xinfa Zhu, Yuanjun Lv, Yongqi Wang, Dake Guo, He Wang, Linhan Ma, Pei Zhang, Xinyu Zhang, Hongkun Hao, Zishan Guo, Baosong Yang, Bin Zhang, Ziyang Ma, Xipin Wei, Shuai Bai, Keqin Chen, Xuejing Liu, Peng Wang, Mingkun Yang, Dayiheng Liu, Xingzhang Ren, Bo Zheng, Rui Men, Fan Zhou, Bowen Yu, Jianxin Yang, Le Yu, Jingren Zhou, and Junyang Lin. Qwen3-omni technical report. arXiv preprint arXiv:2509.17765, 2025b. Qize Yang, Shimin Yao, Weixuan Chen, Shenghao Fu, Detao Bai, Jiaxing Zhao, Boyuan Sun, Bowe","arxiv_match":true},{"id":"7EtSASfnVn","title":"OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning","forum_url":"https://openreview.net/forum?id=7EtSASfnVn","excerpt":"g via Key Token Attribution. arXiv preprint arXiv:2601.19686, 2026. Xiaomi. Introducing MiMo-V2.5, April 2026. mimo-v2-5/. URL https://mimo.xiaomi.com/ Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, et al. Qwen2.5-Omni Technical Report. arXiv preprint arXiv:2503.20215, 2025a. Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, et al. Qwen3-Omni Technical Report. arXiv preprint arXiv:2509.17765, 2025b. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weipin","arxiv_match":true}],"id":"2509.17765","metadata_source":"Cached arXiv API metadata","rank":199},{"title":"Gemma 2: Improving Open Language Models at a Practical Size","arxiv_id":"2408.00118","year":2024,"url":"https://arxiv.org/abs/2408.00118","citing_submissions":475,"explicit_arxiv_matches":443,"examples":[{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":" 2026. Qihang Fan, Huaibo Huang, and Ran He. Breaking the Low-Rank Dilemma of Linear Attention. In CVPR, 2025. Fengyi Fu, Mengqi Huang, Shaojin Wu, Yunsheng Jiang, Yufei Huo, Hao Li, Yinghang Song, Fei Ding, Jianzhu Guo, Qian He, Zheren Fu, Zhendong Mao, and Yongdong Zhang. Lance: Unified Multimodal Modeling by Multi-Task Synergy, 2026. URL https://arxiv.org/abs/2605.18678. Gemma Team. Gemma 2: Improving Open Language Models at a Practical Size. arXiv preprint arXiv:2408.00118, 2024. Mohsen Ghafoorian, Denis Korzhenkov, and Amirhossein Habibian. Attention Surgery: An Efficie","arxiv_match":true},{"id":"WmdhapRKFO","title":"Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits","forum_url":"https://openreview.net/forum?id=WmdhapRKFO","excerpt":"thods in Natural Language Processing, pp. 17432–17445. Association for Computational Linguistics, 2024. doi: 10.18653/ v1/2024.emnlp-main.965. URL https://aclanthology.org/2024.emnlp-main. 965/. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. Gemma 2: Improving open language models at a practical size, 2024. URL https://arxiv.org/ abs/2408.00118. Amirata Ghorbani and James Y Zou. Neuron shapley: Discovering the responsible neurons. In Advances","arxiv_match":true},{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"Kirk, Anand Rao, Minh Giang, Ludovic Peran, Tris Warkentin, Eli Collins, Joelle Barral, Zoubin Ghahramani, Raia Hadsell, D. Sculley, Jeanine Banks, Anca Dragan, Slav Petrov, Oriol Vinyals, Jeff Dean, Demis Hassabis, Koray Kavukcuoglu, Clement Farabet, Elena Buchatskaya, Sebastian Borgeaud, Noah Fiedel, Armand Joulin, Kathleen Kenealy, Robert Dadashi, and Alek Andreev. Gemma 2: Improving open language models at a practical size, 2024. URL https://arxiv.org/abs/2408.00118. Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pear","arxiv_match":true}],"id":"2408.00118","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":200},{"title":"SnapKV: LLM knows what you are looking for before generation","arxiv_id":"2404.14469","year":2024,"url":"https://arxiv.org/abs/2404.14469","citing_submissions":475,"explicit_arxiv_matches":88,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"vances in Neural Information Processing Systems, 38:76342–76366, 2025. Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, and Deming Chen. Snapkv: Llm knows what you are looking for before generation. Advances in Neural Information Processing Systems, 37:22947–22970, 2024. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. Kivi: A tuning-fr","arxiv_match":false},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"guage Modeling, 2026. URL https: //openreview.net/forum?id=Mvdvz3jHiK. Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, and Deming Chen. Snapkv: Llm knows what you are looking for before generation. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 22947–22970. Curran Associates, Inc., 2","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"inth Annual Conference on Neural Information Processing Systems, 2025. Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, and Deming Chen. SnapKV: LLM knows what you are looking for before generation. Advances in Neural Information Processing Systems, 37:22947–22970, 2024. Manlai Liang, JiaMing Zhang, Xiong Li, and Jinlong Li. LagKV: Lag-relative information of the KV cache tells which tokens are","arxiv_match":false}],"id":"2404.14469","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":201},{"title":"Tulu 3: Pushing frontiers in open language model post-training","arxiv_id":"2411.15124","year":2024,"url":"https://arxiv.org/abs/2411.15124","citing_submissions":473,"explicit_arxiv_matches":325,"examples":[{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"ingma and J. Ba. Adam: A method for stochastic optimization, 2017. URL https: //arxiv.org/abs/1412.6980. N. Lambert, J. Morrison, V. Pyatkin, S. Huang, H. Ivison, F. Brahman, L. J. V. Miranda, A. Liu, N. Dziri, S. Lyu, Y. Gu, S. Malik, V. Graf, J. D. Hwang, J. Yang, R. L. Bras, O. Tafjord, C. Wilhelm, L. Soldaini, N. A. Smith, Y. Wang, P. Dasigi, and H. Hajishirzi. Tulu 3: Pushing frontiers in open language model post-training, 2025. URL https://arxiv.org/abs/2411.15124. K. Liang, B. Liu, L. Chen, and qiang liu. Memory-efficient LLM training with online subspace desce","arxiv_match":true},{"id":"RqXP83lQJI","title":"Local Splits, Global Shifts: An Interaction Decomposition of Retokenization in Language Models","forum_url":"https://openreview.net/forum?id=RqXP83lQJI","excerpt":"ularization: Improving neural network translation models with multiple subword candidates. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 66–75, 2018. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, et al. Tulu 3: Pushing frontiers in open language model post-training. arXiv preprint arXiv:2411.15124, 2024. Clara Meister. Tokeval: A tokenizer evaluation suite. arXiv preprint arXiv:2608.18062, 2026.","arxiv_match":true},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"idan Clark, et al. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022. Hugging Face H4. Aime 2024. Hugging Face Dataset, 2025. URL https://huggingface. co/datasets/HuggingFaceH4/aime_2024. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, et al. Tulu 3: Pushing frontiers in open language model post-training. arXiv preprint arXiv:2411.15124, 2024. Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via specul","arxiv_match":true}],"id":"2411.15124","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":202},{"title":"Selective classification for deep neural networks","arxiv_id":"1705.08500","year":2017,"url":"https://arxiv.org/abs/1705.08500","citing_submissions":466,"explicit_arxiv_matches":41,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":" Christopher A Field and Alan H Welsh. Bootstrapping clustered data. Journal of the Royal Statistical Society Series B: Statistical Methodology, 69(3):369–390, 2007. Yonatan Geifman and Ran El-Yaniv. Selective classification for deep neural networks. Advances in neural information processing systems, 30, 2017. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation ","arxiv_match":false},{"id":"uTIoY3tprZ","title":"More Information, Less Uncertainty? Monotonic Uncertainty Regularization under Progressive Information","forum_url":"https://openreview.net/forum?id=uTIoY3tprZ","excerpt":" for improving the accuracy and computational cost of cascade inference systems. ArXiv, abs/2104.09286, 2021. URL https://api.semanticscholar.org/CorpusID:233296030. Yonatan Geifman and Ran El-Yaniv. Selective classification for deep neural networks. ArXiv, abs/1705.08500, 2017. URL https://api.semanticscholar.org/CorpusID: 491127. Yonatan Geifman and Ran El-Yaniv. Selectivenet: A deep neural network with an integrated reject option. In Internat","arxiv_match":false},{"id":"uqsXddQsDQ","title":"How Far Can Reliability Drift Without Changing the Confidence Distribution?","forum_url":"https://openreview.net/forum?id=uqsXddQsDQ","excerpt":"nam Neyshabur, and Hanie Sedghi. Leveraging unlabeled data to predict out-of-distribution performance. In International Conference on Learning Representations, 2022. Yonatan Geifman and Ran El-Yaniv. Selective classification for deep neural networks. In NeurIPS, 2017. Jun-ya Gotoh, Michael Jong Kim, and Andrew E.B. Lim. Robust empirical optimization is almost the same as mean–variance optimization. Operations Research Letters, 46(4):448–452, 201","arxiv_match":false}],"id":"1705.08500","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":203},{"title":"Self-RAG: Learning to retrieve, generate, and critique through self-reflection","arxiv_id":"2310.11511","year":2023,"url":"https://arxiv.org/abs/2310.11511","citing_submissions":466,"explicit_arxiv_matches":63,"examples":[{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":"e% 20Sonnet%204.6%20System%20Card.pdf. Accessed: 2026-08-13. Rahul Aralikatte et al. Ellipsis resolution as question answering: An evaluation. arXiv preprint arXiv:1908.11141, 2019. Akari Asai et al. Self-rag: Learning to retrieve, generate, and critique through self-reflection. In International conference on learning representations, volume 2024, pp. 9112–9141, 2024. Zhoujun Cheng et al. Batch prompting: Efficient inference with large language model apis. In Proceedings of","arxiv_match":false},{"id":"W1MVAZmjG8","title":"ReCAP: Retrieval-Guided Capability Reuse for Multimodal Continual Instruction Tuning","forum_url":"https://openreview.net/forum?id=W1MVAZmjG8","excerpt":" Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In The Twelfth International Conference on Learning Representations, 2024. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George B. M. van den Drie","arxiv_match":false},{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"d Yiqun Liu. MemoryBench: A benchmark for memory and continual learning in LLM systems. arXiv preprint arXiv:2510.17281, 2025. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In International Conference on Learning Representations (ICLR), 2024. Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al.","arxiv_match":false}],"id":"2310.11511","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":204},{"title":"AlphaEvolve: A coding agent for scientific and algorithmic discovery","arxiv_id":"2506.13131","year":2025,"url":"https://arxiv.org/abs/2506.13131","citing_submissions":465,"explicit_arxiv_matches":423,"examples":[{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":" TCOMM.2010.04.080657. Alexander Novikov, Ngân Vũ, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco J. R. Ruiz, Abbas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushmeet Kohli, and Matej Balog. AlphaEvolve: A coding agent for scientific and algorithmic discovery. arXiv preprint arXiv:2506.13131, 2025. URL https://arxiv.org/ abs/2506.13131. Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, and Lihi","arxiv_match":true},{"id":"Xq1WyguERb","title":"Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery","forum_url":"https://openreview.net/forum?id=Xq1WyguERb","excerpt":"s and Systems I: Regular Papers, 60(3):690–702, 2013. Jorge J Moré and Stefan M Wild. Estimating derivatives of noisy simulations. ACM Transactions on Mathematical Software (TOMS), 38(3):1–21, 2012. Alexander Novikov, Ngân Vũ, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco JR Ruiz, Abbas Mehrabian, et al. Alphaevolve: A coding agent for scientific and algorithmic discovery. arXiv preprint arXiv:2506.13131, 2025. Ana K Rivera, Anvita Bhagavathula, Alvaro Carbonero, and Priya Donti. PF∆: A benchmark datas","arxiv_match":true},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"112.09332, 2021. URL https://arxiv.org/abs/2112.09332. Avanika Narayan, Dan Biderman, Sabri Eyuboglu, Avner May, Scott Linderman, James Zou, and Christopher Ré. Minions: Cost-efficient collaboration between on-device and cloud language models. arXiv preprint arXiv:2502.15964, 2025. URL https://arxiv.org/abs/2502. 15964. Alexander Novikov, Ngân Vũ, Marvin Eisenberger, et al. AlphaEvolve: A coding agent for scientific and algorithmic discovery. arXiv preprint arXiv:2506.13131, 2025. URL https://arxiv. org/abs/2506.13131. NVIDIA. NVIDIA Nemotron 3 Ultra, 2026. URL https://re","arxiv_match":true}],"id":"2506.13131","metadata_source":"Cached arXiv API metadata","rank":205},{"title":"Learning fine-grained bimanual manipulation with low-cost hardware","arxiv_id":"2304.13705","year":2023,"url":"https://arxiv.org/abs/2304.13705","citing_submissions":464,"explicit_arxiv_matches":194,"examples":[{"id":"ylrbHy6YvK","title":"KI-VLA: Kinematics-Informed Action Decoding for Flow-Matching Robot Policies","forum_url":"https://openreview.net/forum?id=ylrbHy6YvK","excerpt":"mplex dynamics forecasting. Journal of Statistical Mechanics: Theory and Experiment, 2021(12):124012, 2021. doi: 10.1088/1742-5468/ac3ae5. Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. In Proceedings of Robotics: Science and Systems, Daegu, Republic of Korea, July 2023. doi: 10.15607/RSS.2023.XIX.016. ","arxiv_match":false},{"id":"fKq8qGzBwD","title":"Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning","forum_url":"https://openreview.net/forum?id=fKq8qGzBwD","excerpt":"Fang, Chelsea Finn, and Sergey Levine. Bridgedata v2: A dataset for robot learning at scale. In Conference on Robot Learning (CoRL), 2023. Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipulation with low-cost hardware, 2023. URL https://arxiv.org/abs/2304.137 05. A ","arxiv_match":false},{"id":"Z11mf2nWjl","title":"Recovering the View: Benchmarking Physical Active Vision for Occlusion Recovery in Robotic Manipulation","forum_url":"https://openreview.net/forum?id=Z11mf2nWjl","excerpt":"eometric priors for inference-efficient world action models. arXiv preprint arXiv:2607.05468, 2026b. URL https://arxiv.org/abs/2607.05468. Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. In Proceedings of Robotics: Science and Systems, Daegu, Republic of Korea, July 2023. doi: 10.15607/RSS.2023.XIX.016. Shuai Zhou, Kaisheng Pang, Wenxuan Song, Wenjie Zhang, Xinhu Zheng, and Haoang L","arxiv_match":false}],"id":"2304.13705","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":206},{"title":"Editing models with task arithmetic","arxiv_id":"2212.04089","year":2022,"url":"https://arxiv.org/abs/2212.04089","citing_submissions":462,"explicit_arxiv_matches":153,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"dable representation cited by Open-MOPD; accessed September 26, 2026. Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum ?id=6t0Kwf8-jrj. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Arm","arxiv_match":false},{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"s and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual, 2021. Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. Youngeun Kim, Seunghwan Lee, Aecheon Jung, Bogon Ryu, and Sungeu","arxiv_match":false},{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"9/TKDE.2025.3594687. URL https://doi.ieeecomputersociety.org/10. 1109/TKDE.2025.3594687. Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id= 6t0Kwf8-jrj. Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanuge","arxiv_match":false}],"id":"2212.04089","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":207},{"title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis","arxiv_id":"2307.01952","year":2023,"url":"https://arxiv.org/abs/2307.01952","citing_submissions":462,"explicit_arxiv_matches":149,"examples":[{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"he IEEE/CVF International Conference on Computer Vision, pp. 15086–15095, 2025. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4195–4205, 2023. Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Gi","arxiv_match":true},{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"i.com/index/ Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kunpeng Li, Felix Juefei-Xu, et al. Transfer between modalities with metaqueries. arXiv preprint arXiv:2504.06256, 2025. Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. Qi Qin, Le Zhuo, Yi Xin, Ruoyi Du, Zhen Li, Bin Fu, Yiting Lu, Jiakang Yuan, Xinyue Li, Dong","arxiv_match":true},{"id":"ncOxUOWsiS","title":"Frequency-Steered VAEs: Restoring Fine Details in Low-Resolution Latents","forum_url":"https://openreview.net/forum?id=ncOxUOWsiS","excerpt":"yalty free images & videos. https://www.pexels. com, 2026. Accessed: 2026-09-04. Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=di52zR8xgf. Javier Portilla and Eero P. Simoncelli. A parametric texture model based on","arxiv_match":false}],"id":"2307.01952","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":208},{"title":"Scaling LLM Test-Time Compute Optimally Can Be More Effective than Scaling Model Parameters","arxiv_id":"2408.03314","year":2024,"url":"https://arxiv.org/abs/2408.03314","citing_submissions":461,"explicit_arxiv_matches":389,"examples":[{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":"ith, Scott W. Linderman, and David Sussillo. Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. In Advances in Neural Information Processing Systems (NeurIPS), 2021. URL https://arxiv.org/abs/2111.01256. Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling LLM test-time compute optimally can be more effective than scaling model parameters. arXiv preprint arXiv:2408.03314, 2024. URL https://arxiv.org/abs/2408.03314. David Sussillo and Omri Barak. Opening the black box: Low-dimensional dynamics in highdimensional ","arxiv_match":true},{"id":"UeO5A1vhy1","title":"Correcting Is Not Reusing: What a Failed Attempt Contributes Beyond the Verdict","forum_url":"https://openreview.net/forum?id=UeO5A1vhy1","excerpt":"o, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems (NeurIPS), 2023. arXiv:2303.11366. Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning. In International Conference on Learning Representations (ICLR), 2025. arXiv:2408.03314. Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati. GPT-4 doesn’t know it’s wrong: An analysi","arxiv_match":true},{"id":"9GDStxR3kP","title":"Inter-Trajectory Dependence Limits Answer Coverage in Local dLLM Repair","forum_url":"https://openreview.net/forum?id=9GDStxR3kP","excerpt":"arXiv preprint arXiv:2604.01624, 2026. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: language agents with verbal reinforcement learning. In Proceedings of the 37th International Conference on Neural Information Processing Systems, pp. 8634–8652, 2023. Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling llm test-time compute optimally can be more effective than scaling model parameters. arXiv preprint arXiv:2408.03314, 2024. Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. Commonsenseqa: A questio","arxiv_match":true}],"id":"2408.03314","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":209},{"title":"Gptq: Accurate post-training quantization for generative pre-trained transformers","arxiv_id":"2210.17323","year":2022,"url":"https://arxiv.org/abs/2210.17323","citing_submissions":457,"explicit_arxiv_matches":270,"examples":[{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"runed-dnn models. Proceedings of Machine Learning and Systems, 5:768–779, 2023. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models. arXiv e-prints, pp. arXiv–2407, 2024. Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:2210.17323, 2022. Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen. Sparse gpu kernels for deep learni","arxiv_match":true},{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"l Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, 2022. Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. GPTQ: accurate post-training quantization for generative pre-trained transformers. CoRR, abs/2210.17323, 2022. Cong Guo, Yuxian Qiu, Jingwen Leng, Xiaotian Gao, Chen Zhang, Yunxin Liu, Fan Yang, Yuhao Zhu, and Minyi Guo. Squant: On-the-fly data-free quantization via diagonal hessi","arxiv_match":false},{"id":"hAJ2b6a8w1","title":"CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning","forum_url":"https://openreview.net/forum?id=hAJ2b6a8w1","excerpt":"parseGPT: Massive language models can be accurately pruned in one-shot. In International Conference on Machine Learning (ICML), 2023. Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. GPTQ: Accurate post-training quantization for generative pre-trained transformers. In International Conference on Learning Representations (ICLR), 2023. Zhiren Gong, Yikun Hou, Fan Wu, Che Wang, Fuyao Zhang, Tiantong Wu, Yurong Hao, Jiaming Zhang, Yiyang Duan, Tiantong Wang, Fei H","arxiv_match":false}],"id":"2210.17323","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":210},{"title":"Can a suit of armor conduct electricity? a new dataset for open book question answering","arxiv_id":"1809.02789","year":2018,"url":"https://arxiv.org/abs/1809.02789","citing_submissions":454,"explicit_arxiv_matches":50,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"15, 2025. Peter McCullagh. Regression models for ordinal data. Journal of the Royal Statistical Society: Series B, 42(2):109–142, 1980. Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can a suit of armor conduct electricity? A new dataset for open book question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2381–2391, 2018. Yatin Nandwani, Abhishek Pathak, Mausam, and Parag Singla. A primal dual formulation f","arxiv_match":false},{"id":"r1zsoUu97b","title":"Advancing the Calibration-Compute Frontier of LLM Classifiers via Asymmetric Duos","forum_url":"https://openreview.net/forum?id=r1zsoUu97b","excerpt":"n Bossan, and Marian Tietz. PEFT: State-of-the-art parameter-efficient fine-tuning methods. https: //github.com/huggingface/peft, 2022. Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can a suit of armor conduct electricity? a new dataset for open book question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2381–2391. Association for Computational Linguistics, October-November 2018. Shervin Minaee, Tomas Miko","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"low: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation. In CVPR, 2025. Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. In ACL, 2022. Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can a suit of armor conduct electricity? a new dataset for open book question answering. arXiv preprint arXiv:1809.02789, 2018. Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document i","arxiv_match":true}],"id":"1809.02789","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":211},{"title":"Group Sequence Policy Optimization","arxiv_id":"2507.18071","year":2025,"url":"https://arxiv.org/abs/2507.18071","citing_submissions":454,"explicit_arxiv_matches":420,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"print arXiv:2601.18734, 2026. Chujie Zheng, Kai Dang, Bowen Yu, Mingze Li, Huiqiang Jiang, Junrong Lin, Yuqiong Liu, Hao Lin, Chencan Wu, Feng Hu, et al. Stabilizing reinforcement learning with llms: Formulation and practices. arXiv preprint arXiv:2512.01374, 2025a. Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, Jingren Zhou, and Junyang Lin. Group sequence policy optimization. arXiv preprint arXiv:2507.18071, 2025b. Xuhui Zheng, Kang An, Ziliang Wang, Yuhang Wang, and Yichao Wu. Stepsearch: Igniting llms s","arxiv_match":true},{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"gyu Zhao, Junming Lin, Tianhao Liang, Yifan Zhou, Wenhao Chai, Yuzhe Gu, Weiyun Wang, Kai Chen, Gen Luo, Wenwei Zhang, et al. Mm-helix: Boosting multimodal long-chain reflective reasoning with holistic platform and adaptive hybrid policy optimization. arXiv preprint arXiv:2510.08540, 2025. Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, et al. Group sequence policy optimization. arXiv preprint arXiv:2507.18071, 2025. A L IMITATIONS AND F UTURE W ORK While P UZZLE C ODE B ENCH introduces an execution-based fr","arxiv_match":true},{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"89. Ziqi Zhao, Xinyu Ma, Liu Yang, Yujie Feng, Daiting Shi, Jingzhou He, Xin Xin, Zhaochun Ren, and Xiao-Ming Wu. Rosd: Reflective on-policy self-distillation for language model reasoning across domains, 2026c. URL https://arxiv.org/abs/2605.28014. Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, Jingren Zhou, and Junyang Lin. Group sequence policy optimization, 2025. URL https://arxiv.org/abs/2507.18071. A E STIMATING THE PMI ATTENUATION COEFFICIENT A.1 E STIMATION ALGORITHM Use the student trajectory","arxiv_match":true}],"id":"2507.18071","metadata_source":"Cached arXiv API metadata","rank":212},{"title":"Parameter-efficient transfer learning for NLP","arxiv_id":"1902.00751","year":2019,"url":"https://arxiv.org/abs/1902.00751","citing_submissions":454,"explicit_arxiv_matches":30,"examples":[{"id":"OFVWcaGfdi","title":"The Delta Is Not the Skill: Zero-Prior Elicitation Transfers LoRA Behavior Across Model Families","forum_url":"https://openreview.net/forum?id=OFVWcaGfdi","excerpt":" neural network. arXiv preprint arXiv:1503.02531, 2015. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for nlp. In International conference on machine learning, pp. 2790–2799. PMLR, 2019. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-ra","arxiv_match":false},{"id":"M1V3pHi7o3","title":"Beyond Text Anchors: Dual-Anchor Flow Matching for Few-Shot and Incremental Adaptation","forum_url":"https://openreview.net/forum?id=M1V3pHi7o3","excerpt":"in Applied Earth Observations and Remote Sensing, 2019. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In ICML, 2019. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In ICLR, 2022. Yunsh","arxiv_match":false},{"id":"QlhWfeqoej","title":"Evidence Is Not Extent: Separating Referential Evidence, Object Extent, and Boundary Trust for Referring Image Segmentation","forum_url":"https://openreview.net/forum?id=QlhWfeqoej","excerpt":"ce on Machine Learning, volume 70, pp. 1321–1330, 2017. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, volume 97, pp. 2790–2799. PMLR, 2019. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu","arxiv_match":false}],"id":"1902.00751","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":213},{"title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","arxiv_id":"1801.01290","year":2018,"url":"https://arxiv.org/abs/1801.01290","citing_submissions":453,"explicit_arxiv_matches":33,"examples":[{"id":"JQG8LaeAwe","title":"Reinforcement Learning by Change of Measure: A BSDE Theory for Generative Policies","forum_url":"https://openreview.net/forum?id=JQG8LaeAwe","excerpt":"of measures. Theory of Probability and Its Applications, 5(3):285–301, 1960. doi: 10.1137/1105027. URL https://doi.org/10.1137/1105027. Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pp. 1861–1870. PMLR, 2018. URL https://proceedings.mlr.press/v80/ haar","arxiv_match":false},{"id":"u3efKiCvkc","title":"Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent","forum_url":"https://openreview.net/forum?id=u3efKiCvkc","excerpt":"nd Sergey Levine. Reinforcement learning with deep energy-based policies. In International conference on machine learning (ICML), 2017. Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning (ICML), 2018. Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine. IDQL: Implicit Q-learning as an actor-c","arxiv_match":false},{"id":"aMg25b6WKq","title":"Consensus Accelerates Evolution: Black-Box Many-Task Optimization via Information-Geometric Consensus","forum_url":"https://openreview.net/forum?id=aMg25b6WKq","excerpt":"g Feng. Multifactorial evolution: Toward evolutionary multitasking. IEEE Transactions on Evolutionary Computation, 20(3):343–357, 2016. Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International Conference on Machine Learning, pp. 1861–1870. PMLR, 2018. Nikolaus Hansen and Andreas Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Comp","arxiv_match":false}],"id":"1801.01290","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":214},{"title":"Flamingo: a visual language model for few-shot learning","arxiv_id":"2204.14198","year":2022,"url":"https://arxiv.org/abs/2204.14198","citing_submissions":450,"explicit_arxiv_matches":48,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"cond International Conference on Machine Learning, 2025. Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, et al. Flamingo: a visual language model for few-shot learning. Advances in neural information processing systems, 35:23716–23736, 2022. Léon Bottou and Vladimir Vapnik. Local learning algorithms. Neural computation, 4(6):888–900, 1992. Bradley Brown, Jordan Jur","arxiv_match":false},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"cessing systems, 34:24206–24221, 2021. Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. Advances in neural information processing systems, 35:23716– 23736, 2022. Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video","arxiv_match":false},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"gence, volume 33, pp. 8076–8084, 2019. Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. Advances in neural information processing systems, 35:23716–23736, 2022. John Arevalo, Thamar Solorio, Manuel Montes-y Gómez, and Fabio A González. Gated multimodal units for information fusion. arX","arxiv_match":false}],"id":"2204.14198","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":215},{"title":"Large language models cannot self-correct reasoning yet","arxiv_id":"2310.01798","year":2023,"url":"https://arxiv.org/abs/2310.01798","citing_submissions":449,"explicit_arxiv_matches":127,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"ird International Joint Conference on Artificial Intelligence, IJCAI-24, pp. 8048–8057, 2024. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. Large language models cannot self-correct reasoning yet. In The Twelfth International Conference on Learning Representations, 2024. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? ","arxiv_match":false},{"id":"ROquSvIhjs","title":"Inverse Garment Design with Visual Agents","forum_url":"https://openreview.net/forum?id=ROquSvIhjs","excerpt":"idance. ACM Transactions on Graphics (TOG), 43(4):1–13, 2024. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. Large language models cannot self-correct reasoning yet. arXiv preprint arXiv:2310.01798, 2024. Boyi Jiang, Juyong Zhang, Yang Hong, Jinhao Luo, Ligang Liu, and Hujun Bao. Bcnet: Learning ","arxiv_match":true},{"id":"0LJ106OAjs","title":"Forecasting Before the Outcome: Catalogue Gated Prequential Cognitive Performance under Participant and Domain Shift","forum_url":"https://openreview.net/forum?id=0LJ106OAjs","excerpt":". Tracking the best expert. Machine Learning, 32(2): 151–178, 1998. doi: 10.1023/A:1007424614876. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Yu, Xinying Song, and Denny Zhou. Large language models cannot self-correct reasoning yet. In International conference on learning representations, volume 2024, pp. 32808–32824, 2024. Saman Khazaei and Rose T Faghih. Decoding a cognitive performance state from behavioral data in the prese","arxiv_match":false}],"id":"2310.01798","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":216},{"title":"Fast inference from transformers via speculative decoding","arxiv_id":"2211.17192","year":2022,"url":"https://arxiv.org/abs/2211.17192","citing_submissions":448,"explicit_arxiv_matches":47,"examples":[{"id":"e9RG7RBDSX","title":"Cadence: Regulating the Speculation Externality in LLM Serving","forum_url":"https://openreview.net/forum?id=e9RG7RBDSX","excerpt":"nt for large language model serving with PagedAttention. In Proceedings of the 29th Symposium on Operating Systems Principles, pp. 611–626. ACM, 2023. Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via speculative decoding. In Proceedings of the 40th International Conference on Machine Learning, Proceedings of Machine Learning Research. PMLR, 2023. Rui Li, Zhaoning Zhang, Libo Zhang, Huaimin Wang, Xiang Fu, and Zhiquan","arxiv_match":false},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"anda, Alisa Liu, Nouha Dziri, Shane Lyu, et al. Tulu 3: Pushing frontiers in open language model post-training. arXiv preprint arXiv:2411.15124, 2024. Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via speculative decoding. In International conference on machine learning, pp. 19274–19286. PMLR, 2023. Jia Li, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Alb","arxiv_match":false},{"id":"qbUThdO3bP","title":"Do Not Go Gentle into Repetition: Escaping Overthinking with Repetition-Aware Reinforcement Learning","forum_url":"https://openreview.net/forum?id=qbUThdO3bP","excerpt":"ent large language model reasoning. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=RciRcJ9LHw. Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via speculative decoding. In International Conference on Machine Learning, pp. 19274–19286. PMLR, 2023. math-ai. AMC23. Hugging Face dataset, 2025. datasets/math-ai/amc23. URL https://huggingface.co/ Niklas Muennighoff, Zito","arxiv_match":false}],"id":"2211.17192","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":217},{"title":"Deep reinforcement learning from human preferences","arxiv_id":"1706.03741","year":2017,"url":"https://arxiv.org/abs/1706.03741","citing_submissions":447,"explicit_arxiv_matches":36,"examples":[{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"ining of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017. Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. IEEE transactions on pat","arxiv_match":false},{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":"ve the calibration of machine learning models. In International Conference on Learning Representations, 2025. Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems, volume 30, pp. 4299–4307, 2017. Florian E. Dorner, Vivian Y. Nastl, and Moritz Hardt. Limits to scalable evaluation at the frontier: LLM as judg","arxiv_match":false},{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":"e models. In Proceedings of the 41st International Conference on Machine Learning, pp. 6621–6642. PMLR, 2024. Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems, 2017. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiic","arxiv_match":false}],"id":"1706.03741","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":218},{"title":"Group-in-Group Policy Optimization for LLM Agent Training","arxiv_id":"2505.10978","year":2025,"url":"https://arxiv.org/abs/2505.10978","citing_submissions":447,"explicit_arxiv_matches":135,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"er, Juan Pavez, and Gilles Louppe. Approximating likelihood ratios with calibrated discriminative classifiers. arXiv preprint arXiv:1506.02169, 2015. Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An. Group-in-group policy optimization for llm agent training. Advances in Neural Information Processing Systems, 38:46375–46408, 2025. Xidong Feng, Ziyu Wan, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, and Jun Wang. Alphazero-like tree-search c","arxiv_match":false},{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"ng Zhang, et al. Agentic reinforced policy optimization. In International Conference on Learning Representations, volume 2026, pp. 16981–17017, 2026. Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An. Group-in-group policy optimization for llm agent training. Advances in Neural Information Processing Systems, 38:46375–46408, 2026. Google. Introducing gemini 2.0: Our new AI model for the agentic era, 2024. https://blog.google/innovation-and-ai/models-and-","arxiv_match":false},{"id":"9N1uPPmr4W","title":"Shared Resources Distort Credit Assignment in Group-Relative Agent Reinforcement Learning","forum_url":"https://openreview.net/forum?id=9N1uPPmr4W","excerpt":" Xia, and Haibo Chen. DeltaBox: Scaling stateful AI agents with millisecond-level sandbox checkpoint/rollback. arXiv preprint arXiv:2605.22781, 2026. Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An. Group-in-group policy optimization for LLM agent training. In Advances in Neural Information Processing Systems, volume 38, pp. 46375–46408, 2026. Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactu","arxiv_match":false}],"id":"2505.10978","metadata_source":"Cached arXiv API metadata","rank":219},{"title":"MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI","arxiv_id":"2311.16502","year":2023,"url":"https://arxiv.org/abs/2311.16502","citing_submissions":447,"explicit_arxiv_matches":65,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":" Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. URL https://arxiv.org/abs/2311.16502. A This appendix expands the task taxonomy in Section 3.2. Table 5 defines the judgment required by","arxiv_match":true},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"egrated capabilities, 2024. URL https://arxiv.org/abs/2308.02490. Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi, 2024. URL https://arxiv.org/abs/2311.16502. Table 7: Current full-model implementation. Visual-depth routing and parameter-expert routing oper","arxiv_match":true},{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"nics, communications and computer sciences, 86(5):1052–1060, 2003. Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9556–9567, 2024. Fengji Zhang, Linquan Wu, Huiyu Bai, Guancheng Lin, Xiao Li, Xiao Yu, Yue Wang, Bei Chen, a","arxiv_match":false}],"id":"2311.16502","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":220},{"title":"Mmbench: Is your multi-modal model an all-around player?","arxiv_id":"2307.06281","year":2023,"url":"https://arxiv.org/abs/2307.06281","citing_submissions":447,"explicit_arxiv_matches":66,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":" Rastogi, Adrien Sadé, Alan Jeffares, Albert Jiang, Alexandre Cahill, Alexandre Gavaudan, et al. Ministral 3. arXiv preprint arXiv:2601.08584, 2026. URL https://arxiv.org/abs/2601.08584. Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, and Dahua Lin. MMBench: Is your multimodal model an all-around player? In European Conference on Computer Vision, 2024. URL https://arxiv.org/abs/2307.06281. Shiyin Lu, Yang Li, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, and Han-Jia Ye. Ovis: Structu","arxiv_match":true},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"85. Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava-next: Improved reasoning, ocr, and world knowledge, January 2024a. URL https: //llava-vl.github.io/blog/2024-01-30-llava-next/. Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, and Dahua Lin. Mmbench: Is your multi-modal model an all-around player?, 2024b. URL https://arxiv.org/abs/2307.06281. Yuliang Liu, Zhang Li, Mingxin Huang, Biao Yang, Wenwen Yu, Chunyuan Li, Xu-Cheng Yin, Cheng-Lin L","arxiv_match":true},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":" objects in context. In Computer Vision – ECCV 2014, pp. 740–755, 2014. Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pp. 216–233. Springer, 2024. Wenhan Ma, Jianyu Wei, Liang Zhao, Hailin Zhang, Bangjun Xiao, Lei Li, Qibin Yang, Bofei Gao, Yudong Wang, Rang Li, et al. MOP","arxiv_match":false}],"id":"2307.06281","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":221},{"title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","arxiv_id":"2312.08935","year":2023,"url":"https://arxiv.org/abs/2312.08935","citing_submissions":445,"explicit_arxiv_matches":80,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"t embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. Math-shepherd: Verify and reinforce llms step-by-step without human annotations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9426–9439, 2024. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffre","arxiv_match":false},{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"gs.iclr.cc/paper_files/paper/2025/hash/ 5434be94e82c54327bb9dcaf7fca52b6-Abstract-Conference.html. Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. Math-Shepherd: Verify and reinforce LLMs step-by-step without human annotations. In Annual Meeting of the Association for Computational Linguistics (ACL), 2024a. https://aclanthology.org/2024.acl-long.510/. Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shigua","arxiv_match":false},{"id":"pK04w5wiDp","title":"Trust Is a Process, Not Just a Score: Agentic Verification for Test-Time Scaling","forum_url":"https://openreview.net/forum?id=pK04w5wiDp","excerpt":"onal Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=nnVO1PvbTv. Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. Math-shepherd: Verify and reinforce LLMs step-by-step without human annotations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, pp. 9426–9439, 2024. doi: 10.18653/v1/2024.acl-long.510. URL https://aclanthology.org/ 2024.acl-long.510/.","arxiv_match":false}],"id":"2312.08935","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":222},{"title":"OpenHands: An open platform for AI software developers as generalist agents","arxiv_id":"2407.16741","year":2024,"url":"https://arxiv.org/abs/2407.16741","citing_submissions":444,"explicit_arxiv_matches":140,"examples":[{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":"irat Singh, Hoang H Tran, Fuqiang Li, Ren Ma, Mingzhang Zheng, Bill Qian, Yanjun Shao, Niklas Muennighoff, Yizhe Zhang, Binyuan Hui, Junyang Lin, Robert Brennan, Hao Peng, Heng Ji, and Graham Neubig. Openhands: An open platform for ai software developers as generalist agents. In International Conference on Learning Representations, volume 2025, pp. 65882–65919, 2025. Chong Xiang, Drew Zagieboylo, Shaona Ghosh, Sanjay Kariyappa, Kai Greshake, Hanshen Xiao, Chaowei Xiao, a","arxiv_match":false},{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"skirat Singh, Hoang Tran, Fuqiang Li, Ren Ma, Mingzhang Zheng, Bill Qian, Daniel Shao, Niklas Muennighoff, Yizhe Zhang, Binyuan Hui, Junyang Lin, Robert Brennan, Hao Peng, Heng Ji, and Graham Neubig. Openhands: An open platform for ai software developers as generalist agents. In Y. Yue, A. Garg, N. Peng, F. Sha, and R. Yu (eds.), International Conference on Learning Representations, volume 2025, pp. 65882–65919, 2025. URL https://proceedings.iclr.cc/paper_files/paper/202","arxiv_match":false},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"/2108.00573. Hanlin Wang, Chak Tou Leong, Jiashuo Wang, Jian Wang, and Wenjie Li. SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attribution. arXiv preprint arXiv:2505.20732, 2025a. URL https://arxiv.org/abs/2505.20732. Xingyao Wang, Boxuan Li, Yufan Song, Frank F. Xu, Xiangru Tang, et al. OpenHands: An Open Platform for AI Software Developers as Generalist Agents. In International Conference on Learning Representations, 2025b. URL https://arxiv.org/abs/2407.16741. Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Y","arxiv_match":true}],"id":"2407.16741","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":223},{"title":"Qlora: Efficient finetuning of quantized llms","arxiv_id":"2305.14314","year":2023,"url":"https://arxiv.org/abs/2305.14314","citing_submissions":441,"explicit_arxiv_matches":81,"examples":[{"id":"hAJ2b6a8w1","title":"CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning","forum_url":"https://openreview.net/forum?id=hAJ2b6a8w1","excerpt":"nt8(): 8-bit matrix multiplication for transformers at scale. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. QLoRA: Efficient finetuning of quantized LLMs. In Advances in Neural Information Processing Systems (NeurIPS), 2023. Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom ","arxiv_match":false},{"id":"OFVWcaGfdi","title":"The Delta Is Not the Skill: Zero-Prior Elicitation Transfers LoRA Behavior Across Model Families","forum_url":"https://openreview.net/forum?id=OFVWcaGfdi","excerpt":"ord problems. arXiv preprint arXiv:2110.14168, 2021. MAA Codeforces. American invitational mathematics examination-aime 2024, 2024. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. Qlora: Efficient finetuning of quantized llms. Advances in neural information processing systems, 36:10088–10115, 2023. Farzad Farhadzadeh, Debasmit Das, Shubhankar Borse, and Fatih Porikli. Lora-x: Bridging foundation models with training-free ","arxiv_match":false},{"id":"vSqsA99XH4","title":"Quantization-Aware Pre-Training with Constrained Empirical Weight Distribution","forum_url":"https://openreview.net/forum?id=vSqsA99XH4","excerpt":"rank Hutter. A downsampled variant of imagenet as an alternative to the cifar datasets, 2017. URL https://arxiv.org/abs/1707.08819. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. Qlora: efficient finetuning of quantized llms. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA, 2023. Curran Associates Inc. DaYou Du, Yijia Zhang, Shijie Cao, Jiaqi Guo, ","arxiv_match":false}],"id":"2305.14314","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":224},{"title":"Sparse autoencoders find highly interpretable features in language models","arxiv_id":"2309.08600","year":2023,"url":"https://arxiv.org/abs/2309.08600","citing_submissions":440,"explicit_arxiv_matches":138,"examples":[{"id":"iksnFm6vFW","title":"Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage","forum_url":"https://openreview.net/forum?id=iksnFm6vFW","excerpt":" Dong. Mitigating overthinking in large reasoning models via manifold steering, 2025. URL https: //arxiv.org/abs/2505.22411. Robert Huben, Hoagy Cunningham, Logan Smith, Aidan Ewart, and Lee Sharkey. Sparse autoencoders find highly interpretable features in language models. In International Conference on Learning Representations, volume 2024, pp. 7827–7845, 2024. Shuyang Jiang, Yusheng Liao, Ya Zhang, Yanfeng Wang, and Yu Wang. Overthinking reduction with decoupled rew","arxiv_match":false},{"id":"fNrQjgb4Ch","title":"DV-Lens: Revealing the Functional Organization of Language Model Parameters","forum_url":"https://openreview.net/forum?id=fNrQjgb4Ch","excerpt":"h the MATH dataset. In Advances in Neural Information Processing Systems, 2021. URL https://arxiv.org/abs/ 2103.03874. Robert Huben, Hoagy Cunningham, Logan Riggs Smith, Aidan Ewart, and Lee Sharkey. Sparse autoencoders find highly interpretable features in language models. In International Conference on Learning Representations, 2024. URL https://proceedings.iclr.cc/paper_files/paper/2024/hash/ 1fa1ab11f4bd5f94b2ec20e794dbfa3b-Abstract-Conference.html. Hugging Face Op","arxiv_match":false},{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"opher Hesse, and John Schulman. Training verifiers to solve math word problems, 2021. URL https://arxiv. org/abs/2110.14168. Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. Sparse autoencoders find highly interpretable features in language models, 2023. URL https://arxiv. org/abs/2309.08600. Boyi Deng, Xu Wang, Yaoning Wang, Yu Wan, Yubo Ma, Baosong Yang, Haoran Wei, Jialong Tang, Huan Lin, Ruize Gao, Tianhao Li, Qian Cao, Xuancheng Ren, Xiao","arxiv_match":false}],"id":"2309.08600","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":225},{"title":"Understanding intermediate layers using linear classifier probes","arxiv_id":"1610.01644","year":2016,"url":"https://arxiv.org/abs/1610.01644","citing_submissions":440,"explicit_arxiv_matches":278,"examples":[{"id":"x6oIXJYZ7g","title":"LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs","forum_url":"https://openreview.net/forum?id=x6oIXJYZ7g","excerpt":" Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv:1610.01644, 2016. Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances. Computational L","arxiv_match":true},{"id":"QLMHtA7hoi","title":"Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection","forum_url":"https://openreview.net/forum?id=QLMHtA7hoi","excerpt":" [2] Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv:1610.01644, 2016. [1] Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, ","arxiv_match":true},{"id":"msQRnzv5Bl","title":"Rigorous State Evolution for General-Core Asymmetric Multilinear AMP","forum_url":"https://openreview.net/forum?id=msQRnzv5Bl","excerpt":" Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. In International Conference on Learning Representations Workshop Track, 2017. URL https://arxiv.org/abs/1610.01644. Brett W. Bader and Tamara G. Kolda. Algorithm 862: MATLAB tensor classes for fast algorithm protot","arxiv_match":true}],"id":"1610.01644","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":226},{"title":"Progressive distillation for fast sampling of diffusion models","arxiv_id":"2202.00512","year":2022,"url":"https://arxiv.org/abs/2202.00512","citing_submissions":438,"explicit_arxiv_matches":161,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"CVPR, 2022. Seyedmorteza Sadat, Otmar Hilliges, and Romann M. Weber. Eliminating oversaturation and artifacts of high guidance scales in diffusion models. In ICLR, 2025. Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In ICLR, 2022. Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, et al. Laion-5b: An open large-scale dataset for training next generation image-text m","arxiv_match":false},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"bo Zhang, Haibin Wan, and Weijie Ma. Biwm: Advancing open-source interactive video world models with bidirectional autoregression. arXiv preprint arXiv:2606.10135, 2026. Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations, 2022. Joonghyuk Shin, Zhengqi Li, Richard Zhang, Jun-Yan Zhu, Jaesik Park, Eli Shechtman, and Xun Huang. Motionstream: Real-time video genera","arxiv_match":false},{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":"7. Amirmojtaba Sabour, Sanja Fidler, and Karsten Kreis. Align your steps: Optimizing sampling schedules in diffusion models. arXiv preprint arXiv:2404.14507, 2024. Amirmojtaba Sabour, Sanja Fidler, and Karsten Kreis. Align your flow: Scaling continuous-time flow map distillation. Advances in Neural Information Processing Systems, 38:146459–146512, 2026. Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. Jaskirat Singh, Xingjian Leng, Zongze Wu, Liang Zheng, Richard Zhang, Eli Shechtman, and Sai","arxiv_match":true}],"id":"2202.00512","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":227},{"title":"SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features","arxiv_id":"2502.14786","year":2025,"url":"https://arxiv.org/abs/2502.14786","citing_submissions":437,"explicit_arxiv_matches":401,"examples":[{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"scalable reinforcement learning, 2025. URL https://arxiv.org/abs/2507.01006. Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, Olivier Hénaff, Jeremiah Harmsen, Andreas Steiner, and Xiaohua Zhai. Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features, 2025. URL https://arxiv.org/abs/2502.14786. Longrong Yang, Dong Shen, Chaoxiang Cai, Fan Yang, Tingting Gao, Di Zhang, and Xi Li. Solving toke","arxiv_match":true},{"id":"VemSN4uC6Q","title":"Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs","forum_url":"https://openreview.net/forum?id=VemSN4uC6Q","excerpt":"ao Wang, Muhammad Ferjad Naeem, Ibrahim M. Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, Olivier H’enaff, Jeremiah Harmsen, Andreas Steiner, and Xiao-Qi Zhai. Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features. ArXiv, abs/2502.14786, 2025. Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotný. Vggt: Visual geometry grounded transformer. 2025 IEEE/CVF Conference ","arxiv_match":false},{"id":"uqg7gDThwD","title":"Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation","forum_url":"https://openreview.net/forum?id=uqg7gDThwD","excerpt":"nd Jia Deng. Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras. Advances in neural information processing systems, 34:16558–16569, 2021. Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al. Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features. arXiv preprint arXiv:2502.14786, 2025. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łuka","arxiv_match":true}],"id":"2502.14786","metadata_source":"Cached arXiv API metadata","rank":228},{"title":"World Action Models are Zero-shot Policies","arxiv_id":"2602.15922","year":2026,"url":"https://arxiv.org/abs/2602.15922","citing_submissions":437,"explicit_arxiv_matches":397,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"upuru, You Liang Tan, Chuning Zhu, Jiannan Xiang, Ayaan Malik, Kyungmin Lee, William Liang, Nadun Ranawaka, Jiasheng Gu, Yinzhen Xu, Guanzhi Wang, Fengyuan Hu, Avnish Narayan, Johan Bjorck, Jing Wang, Gwanghyun Kim, Dantong Niu, Ruijie Zheng, Yuqi Xie, Jimmy Wu, Qi Wang, Ryan Julian, Danfei Xu, Yilun Du, Yevgen Chebotar, Scott Reed, Jan Kautz, Yuke Zhu, Linxi ”Jim” Fan, and Joel Jang. World action models are zero-shot policies, 2026. URL https://arxiv.org/abs/2602.15922. Tianyuan Yuan et al. Fast-wam: Do world action models need test-time future imagination?, 2026. UR","arxiv_match":true},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"Xiaoming Wang, Yiran Zhang, and Zhibo Fang. MT-WAM: Reorienting the one-pass predictive representation toward action generation. arXiv preprint arXiv:2609.21474, 2026e. URL https://arxiv.org/abs/2609.21474. Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng, Shenyuan Gao, Sihyun Yu, George Kurian, Suneel Indupuru, You Liang Tan, Chuning Zhu, Jiannan Xiang, et al. World action models are zero-shot policies. arXiv preprint arXiv:2602.15922, 2026. URL https://arxiv.org/abs/2602.15922. Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-WAM: Do world action models need test-","arxiv_match":true},{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":"ion models with an expert transformer. arXiv, 2024. Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng, Shenyuan Gao, Sihyun Yu, George Kurian, Suneel Indupuru, You Liang Tan, Chuning Zhu, Jiannan Xiang, et al. World action models are zero-shot policies. arXiv, 2026. Demin Yu, Xutao Li, Yunming Ye, Baoquan Zhang, Chuyao Luo, Kuai Dai, Rui Wang, and Xunlai Chen. Diffcast: A unified framework via residual diffusion for precipitation nowcasting. In CVP","arxiv_match":false}],"id":"2602.15922","metadata_source":"Cached arXiv API metadata","rank":229},{"title":"DINO-WM: World models on pretrained visual features enable zero-shot planning","arxiv_id":"2411.04983","year":2024,"url":"https://arxiv.org/abs/2411.04983","citing_submissions":436,"explicit_arxiv_matches":180,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":" embodied world models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025. URL https://arxiv.org/abs/2504.20995. Gaoyue Zhou, Hengkai Pan, Yann Lecun, and Lerrel Pinto. DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 79115–79135. PMLR, 2025. URL https://proceedings.mlr.press/v267/z","arxiv_match":false},{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"A simple decentralized cross-entropy method. In Advances in Neural Information Processing Systems, 2022. URL https: //arxiv.org/abs/2212.08235. Gaoyue Zhou, Hengkai Pan, Yann LeCun, and Lerrel Pinto. DINO-WM: World models on pretrained visual features enable zero-shot planning. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 79115–79135. PMLR, 2025. URL https://proceedings.mlr.press/v267/z","arxiv_match":false},{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":"g, volume 139 of Proceedings of Machine Learning Research, pp. 12611–12620. PMLR, 2021b. URL https: //proceedings.mlr.press/v139/zhang21x.html. Gaoyue Zhou, Hengkai Pan, Yann LeCun, and Lerrel Pinto. DINO-WM: World models on pretrained visual features enable zero-shot planning. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 79115–79135. PMLR, 2025. URL https://proceedings.mlr.press/v267/z","arxiv_match":false}],"id":"2411.04983","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":230},{"title":"HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal","arxiv_id":"2402.04249","year":2024,"url":"https://arxiv.org/abs/2402.04249","citing_submissions":436,"explicit_arxiv_matches":182,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"iu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Understanding R1-Zero-like training: A critical perspective. arXiv preprint arXiv:2503.20783, 2025. URL https://arxiv.org/abs/2503.20783. Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, et al. Harmbench: A standardized evaluation framework for automated red teaming and robust refusal. arXiv preprint arXiv:2402.04249, 2024. OpenAI, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman, Andy Applebaum, Edwin Arbus, Rah","arxiv_match":true},{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":", Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback. Advances in neural information processing systems, 36:46534–46594, 2023. Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David Forsyth, and Dan Hendrycks. Harmbench: A standardized evaluation framework for automated red teaming and robust refusal, 2024. URL https://arxiv.org/abs/2402.04249. Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, an","arxiv_match":true},{"id":"zEvJmHCOUL","title":"SteerProbe: Learning to Bypass Safety Steering in Vision–Language Models","forum_url":"https://openreview.net/forum?id=zEvJmHCOUL","excerpt":"st jailbreak attacks. In First Conference on Language Modeling, 2024. Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, et al. HarmBench: A standardized evaluation framework for automated red teaming and robust refusal. In Proceedings of the 41st International Conference on Machine Learning, 2024. Jayneel Parekh, Pegah Khayatan, Mustafa Shukor, Arnaud Dapogny, Alasdair Newson, and Matthieu Cord. Learning to steer: ","arxiv_match":false}],"id":"2402.04249","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":231},{"title":"RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation","arxiv_id":"2506.18088","year":2025,"url":"https://arxiv.org/abs/2506.18088","citing_submissions":436,"explicit_arxiv_matches":350,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"Baijun Chen, Zijian Cai, Yibin Liu, Zixuan Li, Qiwei Liang, Xianliang Lin, Yiheng Ge, Zhenyu Gu, Weiliang Deng, Yubin Guo, Tian Nian, Xuanbing Xie, Qiangyu Chen, Kailun Su, Tianling Xu, Guodong Liu, Mengkang Hu, Huan ang Gao, Kaixuan Wang, Zhixuan Liang, Yusen Qin, Xiaokang Yang, Ping Luo, and Yao Mu. Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation, 2025. URL https://arxiv.org/abs/2506.18088. Shuang Cheng, Yihan Bian, Dawei Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Gu","arxiv_match":true},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"o, Caroline Pascal, Jade Choghari, Jess Moss, and Thomas Wolf. Lerobot: State-of-the-art machine learning for real-world robotics in pytorch. https://github.com/ huggingface/lerobot, 2024. Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Zixuan Li, Qiwei Liang, Xianliang Lin, Yiheng Ge, Zhenyu Gu, et al. Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation. arXiv preprint arXiv:2506.18088, 2025. Tianxing Chen, Yue Chen, Zixuan Li, Junyuan Tang, Kailun Su, Haoran Lu, Weijie Wan, Baijun C","arxiv_match":true},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"ng Wang, Long Chen, Chuwei Cai, and Jinghui Lu. NanoVLA: Routing decoupled vision-language understanding for nano-sized generalist robotic policies. arXiv preprint arXiv:2510.25122, 2025a. Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Qiwei Liang, Zixuan Li, Xianliang Lin, Yiheng Ge, Zhenyu Gu, et al. RoboTwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation. arXiv preprint arXiv:2506.18088, 2025b. An-Chieh Cheng, Hongxu Yin, Yang Fu, Qiushan Guo, Ruihan Yang, Jan Kautz, Xiaolong Wang, an","arxiv_match":true}],"id":"2506.18088","metadata_source":"Cached arXiv API metadata","rank":232},{"title":"Open X-Embodiment: Robotic learning datasets and RT-X models","arxiv_id":"2310.08864","year":2023,"url":"https://arxiv.org/abs/2310.08864","citing_submissions":435,"explicit_arxiv_matches":112,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":" Li, and Jing Huo. Robohiman: A hierarchical evaluation paradigm for compositional generalization in long-horizon manipulation. arXiv preprint arXiv:2510.13149, 2025. Open X-Embodiment Collaboration. Open x-embodiment: Robotic learning datasets and rt-x models. In IEEE International Conference on Robotics and Automation (ICRA), pp. 6892–6903, May 2024. doi: 10.1109/ICRA57147.2024.10611477. URL https://ieeexplore.ieee.org/ document/10611477. Thomas G. Diett","arxiv_match":false},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":". arXiv preprint arXiv:2304.07193, 2023. Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al. Open X-Embodiment: Robotic learning datasets and RT-X models. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 6892–6903. IEEE, 2024. Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. FiLM: Visual rea","arxiv_match":false},{"id":"ylrbHy6YvK","title":"KI-VLA: Kinematics-Informed Action Decoding for Flow-Matching Robot Policies","forum_url":"https://openreview.net/forum?id=ylrbHy6YvK","excerpt":"d Autonomous Systems, 140: 103744, 2021. Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al. Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 6892–6903. IEEE, 2024. Minho Park, Kinam Kim, Junha Hyung, Hyojin Jang, Hoiyeong Jin, ","arxiv_match":false}],"id":"2310.08864","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":233},{"title":"Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting","arxiv_id":"2305.04388","year":2023,"url":"https://arxiv.org/abs/2305.04388","citing_submissions":434,"explicit_arxiv_matches":92,"examples":[{"id":"Ytw4pWrGGW","title":"PhysioSkeptic: Skeptical Reasoning Grounded in Signal Quality for Multimodal Rhythm Analysis","forum_url":"https://openreview.net/forum?id=Ytw4pWrGGW","excerpt":"an be more effective than scaling parameters for reasoning. In The Thirteenth International Conference on Learning Representations, 2025. Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems, 36:74952–74965, 2023. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistenc","arxiv_match":false},{"id":"ZMK44fI9vu","title":"Sleep as a Window: Auditable LLM Decoding of Aging Signatures from Integrated Sleep Profiles","forum_url":"https://openreview.net/forum?id=ZMK44fI9vu","excerpt":" and prediction of chronic disease and mortality. Nature Medicine, 29(5):1221–1231, 2023. https://doi.org/10. 1038/s41591-023-02296-6. Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. In Advances in Neural Information Processing Systems, volume 36, 2023. https://doi.org/10.52202/ 075280-3275. Xidong Wang, Shuqi Guo, Yue Shen, Junying Chen, Jian Wang, Jinjie Gu, Ping Zhang, Lei Li","arxiv_match":false},{"id":"JwUvLVBgt7","title":"Do Vision-Language Models Use the Regions They Cite? A Step-Specific Intervention Test","forum_url":"https://openreview.net/forum?id=JwUvLVBgt7","excerpt":", Cheng Yang, Yaokang Wu, Linghao Jin, Bo Shui, Taylor Berg-Kirkpatrick, and Xuezhe Ma. Are VLMs seeing or just saying? uncovering the illusion of visual re-examination. arXiv preprint arXiv:2605.15864, 2026. ICML 2026. Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. In Advances in Neural Information Processing Systems, 2023. arXiv:2305.04388. Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas, Zheng Qi, and Sandesh Swamy. Journey before","arxiv_match":true}],"id":"2305.04388","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":234},{"title":"$τ^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment","arxiv_id":"2506.07982","year":2025,"url":"https://arxiv.org/abs/2506.07982","citing_submissions":433,"explicit_arxiv_matches":328,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"hou, Jing Zhou, Yuanzhi Zhu, and Ke Zhu. Qwen3-vl technical report, 2025. URL https://arxiv.org/abs/2511.21631. Rina Foygel Barber, Emmanuel J Candes, Aaditya Ramdas, and Ryan J Tibshirani. Conformal prediction beyond exchangeability. The Annals of Statistics, 51(2):816–845, 2023. Victor Barres, Honghua Dong, Soham Ray, Xujie Si, and Karthik Narasimhan. τ 2 -bench: Evaluating conversational agents in a dual-control environment, 2025. URL https://arxiv.org/abs/ 2506.07982. Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael Jordan. Distributio","arxiv_match":true},{"id":"FCnWjmOlAg","title":"PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust","forum_url":"https://openreview.net/forum?id=FCnWjmOlAg","excerpt":"//doi.org/10.48550/arXiv.2406.00799. Aslan Askarov and Andrei Sabelfeld. Gradual Release: Unifying Declassification, Encryption and Key Release Policies. In 2007 IEEE Symposium on Security and Privacy (SP ’07), 2007. doi: 10.1109/sp.2007.22. URL https://doi.org/10.1109/sp.2007.22. Victor Barres, Honghua Dong, Soham Ray, Xujie Si, and Karthik Narasimhan. τ 2 -bench: Evaluating conversational agents in a dual-control environment, 2025. URL https://arxiv.org/abs/ 2506.07982. Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, and Bo Li. AgentPoison: Red-teaming LLM Agents ","arxiv_match":true},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"ference on Learning Representations, ICLR 2026, Rio de Janeiro, Brazil, April 23-27, 2026. proceedings.iclr.cc, 2026. Victor Barres, Honghua Dong, Soham Ray, Xujie Si, and Karthik R Narasimhan. $\\tauˆ2$-bench: Evaluating conversational agents in a dual-control environment. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id= OC2z7iSQKa. Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Ro","arxiv_match":false}],"id":"2506.07982","metadata_source":"Cached arXiv API metadata","rank":235},{"title":"Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models","arxiv_id":"2510.04618","year":2025,"url":"https://arxiv.org/abs/2510.04618","citing_submissions":431,"explicit_arxiv_matches":157,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"kill evolution. arXiv preprint arXiv:2606.03143, 2026. Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, et al. Agentic context engineering: Evolving contexts for self-improving language models. In International Conference on Learning Representations, volume 2026, pp. 86069–86100, 2026. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are e","arxiv_match":false},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"national Conference on Learning Representations (ICLR), 2026. URL https://openreview.net/ forum?id=IuZoTgsUws. Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou, and Kunle Olukotun. Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models. In International Conference on Learning Representations, 2026. https://arxiv.org/abs/2510.04618. URL Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu","arxiv_match":true},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"id=z5uVAKwmjf. Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou, and Kunle Olukotun. Agentic context engineering: Evolving contexts for self-improving language models. CoRR, abs/2510.04618, 2025b. doi: 10.48550/ARXIV.2510.04618. URL https://doi.org/ 10.48550/arXiv.2510.04618. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürg","arxiv_match":false}],"id":"2510.04618","metadata_source":"Cached arXiv API metadata","rank":236},{"title":"Genie: Generative interactive environments","arxiv_id":"2402.15391","year":2024,"url":"https://arxiv.org/abs/2402.15391","citing_submissions":431,"explicit_arxiv_matches":62,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"arXiv preprint arXiv:2409.16283, 2024. Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al. Genie: Generative interactive environments. In Forty-first international conference on machine learning, 2024. Wei Cao, Marcel Hallgarten, Tianyu Li, Daniel Dauner, Xunjiang Gu, Caojun Wang, Yakov Miron, Marco Aiello, Hongyang Li, Igor Gilits","arxiv_match":false},{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":" world simulators. OpenAI Technical Report, 2024. URL https://openai. com/index/video-generation-models-as-world-simulators/. Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, et al. Genie: Generative interactive environments. In International Conference on Machine Learning, 2024. ByteDance Seed. Introducing Seedance 2.5. https://seed.bytedance.com/en/blog/ one-take-creation-flexible-referencing-introducing-seedance-2-5, ","arxiv_match":false},{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"rker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal Behbahani, Stephanie Chan, Nicolas Heess, Lucy Gonzalez, et al. Genie: Generative interactive environments. In ICML, 2024. Qingwen Bu, Yanting Yang, Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, and Hongyang Li. UniVLA: Learning to act anywhere with task-centric latent actions. In RSS, 20","arxiv_match":false}],"id":"2402.15391","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":237},{"title":"Neural discrete representation learning","arxiv_id":"1711.00937","year":2017,"url":"https://arxiv.org/abs/1711.00937","citing_submissions":431,"explicit_arxiv_matches":44,"examples":[{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"eration via next-scale prediction. In Proceedings of the Advances in Neural Information Processing Systems, volume 37, pp. 84839–84865, 2024. Aaron Van Den Oord, Oriol Vinyals, and Koray Kavukcuoglu. Neural discrete representation learning. In Proceedings of the Advances in Neural Information Processing Systems, volume 30, 2017. Ruohan Wang, Antoine Cully, Hyung Jin Chang, and Yiannis Demiris. MAGAN: Margin adaptation for generative ad","arxiv_match":false},{"id":"ugeUCOyVn0","title":"FlowTalker: Causal Streaming Audio-Driven 3D Head Animation via Factorized Motion Modeling with Rectified Flow","forum_url":"https://openreview.net/forum?id=ugeUCOyVn0","excerpt":" Imitator: Personalized speech-driven 3d facial animation. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 20621–20631, 2023. Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"g, Bin Zhu, et al. Wise: A world knowledge-informed semantic evaluation for text-to-image generation. arXiv preprint arXiv:2503.07265, 2025b. Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. Neural discrete representation learning. In NeurIPS, 2017. A Emin Orhan and Brenden M Lake. Learning high-level visual representations from a child’s perspective without strong inductive biases. Nature Machine Intelligence, 6(3):271–283, 2","arxiv_match":false}],"id":"1711.00937","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":238},{"title":"BoolQ: Exploring the surprising difficulty of natural yes/no questions","arxiv_id":"1905.10044","year":2019,"url":"https://arxiv.org/abs/1905.10044","citing_submissions":428,"explicit_arxiv_matches":54,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"er. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509, 2019. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2924–2936, 2019. Peter Clark, Isaac Cowhey, Ore","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"e perception of images. arXiv preprint arXiv:2511.22805, 2025c. Yitong Chen, Zijie Diao, Junke Wang, Lingyu Kong, Yixuan Ren, Bo He, Yu-Gang Jiang, and Zuxuan Wu. Ideal: In-depth alignment makes a discrete representation autoencoder. arXiv preprint arXiv:2606.11096, 2026. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044, 2019. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, a","arxiv_match":true},{"id":"hAJ2b6a8w1","title":"CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning","forum_url":"https://openreview.net/forum?id=hAJ2b6a8w1","excerpt":" inference acceleration for large visionlanguage models. In Computer Vision – ECCV 2024, 2024. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabha","arxiv_match":false}],"id":"1905.10044","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":239},{"title":"Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?","arxiv_id":"2504.13837","year":2025,"url":"https://arxiv.org/abs/2504.13837","citing_submissions":424,"explicit_arxiv_matches":158,"examples":[{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"ernational Conference on Learning Representations, 2025. URL https: //openreview.net/forum?id=roNSXZpUDN. Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Yang Yue, Shiji Song, and Gao Huang. Does reinforcement learning really incentivize reasoning capacity in LLMs beyond the base model? In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=4OsgYD7em5. Mert Yüksekgönül, Federico Bianchi, Joseph Boen, Sheng Liu, P","arxiv_match":false},{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":"Gaohong Liu, Lingjun Liu, et al. Dapo: An open-source LLM reinforcement learning system at scale. In NeurIPS, 2025. Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, and Gao Huang. Does reinforcement learning really incentivize reasoning capacity in LLMs beyond the base model? In NeurIPS, 2025. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah D. Goodman. STaR: Bootstrapping reasoning with reasoning. arXiv preprint arXiv:2203.14465, 2022. URL https://arxiv.org/abs/2203. 14465.","arxiv_match":false},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"to use large language models while reducing cost and improving performance. arXiv preprint arXiv:2305.05176, 2023b. Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Yang Yue, Shiji Song, and Gao Huang. Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? Advances in Neural Information Processing Systems, 38:57654–57689, 2026. Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Rühle, Laks Lakshmanan, and Ahmed H Awadallah","arxiv_match":false}],"id":"2504.13837","metadata_source":"Cached arXiv API metadata","rank":240},{"title":"HunyuanVideo: A Systematic Framework for Large Video Generative Models","arxiv_id":"2412.03603","year":2024,"url":"https://arxiv.org/abs/2412.03603","citing_submissions":423,"explicit_arxiv_matches":395,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"es in Neural Information Processing Systems, 37:52481–52515, 2024. Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou, et al. Efficient softmax approximation for gpus. In International conference on machine learning, pp. 1302–1310. PMLR, 2017. Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024. Black Forest Labs. Flux: Official inference repository for flux.1 models. https://github. co","arxiv_match":true},{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"ei Yin, Xiaotao Hu, Mingkai Jia, Junyuan Deng, Kaiwen Zhang, Yang Wu, Tianyi Yan, Shenyuan Gao, Song Wang, Linfeng Li, Liang Pan, Yong Liu, Jianke Zhu, Wei Tsang Ooi, Steven C. H. Hoi, and Ziwei Liu. 3d and 4d world modeling: A survey. arXiv preprint arXiv:2509.07996, 2025. URL https://arxiv.org/abs/2509.07996. Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, et al. HunyuanVideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024. Dacheng Li, Yunhao Fang, Yukang Chen, Shuo Yang, Shiyi Cao, Justin Wong, Michael Luo, Xiaolo","arxiv_match":true},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"Analyzing and improving the training dynamics of diffusion models. In CVPR, 2024. Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. Diederik P Kingma and Ruiqi Gao. Understanding diffusion objectives as the elbo with simple data augmentation. In NeurIPS, 2023. Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2025. Jiachen Lei, Keli Liu, Julius Berner, Haiming Yu, Hongkai Zheng, Jiahong Wu, and Xiangxiang ","arxiv_match":true}],"id":"2412.03603","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":241},{"title":"World Models","arxiv_id":"1803.10122","year":2018,"url":"https://arxiv.org/abs/1803.10122","citing_submissions":423,"explicit_arxiv_matches":371,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"nd Machine Intelligence, 44(10):6024–6042, 2022. Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, and Yann LeCun. Learning and leveraging world models in visual representation learning. arXiv preprint arXiv:2403.00504, 2024. David Ha and Jürgen Schmidhuber. Recurrent world models facilitate policy evolution. Advances in neural information processing systems, 31, 2018a. David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018b. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learni","arxiv_match":true},{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"9669, 2026. Howard Gardner. Frames of mind: The theory of multiple intelligences. Basic Books, 2011. Google. Gemini 3 Flash: Frontier intelligence built for speed, 2025. URL https://blog. google/products-and-platforms/products/gemini/gemini-3-flash/. Google. Gemini 3.5: Frontier intelligence with action, 2026. URL https://blog.google/ innovation-and-ai/models-and-research/gemini-models/gemini-3-5/. David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018. Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and J","arxiv_match":true},{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":"urabh Kumar, Jacob Buckman, Ofir Nachum, and Marc G. Bellemare. DeepMDP: Learning continuous latent space models for representation learning. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 2170–2179. PMLR, 2019. URL https://proceedings.mlr.press/v97/gelada19a.html. David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018. URL https://arxiv.org/abs/1803.10122. Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James D","arxiv_match":true}],"id":"1803.10122","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":242},{"title":"Root mean square layer normalization","arxiv_id":"1910.07467","year":2019,"url":"https://arxiv.org/abs/1910.07467","citing_submissions":422,"explicit_arxiv_matches":81,"examples":[{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush. Huggingface’s transformers: State-of-the-art natural language processing, 2019. B. Zhang and R. Sennrich. Root mean square layer normalization, 2019. J. Zhao, Z. Zhang, B. Chen, Z. Wang, A. Anandkumar, and Y. Tian. GaLore: Memory-efficient LLM training by gradient low-rank projection. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Olive","arxiv_match":false},{"id":"FumWWB5wLP","title":"Beyond the Estimate: Wiener-Informed Decoding for Easier Training of Pixel-Space Flow Models","forum_url":"https://openreview.net/forum?id=FumWWB5wLP","excerpt":". In ICLR, 2025. Yongsheng Yu, Wei Xiong, Weili Nie, Yichen Sheng, Shiqiu Liu, and Jiebo Luo. Pixeldit: Pixel diffusion transformers for image generation. In CVPR, 2026. Biao Zhang and Rico Sennrich. Root mean square layer normalization. In NeurIPS, 2019. Lei Zhang, Weisheng Dong, David Zhang, and Guangming Shi. Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognition, 43(4):1531–1549,","arxiv_match":false},{"id":"K9wqTXAYns","title":"LLARA: Low-Rank Linear Attention Residual Adaptation for Large Language Models","forum_url":"https://openreview.net/forum?id=K9wqTXAYns","excerpt":"an Weller, and Weiyang Liu. MetaMath: Bootstrap your own mathematical questions for large language models. In International Conference on Learning Representations, 2024. Biao Zhang and Rico Sennrich. Root mean square layer normalization. In Advances in Neural Information Processing Systems, 2019. Renrui Zhang, Jiaming Han, Chris Liu, Aojun Zhou, Pan Lu, Yu Qiao, Hongsheng Li, and Peng Gao. LLaMA-adapter: Efficient fine-tuning of lar","arxiv_match":false}],"id":"1910.07467","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":243},{"title":"Automated design of agentic systems","arxiv_id":"2408.08435","year":2024,"url":"https://arxiv.org/abs/2408.08435","citing_submissions":420,"explicit_arxiv_matches":123,"examples":[{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"Keller. Sionna: An open-source library for next-generation physical layer research. arXiv preprint arXiv:2203.11854, 2022. URL https://arxiv.org/abs/2203. 11854. Shengran Hu, Cong Lu, and Jeff Clune. Automated design of agentic systems. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview. net/forum?id=Joc8ecV2im. Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec. MLAgentBench: Ev","arxiv_match":false},{"id":"JmKCJSrhxX","title":"FlowCompile: An Optimizing Compiler for Structured LLM Workflows","forum_url":"https://openreview.net/forum?id=JmKCJSrhxX","excerpt":"uting. https://github.com/ulab-uiuc/ LLMRouter, 2025. GitHub repository. Conor F Hayes, Roxana Rădulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M Zintgraf, Richard Dazeley, Fredrik Heintz, et al. A practical guide to multi-objective reinforcement learning and planning. arXiv preprint arXiv:2103.09568, 2021. Shengran Hu, Cong Lu, and Jeff Clune. Automated design of agentic systems. arXiv preprint arXiv:2408.08435, 2024. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando So","arxiv_match":true},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"enchmarking agent self-evolution via ability transfer. CoRR, abs/2607.05202, 2026. doi: 10.48550/ARXIV.2607.05202. URL https://doi.org/10.48550/arXiv.2607.05202. Shengran Hu, Cong Lu, and Jeff Clune. Automated design of agentic systems. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. URL https://openreview.net/forum?id=t9U3LW7JVX. Wenqi Huang, Ch","arxiv_match":false}],"id":"2408.08435","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":244},{"title":"Depth Anything 3: Recovering the Visual Space from Any Views","arxiv_id":"2511.10647","year":2025,"url":"https://arxiv.org/abs/2511.10647","citing_submissions":418,"explicit_arxiv_matches":282,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"ps://huggingface.co/Lightricks/LTX-2.5, 2026. Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen, Zhenyu Li, Yang Zhao, Sida Peng, Hengkai Guo, Xiaowei Zhou, Guang Shi, Jiashi Feng, and Bingyi Kang. Depth anything 3: Recovering the visual space from any views. In International Conference on Learning Representations, 2026. URL https://proceedings.iclr.cc/paper files/paper/2026/hash/ e4cd50120b6d7e8daff1749d6bbaa889-Abstract-Conference.html. Yaofang Liu, Xi","arxiv_match":false},{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":"Lin, Jingwei Huang, Chunchao Guo, and Xiangyu Yue. Lattice: Democratize high-fidelity 3d generation at scale. arXiv preprint arXiv:2512.03052, 2025. Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r, 2024. URL https://arxiv.org/abs/2406.09756. Haotong Lin, Sili Chen, Junhao Liew, Donny Y Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang. Depth anything 3: Recovering the visual space from any views. arXiv preprint arXiv:2511.10647, 2025. Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. One-2","arxiv_match":true},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"arning, pp. 19730–19742. PMLR, 2023a. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. In Proceedings of the 2023 conference on empirical methods in natural language processing, pp. 292–305, 2023b. Haotong Lin, Sili Chen, Junhao Liew, Donny Y Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang. Depth anything 3: Recovering the visual space from any views. arXiv preprint arXiv:2511.10647, 2025a. Tao Lin, Gen Li, Yilei Zhong, Yanwen Zou, Yuxin Du, Jiting Liu, Encheng Gu, and Bo Zhao. Ev","arxiv_match":true}],"id":"2511.10647","metadata_source":"Cached arXiv API metadata","rank":245},{"title":"Scaling laws for reward model overoptimization","arxiv_id":"2210.10760","year":2022,"url":"https://arxiv.org/abs/2210.10760","citing_submissions":417,"explicit_arxiv_matches":68,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"ao Li, Yinghang Song, Fei Ding, Jianzhu Guo, Qian He, et al. Lance: Unified multimodal modeling by multi-task synergy. arXiv preprint arXiv:2605.18678, 2026. Leo Gao, John Schulman, and Jacob Hilton. Scaling laws for reward model overoptimization. In International conference on machine learning, pp. 10835–10866. PMLR, 2023. Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the v in vqa matter: Elevating the ro","arxiv_match":false},{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":"Jiao, Banghua Zhu, Joseph E. Gonzalez, and Ion Stoica. How to evaluate reward models for RLHF. In International Conference on Learning Representations, 2025. Leo Gao, John Schulman, and Jacob Hilton. Scaling laws for reward model overoptimization. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 10835–10866. PMLR, 2023. Chuan Guo, Geoff Pleiss, Yu Sun, and Kil","arxiv_match":false},{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":"Xiv:2110.14168, 2021. DeepSeek-AI. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Leo Gao, John Schulman, and Jacob Hilton. Scaling laws for reward model overoptimization. In Proceedings of the 40th International Conference on Machine Learning, pp. 10835–10866. PMLR, 2023. Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo, Jiarui Yu, Hande Dong, Qiang Lin, Can Wang, and ","arxiv_match":false}],"id":"2210.10760","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":246},{"title":"Adding conditional control to text-to-image diffusion models","arxiv_id":"2302.05543","year":2023,"url":"https://arxiv.org/abs/2302.05543","citing_submissions":414,"explicit_arxiv_matches":42,"examples":[{"id":"MpNPoDTf3B","title":"Semantic Fields Are Interfaces: Consumer-Typed Functional Migration for Representation Autoencoders","forum_url":"https://openreview.net/forum?id=MpNPoDTf3B","excerpt":"hen, Shupeng Su, Fanzi Wu, Chun Yuan, Xuyuan Xu, Yexin Wang, and Ying Shan. Towards universal backward-compatible representation learning. In IJCAI, 2022. Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In ICCV, 2023. Qifeng Zhang, Ting Xiang, Zeyuan Bai, and Changjian Chen. Semantically compatible knowledge distillation for cross-domain object detection with vision foundation models. arXiv preprin","arxiv_match":false},{"id":"V4v6UxsjUg","title":"UniCTPET: Unifying 3D CT and PET Generation through Adaptive Structural Exchange","forum_url":"https://openreview.net/forum?id=V4v6UxsjUg","excerpt":"PET/CT synthesis from demographics. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 99–109. Springer, 2025. Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In IEEE/CVF International Conference on Computer Vision, pp. 3813–3824. IEEE, 2023. A ","arxiv_match":false},{"id":"aocYTIRnhk","title":"GeoVerse: World-Consistent Novel View Synthesis in Geometric Latent Space","forum_url":"https://openreview.net/forum?id=aocYTIRnhk","excerpt":"ng Zhao. Fast-wam: Do world action models need test-time future imagination? arXiv preprint arXiv:2603.16666, 2026. URL https: //arxiv.org/abs/2603.16666. Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3813–3824. IEEE, 2023. Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effecti","arxiv_match":false}],"id":"2302.05543","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":247},{"title":"Gradient surgery for multi-task learning","arxiv_id":"2001.06782","year":2020,"url":"https://arxiv.org/abs/2001.06782","citing_submissions":414,"explicit_arxiv_matches":41,"examples":[{"id":"AFB7bSu4ee","title":"Gradient–Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks","forum_url":"https://openreview.net/forum?id=AFB7bSu4ee","excerpt":"learning. In proceedings of the AAAI conference on artificial intelligence, volume 35, pp. 10665–10673, 2021. Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multi-task learning. Advances in neural information processing systems, 33: 5824–5836, 2020. Weiwei Zhang, Wei Suo, Jiahao Song, and Wenbo Cao. Physics-informed neural networks (pinns) as intelligent computing technique","arxiv_match":false},{"id":"4AEJkVBst5","title":"What Should Consistency Preserve? Readout-Geometric Information Transport for Semi-Supervised Regression","forum_url":"https://openreview.net/forum?id=4AEJkVBst5","excerpt":"augmentation for consistency training. Advances in neural information processing systems, 33:6256–6268, 2020. Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multi-task learning. Advances in neural information processing systems, 33: 5824–5836, 2020. Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016. Bowen Zhang, Yidong Wang,","arxiv_match":false},{"id":"12OrE4miLg","title":"Field Constrained Bargaining DiffusionNFT","forum_url":"https://openreview.net/forum?id=12OrE4miLg","excerpt":" dynamic preference adjustment. In International Conference on Machine Learning, pp. 56276–56297. PMLR, 2024. Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multi-task learning. NIPS ’20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713829546. Qi Zhang, Dawei Wang, and Shaofeng Zou. Step-level denoising-time diffusion alignment with multiple objectives. arXiv pr","arxiv_match":false}],"id":"2001.06782","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":248},{"title":"One-step diffusion with distribution matching distillation","arxiv_id":"2311.18828","year":2023,"url":"https://arxiv.org/abs/2311.18828","citing_submissions":414,"explicit_arxiv_matches":47,"examples":[{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"ast image synthesis. In NeurIPS, 2024a. URL https://openreview.net/forum?id=tQukGCDaNT. Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Frédo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In CVPR, 2024b. Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, and Thomas Huang. Wide activation for efficient and accurate image super-resolution. arXiv preprint arXiv:1","arxiv_match":false},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"age synthesis. Advances in neural information processing systems, 37:47455–47487, 2024a. Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6613–6623, 2024b. Tianwei Yin, Qiang Zhang, Richard Zhang, William T. Freeman, Fredo Durand, Eli Shechtman, ","arxiv_match":false},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"ion with deep sink and participative compression. arXiv preprint arXiv:2512.05081, 2025. Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6613–6623. IEEE, 2024. Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and","arxiv_match":false}],"id":"2311.18828","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":249},{"title":"Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion","arxiv_id":"2506.08009","year":2025,"url":"https://arxiv.org/abs/2506.08009","citing_submissions":413,"explicit_arxiv_matches":139,"examples":[{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"Jonathan Heek, and Tim Salimans. Simple diffusion: End-to-end diffusion for high resolution images. In ICML, pp. 13213–13232, 2023. Xun Huang, Zhengqi Li, Guande He, Mingyuan Zhou, and Eli Shechtman. Self forcing: Bridging the train-test gap in autoregressive video diffusion. In NeurIPS, 2025. Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite ","arxiv_match":false},{"id":"Zf68SX9abN","title":"S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation","forum_url":"https://openreview.net/forum?id=Zf68SX9abN","excerpt":"ip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https://arxiv.org/abs/2106. 09685. Xun Huang, Zhengqi Li, Guande He, Mingyuan Zhou, and Eli Shechtman. Self forcing: Bridging the train-test gap in autoregressive video diffusion. In Advances in Neural Information Processing Systems, 2025. URL https://arxiv.org/abs/2506.08009. Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu","arxiv_match":true},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"Ren, et al. Matrix-game 2.0: An open-source real-time and streaming interactive world model. arXiv preprint arXiv:2508.13009, 2025. Xun Huang, Zhengqi Li, Guande He, Mingyuan Zhou, and Eli Shechtman. Self forcing: Bridging the train-test gap in autoregressive video diffusion. In Advances in Neural Information Processing Systems, 2025. Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et ","arxiv_match":false}],"id":"2506.08009","metadata_source":"Cached arXiv API metadata","rank":250},{"title":"AFlow: Automating agentic workflow generation","arxiv_id":"2410.10762","year":2024,"url":"https://arxiv.org/abs/2410.10762","citing_submissions":411,"explicit_arxiv_matches":90,"examples":[{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"053–3077, 2024. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, and Chenglin Wu. Aflow: Automating agentic workflow generation. In Proceedings of The Thirteenth International Conference on Learning Representations, 2025. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are e","arxiv_match":false},{"id":"JmKCJSrhxX","title":"FlowCompile: An Optimizing Compiler for Structured LLM Workflows","forum_url":"https://openreview.net/forum?id=JmKCJSrhxX","excerpt":"76-251-0. doi: 10.18653/v1/2025.acl-long.757. URL https://aclanthology. org/2025.acl-long.757/. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. Multi-agent architecture search via agentic supernet. arXiv preprint arXiv:2502.04180, 2025. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, et al. Aflow: Automating agentic workflow generation. arXiv preprint arXiv:2410.10762, 2024. Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun","arxiv_match":true},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"Xiv.2603.19461. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, and Chenglin Wu. Aflow: Automating agentic workflow generation. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025a. URL https://openreview.net/forum?id=z5uVAKwmjf. Qizheng Zhang,","arxiv_match":false}],"id":"2410.10762","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":251},{"title":"ImageReward: Learning and evaluating human preferences for text-to-image generation","arxiv_id":"2304.05977","year":2023,"url":"https://arxiv.org/abs/2304.05977","citing_submissions":411,"explicit_arxiv_matches":44,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"t streaming language models with attention sinks, 2024. URL https://arxiv. org/abs/2309.17453, 1, 2024. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36:15903–15935, 2023. Jiazheng Xu, Yu Huang, Jiale Cheng, Yuanming Yang, Jiajun Xu, Yuan Wang, Wenbo Duan, Shen Yang, Qunlin Jin, Shurun Li, et al.","arxiv_match":false},{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"ark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341, 2023. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36:15903–15935, 2023. Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al","arxiv_match":false},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":" Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):13941–13958, 2023a. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. In NeurIPS, 2023b. Shuai Yang, Wei Huang, Ruihang Chu, Yicheng Xiao, Yuyang Zhao, Xianbang Wang, Muyang Li, Enze Xie, Yingcong Chen, Yao Lu, Song Han, and Yukang Chen. LongLive: Real-Time Interactiv","arxiv_match":false}],"id":"2304.05977","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":252},{"title":"Constitutional AI: Harmlessness from AI Feedback","arxiv_id":"2212.08073","year":2022,"url":"https://arxiv.org/abs/2212.08073","citing_submissions":405,"explicit_arxiv_matches":381,"examples":[{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":" Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. Advances in Neural Information Processing Systems, 37:136037–136083, 2024. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022. Jan Betley, Daniel Tan, Niels Warncke, Anna Sztyber-Betley, Xuchan Bao, Martín Soto, Nathan ","arxiv_match":true},{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":" Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mane. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565, 2016. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073, 2022. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, E","arxiv_match":true},{"id":"UrDgYAs0MP","title":"When Good Verifiers Go Bad: Silent Negative Transfer in Verifier-Guided VLM Training","forum_url":"https://openreview.net/forum?id=UrDgYAs0MP","excerpt":" Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073, 2022. Eugene Choi, Arash Ahmadian, Matthieu Geist, Olivier Pietquin, and Mohammad Gheshlaghi Azar.","arxiv_match":true}],"id":"2212.08073","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":253},{"title":"Geneval: An object-focused framework for evaluating text-to-image alignment","arxiv_id":"2310.11513","year":2023,"url":"https://arxiv.org/abs/2310.11513","citing_submissions":405,"explicit_arxiv_matches":66,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":" et al. X-omni: Reinforcement learning makes discrete autoregressive image generative models great again. arXiv preprint arXiv:2507.22058, 2025. Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. In NeurIPS, 2023. Zipeng Guo, Lichen Ma, Yu He, Xiaolong Fu, Jingling Fu, Junshi Huang, and Yan Li. PixelU: A U-shaped transformer for efficient end-to-end pixel diffusion. arXiv preprint arXiv:2606","arxiv_match":false},{"id":"qNhfYunjC7","title":"FASE: Factor-Aware Spectral Evolution for Gradient-Free Continuation of Diffusion Post-Training","forum_url":"https://openreview.net/forum?id=qNhfYunjC7","excerpt":" al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine learning, 2024. Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems, 36: 52132–52152, 2023. Yu Gu, Zhi Zheng, Yunpeng Ba, Xialiang Tong, Mingxuan Yuan, and Zhenkun Wang. Hyper-es: Effective evolution strategies for l","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":" et al. X-omni: Reinforcement learning makes discrete autoregressive image generative models great again. arXiv preprint arXiv:2507.22058, 2025. Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems, 36: 52132–52152, 2023. GPT4o. gpt4o, 2024. Ming Gui, Johannes Schusterbauer, Timy Phan, Felix Krause, Josh Susskind, Miguel Angel Bautista, and Bjö","arxiv_match":false}],"id":"2310.11513","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":254},{"title":"G-Eval: NLG evaluation using GPT-4 with better human alignment","arxiv_id":"2303.16634","year":2023,"url":"https://arxiv.org/abs/2303.16634","citing_submissions":404,"explicit_arxiv_matches":48,"examples":[{"id":"hVPI1fy02w","title":"AVE-Compass: Towards Holistic Evaluation for Audio-Video Editing Abilities","forum_url":"https://openreview.net/forum?id=hVPI1fy02w","excerpt":"nguage Processing, 33:458–471, 2025. doi: 10.1109/TASLP.2024.3520017. URL https://ieeexplore.ieee.org/document/10819011/. Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. G-eval: Nlg evaluation using gpt-4 with better human alignment. In Proceedings of the 2023 conference on empirical methods in natural language processing, pp. 2511–2522, 2023b. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe,","arxiv_match":false},{"id":"QLMHtA7hoi","title":"Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection","forum_url":"https://openreview.net/forum?id=QLMHtA7hoi","excerpt":"uning. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 26286–26296. IEEE, 2024. [17] Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. Geval: Nlg evaluation using gpt-4 with better human alignment. In Proceedings of the 2023 conference on empirical methods in natural language processing, pages 2511–2522, 2023. [18] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv ","arxiv_match":false},{"id":"DkgHpC6dOc","title":"The Other Side of Answering: How Language Models Respond Beyond Direct Answers","forum_url":"https://openreview.net/forum?id=DkgHpC6dOc","excerpt":" and a benchmark for reliable interactive clinical reasoning. In Advances in Neural Information Processing Systems, 2024. Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. G-eval: NLG evaluation using GPT-4 with better human alignment. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 2511–2522, Singapore, 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.emnlp-main","arxiv_match":false}],"id":"2303.16634","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":255},{"title":"Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection","arxiv_id":"2303.05499","year":2023,"url":"https://arxiv.org/abs/2303.05499","citing_submissions":404,"explicit_arxiv_matches":76,"examples":[{"id":"EZ7FN3iHms","title":"ConfluxCount: Forming and Enumerating Semantic Confluences for Text-Guided Object Counting","forum_url":"https://openreview.net/forum?id=EZ7FN3iHms","excerpt":"nd More. In Int. Conf. Comput. Vis., pp. 1676–1685, 2023. Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, and Lei Zhang. Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection. In European Conference on Computer Vision, pp. 38–55. Springer, 2024. doi: 10.1007/978-3-031-72970-6_3. Shuai Liu, Peng Zhang, Shiwei Zhang, and Wei Ke. CountSE: Soft exemplar open-set object counti","arxiv_match":false},{"id":"mwyLZ7hFam","title":"AffectVBench: Benchmarking Affective Faithfulness in Long-Form Text-to-Video Generation","forum_url":"https://openreview.net/forum?id=mwyLZ7hFam","excerpt":" long video diffusion in real time. arXiv preprint arXiv:2509.25161, 2025. Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European conference on computer vision, pp. 38–55. Springer, 2024. Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swin transformer. In 2022 IEEE/CVF conference","arxiv_match":false},{"id":"CquxtFcjgG","title":"Reasoning-DETR: Contextual and Relational Query Learning for Aerial Object Detection","forum_url":"https://openreview.net/forum?id=CquxtFcjgG","excerpt":"resentations, 2022. URL https://openreview.net/forum?id= oMI9PjOb9Jl. Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, and Lei Zhang. Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection. In European Conference on Computer Vision, 2024. Akhil Meethal, Eric Granger, and Marco Pedersoli. Cascaded zoom-in detector for high resolution aerial images. In Proceedings of the IEEE/CVF Confere","arxiv_match":false}],"id":"2303.05499","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":256},{"title":"Solving quantitative reasoning problems with language models","arxiv_id":"2206.14858","year":2022,"url":"https://arxiv.org/abs/2206.14858","citing_submissions":404,"explicit_arxiv_matches":90,"examples":[{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"1-6, 2023, pp. 3992–4003. IEEE, 2023. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al. Solving quantitative reasoning problems with language models. Advances in neural information processing systems, 35:3843–3857, 2022. Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Brecq: Pushing the limit of pos","arxiv_match":false},{"id":"BTTuPGcoJL","title":"Learning from Correctness Trends: Sample-Wise Historical Advantage Shaping for Mathematical Reasoning","forum_url":"https://openreview.net/forum?id=BTTuPGcoJL","excerpt":"illation, 2025. Accessed: 2025-01-22. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al. Solving quantitative reasoning problems with language models. Advances in neural information processing systems, 35:3843–3857, 2022. Ziheng Li, Liu Kang, Feng Xiao, Luxi Xing, Qingyi Si, Zhuoran Li, Weikang Gong, Deqing Yang, Yanghua Xiao, and Hongcheng Guo. O","arxiv_match":false},{"id":"1TfY9hAQoJ","title":"Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening","forum_url":"https://openreview.net/forum?id=1TfY9hAQoJ","excerpt":"LLMs. In International Conference on Machine Learning, 2025. URL https://arxiv.org/abs/2410.01679. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra. Solving quantitative reasoning problems with language models. Advances in Neural Information Processing Systems, 35, 2022. URL https: //arxiv.org/abs/2206.14858. Chenxi Li, Wenxuan Zeng, Yun Luo, Fangchen Yu, Peng Ye, Yu Cheng, and Jun Zhang. SciWalker: Synthe","arxiv_match":true}],"id":"2206.14858","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":257},{"title":"TruthfulQA: Measuring how models mimic human falsehoods","arxiv_id":"2109.07958","year":2021,"url":"https://arxiv.org/abs/2109.07958","citing_submissions":404,"explicit_arxiv_matches":50,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"guage models through multiagent debate. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 17889–17904, 2024. Stephanie Lin, Jacob Hilton, and Owain Evans. Truthfulqa: Measuring how models mimic human falsehoods. In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), pp. 3214–3252, 2022. Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, a","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":" and Xia Hu. KIVI: A tuning-free asymmetric 2bit quantization for KV cache. In International Conference on Machine Learning, pp. 32332–32344. PMLR, 2024. Stephanie Lin, Jacob Hilton, and Owain Evans. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3214–3252, 2022. Enzhe Lu, Zhejun Jiang, Jingyuan Liu, Yulun Du, Tao Jiang, Ch","arxiv_match":false},{"id":"9cpSUDXx9d","title":"LLM Uncertainty Revealed in Behavioral Boundary Effects","forum_url":"https://openreview.net/forum?id=9cpSUDXx9d","excerpt":"earch. Transactions of the Association for Computational Linguistics, 7:452–466, 2019. doi: 10.1162/tacl_a_00276. URL https://aclanthology.org/Q19-1026/. Stephanie Lin, Jacob Hilton, and Owain Evans. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3214–3252, Dublin, Ireland, 2022. Association for Computational Linguistics. d","arxiv_match":false}],"id":"2109.07958","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":258},{"title":"Smoothquant: Accurate and efficient post-training quantization for large language models","arxiv_id":"2211.10438","year":2022,"url":"https://arxiv.org/abs/2211.10438","citing_submissions":403,"explicit_arxiv_matches":38,"examples":[{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"eration. In Proceedings of the 2021 conference on empirical methods in natural language processing, pp. 8696–8708, 2021. Guangxuan Xiao, Ji Lin, Mickaël Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, Proceedings of Machine Learning Research, pp. 38087–38099. PMLR, 2023. Bo Xu, Haotian Wu, Hehai Li","arxiv_match":false},{"id":"LxwIDZer16","title":"When Cleaner Caches Break: Training Recipes Shape MLA Cache Quantizability","forum_url":"https://openreview.net/forum?id=LxwIDZer16","excerpt":"k Bergemann, and Zhuoran Yang. Why muon outperforms adam: A curvature perspective. arXiv preprint arXiv:2606.04662, 2026. Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. SmoothQuant: Accurate and efficient post-training quantization for large language models. In ICML, 2023. Amir Zandieh, Majid Daliri, Majid Hadian, and Vahab Mirrokni. TurboQuant: Online vector quantization with near-optimal distortion rate. ICLR, 2026. A C ACHE SCHEDULE AND LOSS BASELINE","arxiv_match":false},{"id":"k9j0YWUVI2","title":"AlignQuant: Tile-Aligned Mixed-Precision Quantization for Efficient LLM Generation","forum_url":"https://openreview.net/forum?id=k9j0YWUVI2","excerpt":"hi Yu, Lu Hou, Chun Yuan, et al. Flatquant: Flatness matters for llm quantization. arXiv preprint arXiv:2410.09426, 2024. Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International conference on machine learning, pp. 38087–38099. PMLR, 2023. Xilong Xie, Liang Wang, Limin Xiao, Meng Han, Lin Sun, Shuai Zheng, and Xiangrong Xu. Fineq: Software-hardware co-design","arxiv_match":false}],"id":"2211.10438","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":259},{"title":"AppWorld: A controllable world of apps and people for benchmarking interactive coding agents","arxiv_id":"2407.18901","year":2024,"url":"https://arxiv.org/abs/2407.18901","citing_submissions":399,"explicit_arxiv_matches":54,"examples":[{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":"4.11703, 2025. Jeremy Tien, Abishek Anand, Yu-Rou Tuan, Yuchen Shen, J Zico Kolter, and Aran Nayebi. Rogue: Misaligned agent behavior arising from ordinary computer use. arXiv preprint arXiv:2606.00341, 2026. Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, and Niranjan Balasubramanian. AppWorld: A controllable world of apps and people for benchmarking interactive coding agents. arXiv preprint arXiv:2407.18901, 2024. Sanidhya Vijayvargiya, Aditya Bharat Soni, Xuhui Zhou, Zora Zhiruo Wang, Nouha Dziri, Graham","arxiv_match":true},{"id":"9N1uPPmr4W","title":"Shared Resources Distort Credit Assignment in Group-Relative Agent Reinforcement Learning","forum_url":"https://openreview.net/forum?id=9N1uPPmr4W","excerpt":"ral Information Processing Systems, volume 28, 2015. Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, and Niranjan Balasubramanian. AppWorld: A controllable world of apps and people for benchmarking interactive coding agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 16022–16076, 2024. Tao Wang, Suhang Zheng, and Xiaoxiao Xu. RTMC: Step-level c","arxiv_match":false},{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":"A\\_00475. URL https://doi.org/10.1162/tacl_a_ 00475. Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, and Niranjan Balasubramanian. Appworld: A controllable world of apps and people for benchmarking interactive coding agents. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand,","arxiv_match":false}],"id":"2407.18901","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":260},{"title":"Td-mpc2: Scalable, robust world models for continuous control","arxiv_id":"2310.16828","year":2023,"url":"https://arxiv.org/abs/2310.16828","citing_submissions":397,"explicit_arxiv_matches":62,"examples":[{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 8387–8406. PMLR, 2022. URL https: //proceedings.mlr.press/v162/hansen22a.html. Nicklas Hansen, Hao Su, and Xiaolong Wang. TD-MPC2: Scalable, robust world models for continuous control. In International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Oxh5CstDJU. Wassily Hoeffding. Probability inequalities for sums of bounded random variables. Journ","arxiv_match":false},{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":"ream to control: Learning behaviors by latent imagination. International Conference on Learning Representations, 2020. URL https://arxiv.org/abs/1912.01603. Nicklas Hansen, Hao Su, and Xiaolong Wang. TD-MPC2: Scalable, robust world models for continuous control. In International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Oxh5CstDJU. Nicklas A. Hansen, Hao Su, and Xiaolong Wang. Temporal difference learning for model pr","arxiv_match":false},{"id":"itoxkUzY2n","title":"MEND: Measuring and Constraining Latent Dynamics in World Models","forum_url":"https://openreview.net/forum?id=itoxkUzY2n","excerpt":"2020. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse control tasks through world models. Nature, 640(8059):647–653, 2025. Nick Hansen, Hao Su, and Xiaolong Wang. Td-mpc2: Scalable, robust world models for continuous control. In International Conference on Learning Representations (ICLR), volume 2024, pp. 47376–47405, 2024. Maziar S Hemati, Clarence W Rowley, Eric A Deem, and Louis N Cattafesta. De-biasing the dynamic mo","arxiv_match":false}],"id":"2310.16828","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":261},{"title":"The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models","arxiv_id":null,"year":2025,"url":"https://proceedings.mlr.press/v267/patil25a.html","citing_submissions":396,"explicit_arxiv_matches":0,"examples":[{"id":"o7ODDwTMgU","title":"Valid but Wrong: Decomposing Language-Model Errors on Closed Vocabularies","forum_url":"https://openreview.net/forum?id=o7ODDwTMgU","excerpt":" language model connected with massive apis. arXiv preprint arXiv:2305.15334, 2023. Shishir G. Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E. Gonzalez. The berkeley function calling leaderboard (BFCL): From tool use to agentic evaluation of large language models. In Proceedings of the 42nd International Conference on Machine Learning, 2025. Gustavo Correa Publio and José Emilio Labra Gayo. xpshacl: Explainable shacl validation using retrieval-augmented gener","arxiv_match":false},{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"/paper_files/paper/2022/fi le/b1efde53be364a73914f58805a001731-Paper-Conference.pdf. Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E. Gonzalez. The Berkeley Function Calling Leaderboard (BFCL): From tool use to agentic evaluation of large language models. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 48371– 48392. PMLR, 2025. URL https://proceedings.mlr.press/v267/","arxiv_match":false},{"id":"4ihDGJ5CAY","title":"What Early Stopping Hides: Cost-Aware Auditing in Post-Training","forum_url":"https://openreview.net/forum?id=4ihDGJ5CAY","excerpt":"g datasets. Advances in Neural Information Processing Systems, 37:54463– 54482, 2024. Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E Gonzalez. The berkeley function calling leaderboard (BFCL): From tool use to agentic evaluation of large language models. In Forty-second International Conference on Machine Learning, 2025. Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Hu","arxiv_match":false}],"id":"ca40becc28a4caa0","metadata_source":"Curated bibliography catalog / publisher metadata","rank":262},{"title":"Fast-WAM: Do World Action Models Need Test-time Future Imagination?","arxiv_id":"2603.16666","year":2026,"url":"https://arxiv.org/abs/2603.16666","citing_submissions":391,"explicit_arxiv_matches":378,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"uanzhi Wang, Fengyuan Hu, Avnish Narayan, Johan Bjorck, Jing Wang, Gwanghyun Kim, Dantong Niu, Ruijie Zheng, Yuqi Xie, Jimmy Wu, Qi Wang, Ryan Julian, Danfei Xu, Yilun Du, Yevgen Chebotar, Scott Reed, Jan Kautz, Yuke Zhu, Linxi ”Jim” Fan, and Joel Jang. World action models are zero-shot policies, 2026. URL https://arxiv.org/abs/2602.15922. Tianyuan Yuan et al. Fast-wam: Do world action models need test-time future imagination?, 2026. URL https://arxiv.org/abs/2603.16666. Junyi Zhang, Jiaxin Ge, Hanjun Yoo, Letian Fu, Zihan Yang, Yaowei Liu, Raj Saravanan, Shaofeng Yin","arxiv_match":true},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"21474. Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng, Shenyuan Gao, Sihyun Yu, George Kurian, Suneel Indupuru, You Liang Tan, Chuning Zhu, Jiannan Xiang, et al. World action models are zero-shot policies. arXiv preprint arXiv:2602.15922, 2026. URL https://arxiv.org/abs/2602.15922. Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-WAM: Do world action models need test-time future imagination? arXiv preprint arXiv:2603.16666, 2026. URL https://arxiv.org/abs/2603.16666. Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, and Huazhe Xu. 3D Diffusion Policy: ","arxiv_match":true},{"id":"FZ7Aq4uPre","title":"UniWAM: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision","forum_url":"https://openreview.net/forum?id=FZ7Aq4uPre","excerpt":"le manipulation. arXiv preprint arXiv:2306.11565, 2023. Haoqi Yuan, Zhixuan Liang, Anzhe Chen, Ye Wang, Haoyang Li, Pei Lin, Yiyang Huang, Zixing Lei, Tong Zhang, Jiazhao Zhang, et al. Qwen-RobotManip technical report: Alignment unlocks scale for robotic manipulation foundation models. arXiv preprint arXiv:2606.17846, 2026a. Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-WAM: Do world action models need test-time future imagination? arXiv preprint arXiv:2603.16666, 2026b. Jiazhao Zhang, Kunyu Wang, Shaoan Wang, Minghan Li, Haoran Liu, Songlin Wei, Zhongyuan Wang","arxiv_match":true}],"id":"2603.16666","metadata_source":"Cached arXiv API metadata","rank":263},{"title":"Sequence-level knowledge distillation","arxiv_id":"1606.07947","year":2016,"url":"https://arxiv.org/abs/1606.07947","citing_submissions":390,"explicit_arxiv_matches":32,"examples":[{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"and efficient sparsity. J. Mach. Learn. Res., 23:120:1–120:39, 2022. URL https://jmlr.org/papers/v23/21-0998.html. Haoyu He, Katrin Renz, Yong Cao, and Andreas Geiger. MDPO: overcoming the training-inference divide of masked diffusion language models. CoRR, abs/2508.13148, 2025. doi: 10.48550/ARXIV. 2508.13148. URL https://doi.org/10.48550/arXiv.2508.13148. Yoon Kim and Alexander M. Rush. Sequence-level knowledge distillation, 2016. URL https: //arxiv.org/abs/1606.07947. Shufan Li, Konstantinos Kallidromitis, Hritik Bansal, Akash Gokul, Yusuke Kato, Kazuki Kozuka, Jas","arxiv_match":true},{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"elf-distillation (sometimes) degrade the reasoning capability of LLMs? In Third Conference on Language Modeling, 2026. URL https://openreview. net/forum?id=Az7guis46K. Yoon Kim and Alexander M. Rush. Sequence-level knowledge distillation. In Jian Su, Kevin Duh, and Xavier Carreras (eds.), Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1317–1327, Austin, Texas, November 2016. Association fo","arxiv_match":false},{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"a Lapata. When is routing meaningful? diversity and robustness in language model societies. In arXiv preprint arXiv:2607.09197, 2026. https://arxiv.org/abs/2607.09197. Yoon Kim and Alexander M. Rush. Sequence-level knowledge distillation. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016. https://aclanthology.org/D16-1139/. Donghyeok Koh, Gyuwan Kim, Jinyeong Bak, Seung-Hoon Na, Tao Yang, Haneol Jang, an","arxiv_match":false}],"id":"1606.07947","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":264},{"title":"Large language models as optimizers","arxiv_id":"2309.03409","year":2023,"url":"https://arxiv.org/abs/2309.03409","citing_submissions":388,"explicit_arxiv_matches":79,"examples":[{"id":"a0TQ3mSSY5","title":"NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents","forum_url":"https://openreview.net/forum?id=a0TQ3mSSY5","excerpt":" al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. URL https: //arxiv.org/abs/2505.09388. Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers. In International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Bb4VGOWELI. Chengyuan Yang, Zequn Sun, Wei Wei, and Wei Hu. Beyond static summarization: Proactive ","arxiv_match":false},{"id":"EmlATySoKy","title":"Falsify, Repair, Repeat: Auditable Local Repair of Typed Dynamical Equations","forum_url":"https://openreview.net/forum?id=EmlATySoKy","excerpt":"pired method for symbolic regression. Science Advances, 6(16):eaay2631, 2020. doi: 10.1126/sciadv.aay2631. Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers. In International Conference on Learning Representations, 2024. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in ","arxiv_match":false},{"id":"aAcE9xwrev","title":"A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization","forum_url":"https://openreview.net/forum?id=aAcE9xwrev","excerpt":"Mingxuan Yuan, and Chao Qian. BBOPlace-Bench: Benchmarking black-box optimization for chip placement, 2025. Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers. In Proceedings of the 12nd Conference on Learning Representations (ICLR’24), pp. 12028–12068, Vienna, Austria, 2024. Yilun Yao, Xinyu Tan, Chao-Hsuan Liu, Yaoming Li, Zhengyang Wang, Wenhan Yu, Zhew","arxiv_match":false}],"id":"2309.03409","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":265},{"title":"ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory","arxiv_id":"2509.25140","year":2025,"url":"https://arxiv.org/abs/2509.25140","citing_submissions":388,"explicit_arxiv_matches":119,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"olve collectively with agentic evolver. arXiv preprint arXiv:2604.08377, 2026. Siru Ouyang, Jun Yan, I Hsu, Yanfei Chen, Ke Jiang, Zifeng Wang, Rujun Han, Long Le, Samira Daruki, Xiangru Tang, et al. Reasoningbank: Scaling agent self-evolving with reasoning memory. In International Conference on Learning Representations, volume 2026, pp. 94327–94354, 2026. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernst","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"dels/ gpt-5.6-luna, 2026b. Official model documentation. Accessed: 2026-09-20. Siru Ouyang, Jun Yan, I Hsu, Yanfei Chen, Ke Jiang, Zifeng Wang, Rujun Han, Long Le, Samira Daruki, Xiangru Tang, et al. Reasoningbank: Scaling agent self-evolving with reasoning memory. In International Conference on Learning Representations, volume 2026, pp. 94327–94354, 2026. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Joseph E Gonzalez","arxiv_match":false},{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":"ence on Empirical Methods in Natural Language Processing, pp. 9340–9366, 2024. Siru Ouyang, Jun Yan, I Hsu, Yanfei Chen, Ke Jiang, Zifeng Wang, Rujun Han, Long Le, Samira Daruki, Xiangru Tang, et al. ReasoningBank: Scaling agent self-evolving with reasoning memory. In International Conference on Learning Representations, volume 2026, pp. 94327–94354, 2026. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Joseph E Gonzalez","arxiv_match":false}],"id":"2509.25140","metadata_source":"Cached arXiv API metadata","rank":266},{"title":"s1: Simple test-time scaling","arxiv_id":"2501.19393","year":2025,"url":"https://arxiv.org/abs/2501.19393","citing_submissions":387,"explicit_arxiv_matches":120,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"), pp. 1697–1706, January 2022. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori B Hashimoto. s1: Simple test-time scaling. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 20286–20332, 2025. Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Lear","arxiv_match":false},{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":"eprint arXiv:2002.07650, 2021. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori B Hashimoto. s1: Simple test-time scaling. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 20286–20332, 2025. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E Gonzale","arxiv_match":false},{"id":"JmKCJSrhxX","title":"FlowCompile: An Optimizing Compiler for Structured LLM Workflows","forum_url":"https://openreview.net/forum?id=JmKCJSrhxX","excerpt":"ps://arxiv.org/abs/2305.20050. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori B Hashimoto. s1: Simple test-time scaling. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 20286–20332, 2025. Lunyiu Nie, Zhimin Ding, Kevin Yu, Marco Cheung, Chris Jermaine, and Swarat Chaudhur","arxiv_match":false}],"id":"2501.19393","metadata_source":"Cached arXiv API metadata","rank":267},{"title":"GAIA: a benchmark for general AI assistants","arxiv_id":"2311.12983","year":2023,"url":"https://arxiv.org/abs/2311.12983","citing_submissions":386,"explicit_arxiv_matches":113,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"imization with a reference-free reward. Advances in Neural Information Processing Systems, 37:124198–124235, 2024. Grégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun, and Thomas Scialom. Gaia: a benchmark for general ai assistants. In International Conference on Learning Representations, volume 2024, pp. 9025–9049, 2024. Andrew Y Ng, Daishi Harada, and Stuart Russell. Policy invariance under reward transformations: Theory and ","arxiv_match":false},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":"ness: Advancing Long-Horizon Agents for Real-World Tasks, 2026. URL https://arxiv.org/abs/2608.01964. Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom. GAIA: a benchmark for General AI Assistants. In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 9025–9049, 2024. URL https://proceedings.iclr.cc/p","arxiv_match":false},{"id":"ROD8EjYEgO","title":"One Answer to Grade Them All? Reference-Based Evaluation and Its Optimization Effects in Search Agents","forum_url":"https://openreview.net/forum?id=ROD8EjYEgO","excerpt":"orrected grpo: From noisy rewards to unbiased gradients, 2026. URL https://arxiv.org/ abs/2510.18924. Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom. GAIA: a benchmark for General AI Assistants. In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 9025–9049, 2024. URL https://proceedings.iclr.cc/p","arxiv_match":false}],"id":"2311.12983","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":268},{"title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","arxiv_id":"2210.02186","year":2022,"url":"https://arxiv.org/abs/2210.02186","citing_submissions":386,"explicit_arxiv_matches":68,"examples":[{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":" pp. 22419–22430, 2021. URL https://proceedings.neurips.cc/paper/2021/hash/ bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html. Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. Timesnet: Temporal 2d-variation modeling for general time series analysis. In Proceedings of the International Conference on Learning Representations, 2023. URL https://openreview.net/ forum?id=ju_Uqw384Oq. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhan","arxiv_match":false},{"id":"843NwTNlpm","title":"Where Can Normality Go: Learning Admissible Futures for Time Series Anomaly Detection","forum_url":"https://openreview.net/forum?id=843NwTNlpm","excerpt":"(6): 1201–1214, 2022. Herman Wold. A Study in the Analysis of Stationary Time Series. PhD thesis, Almqvist & Wiksell, 1938. Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. Timesnet: Temporal 2d-variation modeling for general time series analysis. In Proceedings of the Eleventh International Conference on Learning Representations, 2023. Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, and Guansong Pang. Calibrated one-class clas","arxiv_match":false},{"id":"Ytw4pWrGGW","title":"PhysioSkeptic: Skeptical Reasoning Grounded in Signal Quality for Multimodal Rhythm Analysis","forum_url":"https://openreview.net/forum?id=Ytw4pWrGGW","excerpt":"cg classification performance. Computers in Biology and Medicine, 196: 110809, 2025. doi: 10.1016/j.compbiomed.2025.110809. Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. TimesNet: Temporal 2d-variation modeling for general time series analysis. In International Conference on Learning Representations, 2023. Xian Wu, Yutian Zhao, Yunyan Zhang, Jiageng Wu, Zhihong Zhu, Yingying Zhang, Yi Ouyang, Ziheng Zhang, Huimin Wang, Zhenxi Lin, et al. M","arxiv_match":false}],"id":"2210.02186","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":269},{"title":"FrugalGPT: How to use large language models while reducing cost and improving performance","arxiv_id":"2305.05176","year":2023,"url":"https://arxiv.org/abs/2305.05176","citing_submissions":382,"explicit_arxiv_matches":197,"examples":[{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":"ton. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021. Jiechen Chen, Sangwoo Park, and Osvaldo Simeone. Knowing when to stop: Delay-adaptive spiking neural network classifiers with reliability guarantees. IEEE Journal of Selected Topics in Signal Processing, 19(1):88–102, 2024. Lingjiao Chen, Matei Zaharia, and James Zou. FrugalGPT: How to use large language models while reducing cost and improving performance. arXiv preprint arXiv:2305.05176, 2023. Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo de Lazcano, Lucas Willems, Salem ","arxiv_match":true},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"v.org/abs/2506.02153. Aili Chen, Aonian Li, Baichuan Zhou, Bangwei Gong, Binyang Jiang, Boji Dan, et al. The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence. arXiv preprint arXiv:2605.26494, 2026a. URL https://arxiv.org/abs/2605.26494. Lingjiao Chen, Matei Zaharia, and James Zou. FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance. arXiv preprint arXiv:2305.05176, 2023. URL https://arxiv.org/abs/2305.05176. Tom Chen, Mao Cheng, Shi Dong, Kangrui Du, Yanbin Jiang, Jiajun Li, Yiming Li, Tao Lin, Yusheng Su","arxiv_match":true},{"id":"2te1UDMg3T","title":"MedRouter: Demystifying Knowledge Differences Across Medical LLMs for Routing-Based Reasoning","forum_url":"https://openreview.net/forum?id=2te1UDMg3T","excerpt":"014b8dc52e723f-Abstract-Conference.html. Junying Chen, Chi Gui, Ruyi Ouyang, Anningzhe Gao, Shunian Chen, Guiming Hardy Chen, Xidong Wang, Ruifei Zhang, Zhenyang Cai, Ke Ji, Guangjun Yu, Xiang Wan, and Benyou Wang. Huatuogpt-vision: Towards injecting medical visual knowledge into multimodal llms at scale, 2024a. Lingjiao Chen, Matei Zaharia, and James Zou. Frugalgpt: How to use large language models while reducing cost and improving performance. arXiv preprint arXiv:2305.05176, 2023. Shuhao Chen, Weisen Jiang, Baijiong Lin, James T. Kwok, and Yu Zhang. RouterDC: Query-based ","arxiv_match":true}],"id":"2305.05176","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":270},{"title":"Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents","arxiv_id":"2505.22954","year":2025,"url":"https://arxiv.org/abs/2505.22954","citing_submissions":381,"explicit_arxiv_matches":160,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"https://proceedings.neurips.cc/paper_files/paper/2025/file/ 5afa9cb1e917b898ad418216dc726fbd-Paper-Datasets_and_Benchmarks_ Track.pdf. Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, and Jeff Clune. Darwin gödel machine: openended evolution of self-improving agents. In International Conference on Learning Representations, volume 2026, pp. 104223–104294, 2026. Andrew Zhao, Yiran Wu, Tong Wu, Quentin Xu, Yang Yue, Matthieu Lin, Shenzhi Wang, Qingyun Wu, Zilong Zh","arxiv_match":false},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"Philip S. Yu. Coevoskills: Self-evolving agent skills via co-evolutionary verification. arXiv preprint arXiv:2604.01687, 2026a. Jenny Zhang, Shengran Hu, Cong Lu, Robert Tjarko Lange, and Jeff Clune. Darwin gödel machine: Open-ended evolution of self-improving agents. In The Fourteenth International Conference on Learning Representations, 2026b. URL https://openreview.net/forum?id= pUpzQZTvGY. Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob N. Foerster, Jeff Clune","arxiv_match":false},{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"Self-Improving Code Generation. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=1gkePTsAWf. Jenny Zhang, Shengran Hu, Cong Lu, Robert Tjarko Lange, and Jeff Clune. Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents. In The Fourteenth International Conference on Learning Representations, 2026a. URL https://openreview.net/forum?id= pUpzQZTvGY. Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob Foerster, Jeff Clune, M","arxiv_match":false}],"id":"2505.22954","metadata_source":"Cached arXiv API metadata","rank":271},{"title":"Pythia: A suite for analyzing large language models across training and scaling","arxiv_id":"2304.01373","year":2023,"url":"https://arxiv.org/abs/2304.01373","citing_submissions":378,"explicit_arxiv_matches":62,"examples":[{"id":"M82NayqkbE","title":"Dataset Inference via Cumulative Evidence","forum_url":"https://openreview.net/forum?id=M82NayqkbE","excerpt":"/1506.03486. Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al. Pythia: A suite for analyzing large language models across training and scaling. In International Conference on Machine Learning, pp. 2397–2430. PMLR, 2023. Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Br","arxiv_match":false},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"ng model stitching to compare neural representations. Advances in neural information processing systems, 34:225–236, 2021. Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. Pythia: A suite for analyzing large language models across training and scaling, 2023. URL https://arxiv.org/abs/2304.01373. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. Piqa: Reasoning about ph","arxiv_match":true},{"id":"WmdhapRKFO","title":"Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits","forum_url":"https://openreview.net/forum?id=WmdhapRKFO","excerpt":"Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, Usvsn Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar Van Der Wal. Pythia: A suite for analyzing large language models across training and scaling. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 2397– 2430, 2023. URL https://proceedings.mlr.press/v202/biderman","arxiv_match":false}],"id":"2304.01373","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":272},{"title":"Towards best practices of activation patching in language models: Metrics and methods","arxiv_id":"2309.16042","year":2023,"url":"https://arxiv.org/abs/2309.16042","citing_submissions":377,"explicit_arxiv_matches":95,"examples":[{"id":"fNrQjgb4Ch","title":"DV-Lens: Revealing the Functional Organization of Language Model Parameters","forum_url":"https://openreview.net/forum?id=fNrQjgb4Ch","excerpt":"en Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025a. URL https://arxiv. org/abs/2505.09388. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2025b. URL https://arxiv.org/abs/ 2412.15115. Fred Zhang and Neel Nanda. Towards best practices of activation patching in language models: Metrics and methods. arXiv preprint arXiv:2309.16042, 2023. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zho","arxiv_match":true},{"id":"JINWOT9zWh","title":"Open-Weight Masked Introspection: Measuring What Language Models Can Report About Their Own Computation","forum_url":"https://openreview.net/forum?id=JINWOT9zWh","excerpt":"arXiv:2605.18890. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. HellaSwag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019. arXiv:1905.07830. Fred Zhang and Neel Nanda. Towards best practices of activation patching in language models: Metrics and methods. In International Conference on Learning Representations (ICLR), 2024. arXiv:2309.16042. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and","arxiv_match":true},{"id":"brgf3Wa1iv","title":"One Pixel Wider, a Different Click: How Image Preprocessing Destabilizes GUI Grounding","forum_url":"https://openreview.net/forum?id=brgf3Wa1iv","excerpt":"nzhe Huang, Amrita Saha, Zeyuan Chen, et al. Gta1: Gui test-time scaling agent. In International Conference on Learning Representations, volume 2026, pp. 48295–48323, 2026. Fred Zhang and Neel Nanda. Towards best practices of activation patching in language models: Metrics and methods. In International Conference on Learning Representations, volume 2024, pp. 1651–1678, 2024. Yunzhu Zhang, Zeyu Pan, Zhengwen Zeng, Shuheng Shen, Changhua Meng, and Linchao Zhu. Mvp: Multiple view pre","arxiv_match":false}],"id":"2309.16042","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":273},{"title":"Flow-GRPO: Training Flow Matching Models via Online RL","arxiv_id":"2505.05470","year":2025,"url":"https://arxiv.org/abs/2505.05470","citing_submissions":375,"explicit_arxiv_matches":145,"examples":[{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":"ximilian Nickel, and Matt Le. Flow matching for generative modeling. In ICML, 2023. Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang. Flow-grpo: Training flow matching models via online rl. In ICLR, 2025. Xin Ma, Yaohui Wang, Xinyuan Chen, Gengyun Jia, Ziwei Liu, Yuan-Fang Li, Cunjian Chen, and Yu Qiao. Latte: Latent diffusion transformer for video generation. In TMLR, 2025. William Pe","arxiv_match":false},{"id":"qNhfYunjC7","title":"FASE: Factor-Aware Spectral Evolution for Gradient-Free Continuation of Diffusion Post-Training","forum_url":"https://openreview.net/forum?id=qNhfYunjC7","excerpt":"models. Advances in Neural Information Processing Systems, 37: 121038–121072, 2024. Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang. Flow-grpo: Training flow matching models via online rl. In Advances in Neural Information Processing Systems, volume 38, pp. 40783–40818, 2025. Xin Qiu, Yulu Gan, Conor F Hayes, Qiyao Liang, Yinggan Xu, Roberto Dailey, Elliot Meyerson, Babak Hodjat, and ","arxiv_match":false},{"id":"tSpuyBaWWf","title":"Displacement Matching: One Objective for Reward Learning and Distillation in Few-Step Generators","forum_url":"https://openreview.net/forum?id=tSpuyBaWWf","excerpt":"ps: //arxiv.org/abs/2605.26013. Junzhe Li, Yutao Cui, Tao Huang, Weijie Kong, Yiming Cheng, Chuxuan Zeng, Yinping Ma, Chun Fan, Miles Yang, Zhao Zhong, and Liefeng Bo. Mixgrpo: Unlocking flow-based grpo efficiency with mixed ode-sde, 2026. URL https://arxiv.org/abs/2507.21802. Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang. Flow-grpo: Training flow matching models via online rl. arXiv preprint arXiv:2505.05470, 2025. Yihong Luo, Tianyang Hu, Jiacheng Sun, Yujun Cai, and Jing Tang. Learning few-step diffusion","arxiv_match":true}],"id":"2505.05470","metadata_source":"Cached arXiv API metadata","rank":274},{"title":"Gemini: a family of highly capable multimodal models","arxiv_id":"2312.11805","year":2023,"url":"https://arxiv.org/abs/2312.11805","citing_submissions":375,"explicit_arxiv_matches":349,"examples":[{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":". arXiv preprint arXiv:2501.13956, 2025. Xubin Ren et al. Videorag: Retrieval-augmented generation with extreme long-context videos. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1, pp. 2390–2401, 2026. Shitong Sun et al. Egograph: Temporal knowledge graph for egocentric video understanding. arXiv preprint arXiv:2602.23709, 2026. Gemini Team et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. Gemma Team et al. Gemma 4 technical report. arXiv preprint arXiv:2607.02770, 2026. Shulin Ti","arxiv_match":true},{"id":"PxFICAgn1X","title":"Omnimodal Dataset Distillation via High-Order Proxy Alignment","forum_url":"https://openreview.net/forum?id=PxFICAgn1X","excerpt":" Dˆ 4: Dataset distillation via disentangled diffusion model. In CVPR, pp. 5809–5818, 2024. Duo Su, Huyu Wu, Huanran Chen, Yiming Shi, Yuzhu Wang, Xi Ye, and Jun Zhu. Diffusion models as dataset distillation priors. In ICLR, 2025. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models, 2023. arXiv preprint arXiv:2312.11805. Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune. Generative te","arxiv_match":true},{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"Zixuan Li, Zuodong Zhao, Wenguang Chen, Jidong Zhai, Bin Xu, Minlie Huang, Hongning Wang, Juanzi Li, Yuxiao Dong, and Jie Tang. Glm-4.5: Agentic, reasoning, and coding (arc) foundation models, 2025. URL https://arxiv.org/abs/2508.06471. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. Qixun Wang, Yang Shi, Yifei Wang, Yuanxing Zhang, Pengfei Wan, Kun Gai, Xianghua Ying, and Y","arxiv_match":true}],"id":"2312.11805","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":275},{"title":"Olmo 3","arxiv_id":"2512.13961","year":2025,"url":"https://arxiv.org/abs/2512.13961","citing_submissions":375,"explicit_arxiv_matches":358,"examples":[{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"ent observations for agentic self-distillation, 2026. URL https:// arxiv.org/abs/2606.11559. Kevin Lu and Thinking Machines Lab. On-policy distillation. Thinking Machines Lab: Connectionism, 2025. doi: 10.64434/tml.20251026. https://thinkingmachines.ai/blog/on-policy-distillation. Team Olmo, Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, David Graham, David Heineman, Dirk Groeneveld, Faeze Brahman, Finbarr Timbers, Hamish Ivison, et al. Olmo 3. arXiv preprint arXiv:2512.13961, 2025. Hejian Sang, Yuanda Xu, Zhengze Zhou, Ran He, Zhipeng Wang, and Jiachen Sun. Crisp: Compress","arxiv_match":true},{"id":"M752QnEbXC","title":"CoPE: Co-Evolving Policy and Experience via Context Distillation for Open-Ended Generation","forum_url":"https://openreview.net/forum?id=M752QnEbXC","excerpt":" Valentina Pyatkin, Sander Land, Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi, and Nathan Lambert. Rewardbench 2: Advancing reward model evaluation. In The Fourteenth International Conference on Learning Representations, 2026. URL https: //openreview.net/forum?id=fb0G86Dewb. Team Olmo, Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, David Graham, David Heineman, Dirk Groeneveld, Faeze Brahman, Finbarr Timbers, Hamish Ivison, et al. Olmo 3. arXiv preprint arXiv:2512.13961, 2025. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong ","arxiv_match":true},{"id":"cnb7XeypgW","title":"Search Shapes Conclusions: Auditing Evidence Selection Bias in Deep Research Agents","forum_url":"https://openreview.net/forum?id=cnb7XeypgW","excerpt":"ter James V. Miranda, Maarten Sap, Malia Morgan, Michael Schmitz, Michal Guerquin, Michael Wilson, Regan Huff, Ronan Le Bras, Rui Xin, Rulin Shao, Sam Skjonsberg, Shannon Zejiang Shen, Shuyue Stella Li, Tucker Wilde, Valentina Pyatkin, Will Merrill, Yapei Chang, Yuling Gu, Zhiyuan Zeng, Ashish Sabharwal, Luke Zettlemoyer, Pang Wei Koh, Ali Farhadi, Noah A. Smith, and Hannaneh Hajishirzi. Olmo 3. arXiv preprint arXiv:2512.13961, 2025. URL https://arxiv.org/abs/2512.13961. Jujia Zhao, Zhaoxin Huan, Zihan Wang, Xiaolu Zhang, Jun Zhou, Suzan Verberne, and Zhaochun Ren. Re","arxiv_match":true}],"id":"2512.13961","metadata_source":"Cached arXiv API metadata","rank":276},{"title":"FlashAttention-2: Faster attention with better parallelism and work partitioning","arxiv_id":"2307.08691","year":2023,"url":"https://arxiv.org/abs/2307.08691","citing_submissions":374,"explicit_arxiv_matches":113,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":". Pengtao Chen, Xianfang Zeng, Maosen Zhao, Peng Ye, Mingzhu Shen, Wei Cheng, Gang Yu, and Tao Chen. Sparse-vdit: Unleashing the power of sparse attention to accelerate video diffusion transformers. arXiv preprint arXiv:2506.03065, 2025. LightX2V Contributors. Lightx2v: Light video generation inference framework. https:// github.com/ModelTC/lightx2v, 2025. Tri Dao. Flashattention-2: Faster attention with better parallelism and work partitioning. arXiv preprint arXiv:2307.08691, 2023. Tri Dao. Flashattention-2: Faster attention with better parallelism and work partitioning. I","arxiv_match":true},{"id":"PCAODQ61Qw","title":"BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation","forum_url":"https://openreview.net/forum?id=PCAODQ61Qw","excerpt":"URL https://openreview.net/forum?id=LWMS4pk2vK. Chi-Chih Chang, Wei-Cheng Lin, Chien-Yu Lin, Hung-Yueh Chiang, Yash Akhauri, Xilai Dai, Huiqiang Jiang, Yucheng Li, Luis Ceze, Kai-Chiang Wu, and Mohamed S. Abdelfattah. xkv: Cross-layer kv-cache compression via aligned singular vector extraction, 2026. URL https: //arxiv.org/abs/2503.18893. Tri Dao. Flashattention-2: Faster attention with better parallelism and work partitioning, 2023. URL https://arxiv.org/abs/2307.08691. Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margare","arxiv_match":true},{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":"uanhao Ban, and ChoJui Hsieh. Self-forcing++: Towards minute-scale high-quality video generation. In International Conference on Learning Representations, volume 2026, pp. 85802–85822, 2026. Tri Dao. Flashattention-2: Faster attention with better parallelism and work partitioning. In International Conference on Learning Representations, volume 2024, pp. 35549–35562, 2024. Xiao Fu, Shitao Tang, Min Shi, Xian Liu, Jinwei Gu, Ming-Yu Liu, Dahua Lin, and Chen-Hsuan Lin. Plenoptic","arxiv_match":false}],"id":"2307.08691","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":277},{"title":"RT-1: Robotics transformer for real-world control at scale","arxiv_id":"2212.06817","year":2022,"url":"https://arxiv.org/abs/2212.06817","citing_submissions":372,"explicit_arxiv_matches":223,"examples":[{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"nny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. pi0: A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164, 2024. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022. Remi Cadene, Simon Alibert, Alexander Soare, Quentin Gallouedec, Adil Zouitine, Steven Palma","arxiv_match":true},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"nny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. π0 : A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164, 2024. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. RT-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022. Qingwen Bu, Yanting Yang, Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, and","arxiv_match":true},{"id":"ylrbHy6YvK","title":"KI-VLA: Kinematics-Informed Action Decoding for Flow-Matching Robot Policies","forum_url":"https://openreview.net/forum?id=ylrbHy6YvK","excerpt":"ng Research, pp. 2165–2183, 2023a. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. RT-1: Robotics transformer for real-world control at scale. In Robotics: Science and Systems XIX, 2023b. doi: 10.15607/RSS.2023.XIX.025. Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Qiwei Liang, Zixuan Li, Xianliang Lin, Yiheng Ge, Zhenyu ","arxiv_match":false}],"id":"2212.06817","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":278},{"title":"Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs","arxiv_id":"2402.14740","year":2024,"url":"https://arxiv.org/abs/2402.14740","citing_submissions":371,"explicit_arxiv_matches":96,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":" Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, and Sara Hooker. Back to basics: Revisiting reinforce-style optimization for learning from human feedback in llms. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12248–12267, 2024. Anthropic. Claude 3.7 Sonnet and Claude Code, 2025a. URL ht","arxiv_match":false},{"id":"9N1uPPmr4W","title":"Shared Resources Distort Credit Assignment in Group-Relative Agent Reinforcement Learning","forum_url":"https://openreview.net/forum?id=9N1uPPmr4W","excerpt":" Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, and Sara Hooker. Back to basics: Revisiting REINFORCE-style optimization for learning from human feedback in LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12248–12267, 2024. doi: 10.18653/v1/2024.acl-long.662. Peter M. Aronow and Cyr","arxiv_match":false},{"id":"cbEkVPsGSJ","title":"DriveBeyond: Rethinking Group Advantage for Driving Policy Optimization","forum_url":"https://openreview.net/forum?id=cbEkVPsGSJ","excerpt":"bution shift. Journal of Machine Learning Research, 22(98):1–76, 2021. Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, and Sara Hooker. Back to basics: Revisiting REINFORCE-style optimization for learning from human feedback in LLMs. In ACL, pp. 12248–12267, 2024. Haojie Bai, Aimin Li, Ruoyu Yao, Xiongwei Zhao, Tingting Zhang, Xing Zhang, Lin Gao, et al. SCORP: Scene-consistent multi-agent diffusion planning with stable online r","arxiv_match":false}],"id":"2402.14740","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":279},{"title":"Highdimensional continuous control using generalized advantage estimation","arxiv_id":"1506.02438","year":2015,"url":"https://arxiv.org/abs/1506.02438","citing_submissions":368,"explicit_arxiv_matches":220,"examples":[{"id":"O0MnDF6uMA","title":"Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents","forum_url":"https://openreview.net/forum?id=O0MnDF6uMA","excerpt":" Hierarchical plan–execute RL for multi-turn LLM agents. In International Conference on Machine Learning (ICML), 2026. John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. Highdimensional continuous control using generalized advantage estimation. In International Conference on Learning Representations (ICLR), 2016. Rulin Shao, Akari Asai, Shannon Zejiang Shen, Hamish Ivison, Varsha Kishore, Jingming Zhuo, Xinran Zhao, Molly Park, Samuel G Fi","arxiv_match":false},{"id":"cbEkVPsGSJ","title":"DriveBeyond: Rethinking Group Advantage for Driving Policy Optimization","forum_url":"https://openreview.net/forum?id=cbEkVPsGSJ","excerpt":" Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In ICML, pp. 1889–1897, 2015. John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and Pieter Abbeel. Highdimensional continuous control using generalized advantage estimation. In ICLR, 2016. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Zhihong Shao, Peiyi Wang","arxiv_match":false},{"id":"MREjTH2FlB","title":"Seeing What Changed: Transition-Guided Credit Assignment for GUI Agents","forum_url":"https://openreview.net/forum?id=MREjTH2FlB","excerpt":"en Team. Qwen3.6-27B: Flagship-level coding in a 27B dense model. https://qwen.ai/ blog?id=qwen3.6-27b, April 2026b. John Schulman, Philipp Moritz, Sergey Levine, Michael I Jordan, and Pieter Abbeel. Highdimensional continuous control using generalized advantage estimation. In International Conference on Learning Representations, 2016. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint ","arxiv_match":false}],"id":"1506.02438","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":280},{"title":"Imagenet large scale visual recognition challenge","arxiv_id":"1409.0575","year":2014,"url":"https://arxiv.org/abs/1409.0575","citing_submissions":368,"explicit_arxiv_matches":31,"examples":[{"id":"aVu1Zo8kCI","title":"Beyond Holistic Representations: Dual Distribution Modeling for Test-Time Adaptation of Vision–Language Models","forum_url":"https://openreview.net/forum?id=aVu1Zo8kCI","excerpt":"chine learning (ICML), pp. 5389–5400. PMLR, 2019. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, and Michael Bernstein. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252, 2015. Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao. Test-time prompt tuning for zero-shot g","arxiv_match":false},{"id":"MpNPoDTf3B","title":"Semantic Fields Are Interfaces: Consumer-Typed Functional Migration for Representation Autoencoders","forum_url":"https://openreview.net/forum?id=MpNPoDTf3B","excerpt":"ts. In ICLR, 2015. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115:211–252, 2015. Debopam Sanyal, Anantharaman Iyer, Alind Khare, Trisha Jain, Akshay Jajoo, Myungjin Lee, Clayton Kerce, and Alexey Tumanov. KLAS: Using s","arxiv_match":false},{"id":"Z4dlVT9XRr","title":"VastMAT: A Large-Scale Multi-Category Benchmark for Multi-Animal Tracking","forum_url":"https://openreview.net/forum?id=Z4dlVT9XRr","excerpt":" multi-camera tracking. In Proceedings of the European Conference on Computer Vision Workshops, pp. 17–35, 2016. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252, 2015. Peize Sun, Jinkun Cao, Yi Jiang, Rufeng Zhang, Enze Xie, Zehuan Yuan, Changhu Wang, and Ping Luo. TransTrack: Multiple object tracking","arxiv_match":false}],"id":"1409.0575","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":281},{"title":"Revisiting feature prediction for learning visual representations from video","arxiv_id":"2404.08471","year":2024,"url":"https://arxiv.org/abs/2404.08471","citing_submissions":367,"explicit_arxiv_matches":199,"examples":[{"id":"RYAAeHLU5G","title":"Backward Latent Certainty: When More Certain Forecasts Lie Farther","forum_url":"https://openreview.net/forum?id=RYAAeHLU5G","excerpt":" David Fan, Quentin Garrido, Russell Howes, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, et al. V-JEPA 2: Self-supervised video models enable understanding, prediction and planning. arXiv preprint arXiv:2506.09985, 2025. Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mahmoud Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. arXiv preprint arXiv:2404.08471, 2024. Gamaleldin Elsayed, Aravindh Mahendran, Sjoerd Van Steenkiste, Klaus Greff, Michael C Mozer,","arxiv_match":true},{"id":"ubiZuUXpr6","title":"Copper-Policy: Focus on the Representation for Robust Robot Manipulation","forum_url":"https://openreview.net/forum?id=ubiZuUXpr6","excerpt":"hael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15619–15629, June 2023. Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mahmoud Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. arXiv preprint arXiv:2404.08471, 2024. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fus","arxiv_match":true},{"id":"WTmChZA9OO","title":"DreamSeek: Imagination-Guided Evidence Acquisition for Long-Video Question Answering","forum_url":"https://openreview.net/forum?id=WTmChZA9OO","excerpt":" Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. Trans. Mach. Learn. Res., 2024, 2024. Keshigeyan Chandrasegaran, Agrim Gupta, Lea M. Hadzic, Taran Kota, Jimming He, Cristóbal Eyzaguirre, Zane Durante, Manling Li, Jiajun Wu, and Li Fei-Fei. Hourv","arxiv_match":false}],"id":"2404.08471","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":282},{"title":"Gated delta networks: Improving mamba2 with delta rule","arxiv_id":"2412.06464","year":2024,"url":"https://arxiv.org/abs/2412.06464","citing_submissions":366,"explicit_arxiv_matches":121,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":" preprint arXiv:2502.16002, 2025. John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik R Narasimhan, and Ofir Press. SWE-agent: Agent-computer interfaces enable automated software engineering. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024b. URL https://arxiv.org/abs/2405.15793. Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated delta networks: Improving mamba2 with delta rule. arXiv preprint arXiv:2412.06464, 2024c. Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yu","arxiv_match":true},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":", et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. Shulai Zhang, Ningxin Zheng, Haibin Lin, Ziheng Jiang, Wenlei Bao, Chengquan Jiang, Qi Hou, Weihao Cui, Size Zheng, Li-Wen Chang, et al. Comet: Fine-grained computation-communication overlapping for mixture-of-experts. arXiv preprint arXiv:2502.19811, 2025. Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated delta networks: Improving Mamba2 with delta rule, 2024. URL https://arxiv.org/abs/2412.06464. Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojan","arxiv_match":true},{"id":"OAX2QPoTJg","title":"HyLin: Modality-Aware Hybrid Attention for Efficient Long-Context Vision–Language Models","forum_url":"https://openreview.net/forum?id=OAX2QPoTJg","excerpt":"roceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 56501–56523. PMLR, 2024. Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated Delta Networks: Improving Mamba2 with Delta Rule. In International Conference on Learning Representations, 2025b. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da ","arxiv_match":false}],"id":"2412.06464","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":283},{"title":"DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models","arxiv_id":"2512.02556","year":2025,"url":"https://arxiv.org/abs/2512.02556","citing_submissions":365,"explicit_arxiv_matches":326,"examples":[{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":" Aaron Chatterji, Thomas Cunningham, David J Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, and Kevin Wadman. How people use chatgpt. Technical report, National Bureau of Economic Research, 2025. DeepSeek-AI. Deepseek-v3.2: Pushing the frontier of open large language models, 2025b. URL https://arxiv.org/abs/2512.02556. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. URL h","arxiv_match":true},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"i, Shichao Song, Hanyu Wang, Simin Niu, Ding Chen, Jiawei Yang, Chenyang Xi, Huayi Lai, Jihao Zhao, Yezhaohui Wang, et al. Memos: An operating system for memory-augmented generation (mag) in large language models. arXiv preprint arXiv:2505.22101, 2025. Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, et al. Deepseek-v3. 2: Pushing the frontier of open large language models. arXiv preprint arXiv:2512.02556, 2025. Shu Liu, Mert Cemri, Shubham Agarwal, Alexander Krentsel, Ashwin Naren, Qiuyang Mang, Zhifei","arxiv_match":true},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":"ling-book/. Damai Dai, Chengqi Deng, Chenggang Zhao, R. X. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, et al. DeepSeekMoE: Towards ultimate expert specialization in mixture-ofexperts language models. arXiv preprint arXiv:2401.06066, 2024. DeepSeek-AI. DeepSeek-V3 technical report, 2025b. URL https://arxiv.org/abs/2412. 19437v2. DeepSeek-AI. DeepSeek-V3.2: Pushing the frontier of open large language models, 2025a. URL https://arxiv.org/abs/2512.02556. DeepSeek-AI. DeepSeek-V4: Towards highly efficient million-token context intelligence, 2026. URL h","arxiv_match":true}],"id":"2512.02556","metadata_source":"Cached arXiv API metadata","rank":284},{"title":"Representation alignment for generation: Training diffusion transformers is easier than you think","arxiv_id":"2410.06940","year":2024,"url":"https://arxiv.org/abs/2410.06940","citing_submissions":363,"explicit_arxiv_matches":130,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"ngguk Jeon, Se June Joo, Jianwei Yang, Baolin Peng, Ajay Mandlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, et al. Latent action pretraining from videos. In International Conference on Learning Representations, volume 2025, pp. 28213–28239, 2025. Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:2410.06940, 2024. Xiu Yuan, Tongzhou Mu, Stone Tao, Yunhao Fang, Zhang Zhang, and Hao Su. Policy decorator: Mo","arxiv_match":true},{"id":"D2lvweC7MC","title":"When Are Diffusion Recovery Clocks Identifiable? A Spectral Measurement Protocol for Teacher-Forced Denoising","forum_url":"https://openreview.net/forum?id=D2lvweC7MC","excerpt":"reconstruction fid predictive of diffusion generation fid. arXiv preprint arXiv:2603.05630, 2026. Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. In International Conference on Learning Representations, 2025. Yongsheng Yu, Wei Xiong, Weili Nie, Yichen Sheng, Shiqiu Liu, and Jiebo Luo. Pixeldit: Pixel diffusion transformers for image generatio","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"ng, Zhi Tian, Weilin Huang, and Limin Wang. Ddt: Decoupled diffusion transformer. In CVPR, 2026c. Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. In ICLR, 2025. Xianbang Wang, Hanhong Zhao, Yiyang Lu, Kangyang Zhou, Linrui Ma, and Kaiming He. MiniT2I: A minimalist baseline for text-to-image generation. https://peppaking8. github.io/#/post/min","arxiv_match":false}],"id":"2410.06940","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":285},{"title":"Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation","arxiv_id":"2302.09664","year":2023,"url":"https://arxiv.org/abs/2302.09664","citing_submissions":361,"explicit_arxiv_matches":117,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"ee. Self-generated in-context learning: Leveraging auto-regressive language models as a demonstration generator. arXiv preprint arXiv:2206.08082, 2022. Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. In The Eleventh International Conference on Learning Representations, 2023. Ang Li, Charles Wang, Deqing Fu, Kaiyu Yue, Zikui Cai, Wang Bill Zhu, Ollie Liu, Peng Guo, Willie Neiswanger, Furong Huang","arxiv_match":false},{"id":"hlquBnFSq0","title":"A Multidimensional View of LLM Uncertainty Quantification via Scenario Interventions","forum_url":"https://openreview.net/forum?id=hlquBnFSq0","excerpt":"guage models (mostly) know what they know, 2022. URL https://arxiv.org/abs/2207.05221. Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth A. Malik, and Yarin Gal. Semantic entropy probes: Robust and cheap hallucination detection in LLMs, 2024. URL https: //arxiv.org/abs/2406.15927. Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. arXiv preprint arXiv:2302.09664, 2023. Lucie Kunitomo-Jacquin, Edison Marrese-Taylor, Ken Fukuda, and Masahiro Hamasaki. Evidential","arxiv_match":true},{"id":"9cpSUDXx9d","title":"LLM Uncertainty Revealed in Behavioral Boundary Effects","forum_url":"https://openreview.net/forum?id=9cpSUDXx9d","excerpt":"05221. Adam Tauman Kalai and Santosh S. Vempala. Calibrated language models must hallucinate. In Proceedings of the 56th Annual ACM Symposium on Theory of Computing, pp. 160–171. Association for Computing Machinery, 2024. doi: 10.1145/3618260.3649777. URL https: //doi.org/10.1145/3618260.3649777. Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. arXiv preprint arXiv:2302.09664, 2023. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris ","arxiv_match":true}],"id":"2302.09664","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":286},{"title":"Transformers are RNNs: Fast autoregressive transformers with linear attention","arxiv_id":"2006.16236","year":2020,"url":"https://arxiv.org/abs/2006.16236","citing_submissions":360,"explicit_arxiv_matches":38,"examples":[{"id":"XIvhtY214J","title":"SOLAR: SVD-Optimized Lifelong Attention for Recommendation","forum_url":"https://openreview.net/forum?id=XIvhtY214J","excerpt":"ng Kang and Julian J. McAuley. Self-attentive sequential recommendation, 2018. URL https://doi.org/10.1109/ICDM.2018.00035. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Transformers are rnns: Fast autoregressive transformers with linear attention, 2020. URL http://proceedings. mlr.press/v119/katharopoulos20a.html. Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer, 2020. URL https://openreview.net/forum?id=","arxiv_match":false},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"s of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1601– 1611, 2017. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Transformers are rnns: Fast autoregressive transformers with linear attention. In International conference on machine learning, pp. 5156–5165. PMLR, 2020. Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, and Dragomir Radev. Booksum: A collection of datas","arxiv_match":false},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":" Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive Benchmark Suite for Video Generative Models. In CVPR, 2024. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. In ICML, 2020. Kimi Team. Kimi Linear: An Expressive, Efficient Attention Architecture. arXiv preprint arXiv:2510.26692, 2025. Kimi Team. Kimi K3: Open Frontier Intelligence. Moonshot AI technical b","arxiv_match":false}],"id":"2006.16236","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":287},{"title":"An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models","arxiv_id":"2403.06764","year":2024,"url":"https://arxiv.org/abs/2403.06764","citing_submissions":359,"explicit_arxiv_matches":42,"examples":[{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":" Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster. arXiv preprint arXiv:2210.09461, 2022. Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large visionlanguage models. In European Conference on Computer Vision, pp. 19–35. Springer, 2024. Zhibao Chen and Qian Cheng. Learning what to remember: A cognitively grounded multi-factor value model for agentic memory. arXiv","arxiv_match":false},{"id":"Xdik5HW8VD","title":"WaLKPruner: Cognitively Inspired Visual Token Pruning for Spatial Understanding","forum_url":"https://openreview.net/forum?id=Xdik5HW8VD","excerpt":"Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923, 2025. Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large visionlanguage models. In European Conference on Computer Vision, pp. 19–35. Springer, 2024a. Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan, and Tao Chen. Ll3da: Visual interac","arxiv_match":false},{"id":"iBCBaGASs6","title":"HEAT: Hierarchical Elastic Adaptive Token Sparsification via Depth Scaling","forum_url":"https://openreview.net/forum?id=iBCBaGASs6","excerpt":" In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3489–3499, 2026a. Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large visionlanguage models. In Ales Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, and Gül Varol (eds.), European Conference on Computer Vision, Lecture Notes in Computer Science, pp. 19–35, 2024a. Li","arxiv_match":false}],"id":"2403.06764","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":288},{"title":"Challenging big-bench tasks and whether chain-of-thought can solve them","arxiv_id":"2210.09261","year":2022,"url":"https://arxiv.org/abs/2210.09261","citing_submissions":359,"explicit_arxiv_matches":90,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"on Machine Learning, pp. 45883–45905. PMLR, 2024. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them. In Findings of the Association for Computational Linguistics: ACL 2023, pp. 13003–13051, 2023. Xiangru Tang, Anni Zou, Zhuosheng Zhang, Ziming Li, Yilun Zhao, Xingyao Zhang, Arman Cohan, and Mark Ge","arxiv_match":false},{"id":"OaKuNlZFmJ","title":"SCRATCH: Efficient Math Inference for Large Reasoning Models","forum_url":"https://openreview.net/forum?id=OaKuNlZFmJ","excerpt":"ps://openreview.net/forum?id=HvoG8SxggZ. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, and Jason Wei. Challenging BIG-bench tasks and whether chain-of-thought can solve them. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.), Findings of the Association for Computational Linguistics: ACL 2023, pp. 13003–13051, Toronto, Canada, July 2023. Association for Compu","arxiv_match":false},{"id":"fNrQjgb4Ch","title":"DV-Lens: Revealing the Functional Organization of Language Model Parameters","forum_url":"https://openreview.net/forum?id=fNrQjgb4Ch","excerpt":"the limits of chain-of-thought with multistep soft reasoning. In International Conference on Learning Representations, 2024. URL https://arxiv.org/abs/2310.16049. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei. Challenging BIGBench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022. URL https://arxiv.org/abs/2210.09261. Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, et al. 2 OLMo 2 furious. arXiv preprint arX","arxiv_match":true}],"id":"2210.09261","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":289},{"title":"Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts","arxiv_id":"2310.02255","year":2023,"url":"https://arxiv.org/abs/2310.02255","citing_submissions":357,"explicit_arxiv_matches":97,"examples":[{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"n neural information processing systems, 35:2507–2521, 2022. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, KaiWei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 23439–23554, 2024. URL https://proceedings.iclr.cc","arxiv_match":false},{"id":"F0hAiHbpm3","title":"FigAct: Turning Scientific Figures into Active Canvases for Explanation","forum_url":"https://openreview.net/forum?id=F0hAiHbpm3","excerpt":" scientific figures. arXiv preprint arXiv:2609.01006, 2026b. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, KaiWei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. In International Conference on Learning Representations, volume 2024, pp. 23439–23554, 2024. Ahmed Masry, Mohammed Saidul Islam, Mahir Ahmed, Aayush Bajaj, Firoz Kabir, Aaryaman Kartha, Md Tahmid Ra","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"t chains for science question answering. In NeurIPS, 2022b. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. In ICLR, 2023. Yiyang Ma, Xingchao Liu, Xiaokang Chen, Wen Liu, Chengyue Wu, Zhiyu Wu, Zizheng Pan, Zhenda Xie, Haowei Zhang, Xingkai Yu, et al. Janusflow: Harmonizing autoregression and rectified f","arxiv_match":false}],"id":"2310.02255","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":290},{"title":"Chronos: Learning the language of time series","arxiv_id":"2403.07815","year":2024,"url":"https://arxiv.org/abs/2403.07815","citing_submissions":356,"explicit_arxiv_matches":133,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":"ngapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, and Yuyang Wang. Chronos: Learning the Language of Time Series. Transactions on Machine Learning Research, 2024. Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyu","arxiv_match":false},{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":" Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, et al. Chronos: Learning the language of time series. Transactions on Machine Learning Research, 2024. URL https://openreview.net/forum?id=gerNCVqqtR. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. Adaptive graph convolutional recurrent network","arxiv_match":false},{"id":"0LJ106OAjs","title":"Forecasting Before the Outcome: Catalogue Gated Prequential Cognitive Performance under Participant and Domain Shift","forum_url":"https://openreview.net/forum?id=0LJ106OAjs","excerpt":"vel dataset, models, and evaluation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pp. 147–165, 2023. Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, et al. Chronos: Learning the language of time series. arXiv preprint arXiv:2403.07815, 2024. URL https://arxiv.org/abs/2403.07815. Nicolo Cesa-Bianchi, Gábor Lugosi, et al. Prediction, learning, and games, volume 1. Cambridge uni","arxiv_match":true}],"id":"2403.07815","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":291},{"title":"Reinforcement Learning via Self-Distillation","arxiv_id":"2601.20802","year":2026,"url":"https://arxiv.org/abs/2601.20802","citing_submissions":356,"explicit_arxiv_matches":267,"examples":[{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"gs-acl.507/. Jonas Hübotter, Frederike Lübeck, Lejs Deen Behric, Anton Baumann, Marco Bagatella, Daniel Marta, Ido Hakimi, Idan Shenfeld, Thomas Kleine Buening, Carlos Guestrin, and Andreas Krause. Reinforcement learning via self-distillation. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=QkfkxyRizZ. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, A","arxiv_match":false},{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":"reprint arXiv:2501.12948, 2025. Qianyun Guo, Yibo Li, Yue Liu, and Bryan Hooi. Towards natural personalization: Evaluating long-horizon preference following in personalized user-llm interactions. arXiv preprint arXiv:2603.04191, 2026. Jonas Hübotter, Frederike Lübeck, Lejs Behric, Anton Baumann, Marco Bagatella, Daniel Marta, Ido Hakimi, Idan Shenfeld, Thomas Kleine Buening, Carlos Guestrin, et al. Reinforcement learning via self-distillation. arXiv preprint arXiv:2601.20802, 2026. Yeonjun In, Wonjoong Kim, Sangwu Park, Kanghoon Yoon, and Chanyoung Park. Personalize-thenst","arxiv_match":true},{"id":"7EtSASfnVn","title":"OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning","forum_url":"https://openreview.net/forum?id=7EtSASfnVn","excerpt":"Multimodal LLMs. arXiv preprint arXiv:2502.04326, 2025. Kairui Hu, Penghao Wu, Fanyi Pu, Wang Xiao, Yuanhan Zhang, Xiang Yue, Bo Li, and Ziwei Liu. Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos. arXiv preprint arXiv:2501.13826, 2025. Jonas Hübotter, Frederike Lübeck, Lejs Behric, Anton Baumann, Marco Bagatella, Daniel Marta, Ido Hakimi, Idan Shenfeld, et al. Reinforcement Learning via Self-Distillation. arXiv preprint arXiv:2601.20802, 2026. Kimi Team. Kimi K3: Open Frontier Intelligence. arXiv preprint arXiv:2607.24653, 2026. Caoru","arxiv_match":true}],"id":"2601.20802","metadata_source":"Cached arXiv API metadata","rank":292},{"title":"Large language monkeys: Scaling inference compute with repeated sampling","arxiv_id":"2407.21787","year":2024,"url":"https://arxiv.org/abs/2407.21787","citing_submissions":355,"explicit_arxiv_matches":329,"examples":[{"id":"o7ODDwTMgU","title":"Valid but Wrong: Decomposing Language-Model Errors on Closed Vocabularies","forum_url":"https://openreview.net/forum?id=o7ODDwTMgU","excerpt":"h. Crane: Reasoning with constrained llm generation, 2025. URL https://arxiv.org/abs/ 2502.09061. Marco Bombieri, Paolo Fiorini, Simone Paolo Ponzetto, and Marco Rospocher. Do llms dream of ontologies?, 2025. URL https://arxiv.org/abs/2401.14931. Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, and Azalia Mirhoseini. Large language monkeys: Scaling inference compute with repeated sampling, 2024. URL https://arxiv.org/abs/2407.21787. Matthias Busch, Marius Tacke, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Christian J. Cyron, Ch","arxiv_match":true},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":" Mensch, Katherine Millican, et al. Flamingo: a visual language model for few-shot learning. Advances in neural information processing systems, 35:23716–23736, 2022. Léon Bottou and Vladimir Vapnik. Local learning algorithms. Neural computation, 4(6):888–900, 1992. Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. Large language monkeys: Scaling inference compute with repeated sampling. arXiv preprint arXiv:2407.21787, 2024. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, et","arxiv_match":true},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":", 2024. Emil Biju, Shayan Talaei, Zhemin Huang, Mohammadreza Pourreza, Azalia Mirhoseini, and Amin Saberi. Sprint: Enabling interleaved planning and parallelized execution in reasoning models. Advances in Neural Information Processing Systems, 38:96393–96433, 2026. Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. Large language monkeys: Scaling inference compute with repeated sampling. arXiv preprint arXiv:2407.21787, 2024. Justin Chen, Swarnadeep Saha, and Mohit Bansal. Reconcile: Round-table conference improves r","arxiv_match":true}],"id":"2407.21787","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":293},{"title":"Tent: Fully test-time adaptation by entropy minimization","arxiv_id":"2006.10726","year":2020,"url":"https://arxiv.org/abs/2006.10726","citing_submissions":354,"explicit_arxiv_matches":90,"examples":[{"id":"2N8aFXk52A","title":"How Much to Trust the Eyes? Reliability-Guided Inference for Diffusion-Based Audio-Visual Speech Enhancement","forum_url":"https://openreview.net/forum?id=2N8aFXk52A","excerpt":"ase (DEMAND): A Database of Multichannel Environmental Noise Recordings. In Proceedings of Meetings on Acoustics, 2013. Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully Test-Time Adaptation by Entropy Minimization. In International Conference on Learning Representations, 2021. Qingyang Zhang, Haitao Wu, Changqing Zhang, Qinghua Hu, Huazhu Fu, Joey Tianyi Zhou, and Xi Peng. Provable Dynamic Fusion for Low-Quali","arxiv_match":false},{"id":"SiF9YBf3x7","title":"Situated Inference: Adapting Frozen Generative Speech Enhancement under Acoustic Distribution Shift","forum_url":"https://openreview.net/forum?id=SiF9YBf3x7","excerpt":"isy Speech Database for Training Speech Enhancement Algorithms and TTS Models. University of Edinburgh DataShare, 2017. Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully Test-Time Adaptation by Entropy Minimization. In International Conference on Learning Representations, 2021. Zhen-Yu Zhang, Zhiyu Xie, Huaxiu Yao, and Masashi Sugiyama. Test-Time Adaptation in NonStationary Environments via Adaptive Representat","arxiv_match":false},{"id":"0LJ106OAjs","title":"Forecasting Before the Outcome: Catalogue Gated Prequential Cognitive Performance under Participant and Domain Shift","forum_url":"https://openreview.net/forum?id=0LJ106OAjs","excerpt":"69505–69531, 2023. Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. Proofwriter: Generating implications, proofs, and abductive statements over natural language. In Findings of the association for computational linguistics: ACL-IJCNLP 2021, pp. 3621–3634, 2021. Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726, 2020. URL https://arxiv.org/abs/2006.10726. Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai. Continual test-time domain adaptation. In 2022","arxiv_match":true}],"id":"2006.10726","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":294},{"title":"Training large language models to reason in a continuous latent space","arxiv_id":"2412.06769","year":2024,"url":"https://arxiv.org/abs/2412.06769","citing_submissions":353,"explicit_arxiv_matches":230,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"cit behavioral cloning. In Conference on robot learning, pp. 158–168. PMLR, 2022. Abner Guzmán-Rivera, Dhruv Batra, and Pushmeet Kohli. Multiple choice learning: Learning to produce multiple structured outputs. In Advances in Neural Information Processing Systems (NeurIPS), 2012. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space. arXiv preprint arXiv:2412.06769, 2024. Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance, 2022. https://arxiv.org/ab","arxiv_match":true},{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":", 2026. URL https://arxiv.org/abs/ 2510.15522. Yue Fan, Xuehai He, Diji Yang, Kaizhi Zheng, Ching-Chen Kuo, Yuting Zheng, Sravana Jyothi Narayanaraju, Xinze Guan, and Xin Eric Wang. Grit: Teaching mllms to think with images. arXiv preprint arXiv:2505.15879, 2025. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space, 2025. URL https://arxiv.org/abs/ 2412.06769. Jiawei Gu, Yunzhuo Hao, Huichen Will Wang, Linjie Li, Michael Qizhe Shieh, Yejin Choi, Ranjay Kris","arxiv_match":true},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"Vision (ECCV), 2024. Xingyu Fu, Minqian Liu, Zhengyuan Yang, John Corring, Yijuan Lu, Jianwei Yang, Dan Roth, Dinei Florencio, and Cha Zhang. Refocus: Visual editing as a chain of thought for structured image understanding. arXiv preprint arXiv:2501.05452, 2025. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space, 2025. URL https: //arxiv.org/abs/2412.06769. Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, and Shaoh","arxiv_match":true}],"id":"2412.06769","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":295},{"title":"Large language models are zero-shot reasoners","arxiv_id":"2205.11916","year":2022,"url":"https://arxiv.org/abs/2205.11916","citing_submissions":351,"explicit_arxiv_matches":41,"examples":[{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":"ge Processing. Association for Computational Linguistics, 2025. URL https://aclanthology.org/2025. emnlp-main.1318/. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems, volume 35, pp. 22199–22213, 2022. doi: 10.52202/068431-1613. URL https://proceedings.neurips.cc/paper_files/paper/2022/hash/ 8bb0d291acd4acf06ef","arxiv_match":false},{"id":"PEoM2nDAcy","title":"EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation","forum_url":"https://openreview.net/forum?id=PEoM2nDAcy","excerpt":"medicine: A large-scale, systematic expert evaluation and practical insights. arXiv preprint arXiv:2511.06738, 2025. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199–22213, 2022. Maya Kruse, Shiyue Hu, Nicholas Derby, Yifu Wu, Samantha Stonbraker, Bingsheng Yao, Dakuo Wang, Elizabeth Goldberg, and Yanju","arxiv_match":false},{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"for Computational Linguistics. doi: 10.18653/v1/ 2020.acl-main.386. URL https://aclanthology.org/2020.acl-main.386/. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large Language Models are Zero-Shot Reasoners. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (eds.), Advances in Neural Information Processing Systems, 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/ hash/8","arxiv_match":false}],"id":"2205.11916","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":296},{"title":"Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe","arxiv_id":"2604.13016","year":2026,"url":"https://arxiv.org/abs/2604.13016","citing_submissions":348,"explicit_arxiv_matches":277,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"Bench: Holistic and contamination free evaluation of large language models for code, 2024. URL https://arxiv.org/abs/2403 .07974v2. arXiv preprint, version 2. Yaxuan Li, Yuxin Zuo, Bingxiang He, Jinqian Zhang, Chaojun Xiao, Cheng Qian, Tianyu Yu, Huanang Gao, Wenkai Yang, Zhiyuan Liu, and Ning Ding. Rethinking on-policy distillation of large language models: Phenomenology, mechanism, and recipe. arXiv preprint arXiv:2604.13016, 2026. URL https://arxiv.org/abs/2604.13016v2. Michael Luo, Sijun Tan, Roy Huang, Ameen Patel, Alpay Ariyak, Qingyang Wu, Xiaoxiang Shi, Rachel X","arxiv_match":true},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"404.12045. URL https://doi.org/10.48550/arXiv.2404.12045. Yaxuan Li, Yuxin Zuo, Bingxiang He, Jinqian Zhang, Chaojun Xiao, Cheng Qian, Tianyu Yu, Huanang Gao, Wenkai Yang, Zhiyuan Liu, and Ning Ding. Rethinking on-policy distillation of large language models: Phenomenology, mechanism, and recipe. CoRR, abs/2604.13016, 2026. doi: 10. 48550/ARXIV.2604.13016. URL https://doi.org/10.48550/arXiv.2604.13016. Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Xuanjing Huang, Hang Yan, Z","arxiv_match":false},{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":" and Sanjeev Arora. Rethinking on-policy self-distillation for thinking models. arXiv preprint arXiv:2607.05184, 2026. URL https: //arxiv.org/abs/2607.05184. Yaxuan Li, Yuxin Zuo, Bingxiang He, Jinqian Zhang, Chaojun Xiao, Cheng Qian, Tianyu Yu, Huanang Gao, Wenkai Yang, Zhiyuan Liu, and Ning Ding. Rethinking on-policy distillation of large language models: Phenomenology, mechanism, and recipe. arXiv preprint arXiv:2604.13016, 2026a. URL https://arxiv.org/abs/2604.13016. Yijiang Li, Bingyang Wang, Yijun Liang, Yunjie Tian, Di Fu, and Nuno Vasconcelos. On-policy self-d","arxiv_match":true}],"id":"2604.13016","metadata_source":"Cached arXiv API metadata","rank":297},{"title":"Training a helpful and harmless assistant with reinforcement learning from human feedback","arxiv_id":"2204.05862","year":2022,"url":"https://arxiv.org/abs/2204.05862","citing_submissions":348,"explicit_arxiv_matches":310,"examples":[{"id":"mVJzfFoRun","title":"Checking Leakage Witnesses versus Certifying Bounded Non-Leakage","forum_url":"https://openreview.net/forum?id=mVJzfFoRun","excerpt":"as Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022. URL https://arxiv.org/abs/2204.05862. Gilles Audemard and Laurent Simon. Predicting learnt clauses quality in modern SAT solvers. In Pro","arxiv_match":true},{"id":"M752QnEbXC","title":"CoPE: Co-Evolving Policy and Experience via Context Distillation for Open-Ended Generation","forum_url":"https://openreview.net/forum?id=M752QnEbXC","excerpt":"nero-Candela, Foivos Tsimpourlas, Michael Sharman, Meghan Shah, Andrea Vallone, Alex Beutel, et al. Healthbench: Evaluating large language models towards improved human health. arXiv preprint arXiv:2505.08775, 2025. Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022. Angelica Chen, Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Samuel R.","arxiv_match":true},{"id":"GGw37srpoS","title":"Safe Representation for Combined Image-Text Generation in Multi-Turn Conversations","forum_url":"https://openreview.net/forum?id=GGw37srpoS","excerpt":", Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923, 2025b. Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022. Boyuan Chen, Donghai Hong, Jiaming Ji, Jiacheng Zheng, Bowen Dong, Jiayi Zhou, Kaile Wang, J","arxiv_match":true}],"id":"2204.05862","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":298},{"title":"Dream 7B: Diffusion Large Language Models","arxiv_id":"2508.15487","year":2025,"url":"https://arxiv.org/abs/2508.15487","citing_submissions":345,"explicit_arxiv_matches":312,"examples":[{"id":"UtSpCnqHk8","title":"Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving","forum_url":"https://openreview.net/forum?id=UtSpCnqHk8","excerpt":"ding. In International Conference on Learning Representations, volume 2026, pp. 57027–57051, 2026b. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7B: Diffusion large language models, 2025. URL https://arxiv.org/abs/ 2508.15487. Haoyu Zheng, Yongqiang Zhang, Fangcheng Fu, Xiaokai Zhou, Hao Luo, Hongchao Zhu, Yuanyuan Zhu, Hao","arxiv_match":true},{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"ght: Chain-of-thought reasoning in diffusion language models. In Advances in Neural Information Processing Systems, volume 37, 2024. URL https://proceedings.neurips.cc/paper_files/paper/2024/ hash/be30024e7fa2c29cac7a6dafcbb8571f-Abstract-Conference.html. Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7B: Diffusion large language models. arXiv preprint arXiv:2508.15487, 2025. URL https://arxiv.org/abs/2508.15487. Longxuan Yu, Shaorong Zhang, Yu Fu, Hui Liu, Yue Dong, and Greg Ver Steeg. Revise, don’t freeze: S","arxiv_match":true},{"id":"6zSuqyB4a9","title":"What Makes a Good Decoding Path? Selecting Parallel-Decodable Trajectories for Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=6zSuqyB4a9","excerpt":"rican Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 22–39, Mexico City, Mexico, June 2024. Association for Computational Linguistics. doi: 10.18653/v1/2024.naacl-long.2. URL https://aclanthology. org/2024.naacl-long.2/. Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7b: Diffusion large language models, 2025. URL https://arxiv.org/abs/ 2508.15487. Shaorong Zhang, Longxuan Yu, Rob Brekelmans, Luhan Tang, Salman Asif, and Greg Ver Steeg. Generati","arxiv_match":true}],"id":"2508.15487","metadata_source":"Cached arXiv API metadata","rank":299},{"title":"Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small","arxiv_id":"2211.00593","year":2022,"url":"https://arxiv.org/abs/2211.00593","citing_submissions":345,"explicit_arxiv_matches":124,"examples":[{"id":"YiJfrsQXZg","title":"FairFlow: Demystifying and Mitigating Stereotype Bias in Text-to-Image Diffusion Transformers","forum_url":"https://openreview.net/forum?id=YiJfrsQXZg","excerpt":"026. Yixin Wan, Arjun Subramonian, Anaelia Ovalle, Zongyu Lin, Ashima Suvarna, Christina Chance, Hritik Bansal, Rebecca Pattichis, and Kai-Wei Chang. Survey of bias in text-to-image generation: Definition, evaluation, and mitigation. arXiv preprint arXiv:2404.01030, 2024. Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt. Interpretability in the wild: a circuit for indirect object identification in gpt-2 small. arXiv preprint arXiv:2211.00593, 2022. Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, and Huajun Chen. Kno","arxiv_match":true},{"id":"WmdhapRKFO","title":"Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits","forum_url":"https://openreview.net/forum?id=WmdhapRKFO","excerpt":"multiple mediators: Hidden interaction effects in activation patching. arXiv preprint, 2026. arXiv:2606.27510. Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt. Interpretability in the wild: a circuit for indirect object identification in GPT-2 small. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview. net/forum?id=NpsVSN6o4ul. Yongzhong Xu. Closure-validated circuit discovery in attention heads: Co","arxiv_match":false},{"id":"89cECgVi4S","title":"RealControlBench: A Comprehensive Benchmark for Fine-Grained LLM and LLM Agent steer in the Wild","forum_url":"https://openreview.net/forum?id=89cECgVi4S","excerpt":"op on Reasoning and Planning for Large Language Models, 2025. URL https://openreview.net/forum? id=OwhVWNOBcz. Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt. Interpretability in the wild: a circuit for indirect object identification in GPT-2 small. In The Eleventh International Conference on Learning Representations (ICLR), 2023. URL https: //openreview.net/forum?id=NpsVSN6o4ul. Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Hu","arxiv_match":false}],"id":"2211.00593","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":300},{"title":"Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection","arxiv_id":"2302.12173","year":2023,"url":"https://arxiv.org/abs/2302.12173","citing_submissions":344,"explicit_arxiv_matches":86,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":" reasoning in language models through multiagent debate, 2023. URL https: //arxiv.org/abs/2305.14325. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection. In Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, pp. 79–90, 2023. doi: 10.1145/3605764.3623985. URL https://dl.acm.org/ doi/10.1145/3605764.3623985. Xinmiao Huang, Ji","arxiv_match":false},{"id":"y5X5ZaufZQ","title":"Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections","forum_url":"https://openreview.net/forum?id=y5X5ZaufZQ","excerpt":"-evolving agents: On path to artificial super intelligence. arXiv preprint arXiv:2507.21046, 1, 2025. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection. In Proceedings of the 16th ACM workshop on artificial intelligence and security, pp. 79–90, 2023. Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Ya","arxiv_match":false},{"id":"TO4R4c6u7V","title":"From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents","forum_url":"https://openreview.net/forum?id=TO4R4c6u7V","excerpt":"ations with indirect prompt injection. In Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, pp. 79–90. ACM, 2023a. doi: 10.1145/3605764.3623985. URL https://doi.org/10.1145/3605764.3623985. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection, 2023b. URL https://arxiv.org/abs/2302.12173. Qi Hu, Yifeng Tang, Qinghua Wang, Lanyang Zhao, Pengji Zhang, Yuhao Qing, Xin Yao, Dong Huang, Lin","arxiv_match":true}],"id":"2302.12173","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":301},{"title":"Do as i can, not as i say: Grounding language in robotic affordances","arxiv_id":"2204.01691","year":2022,"url":"https://arxiv.org/abs/2204.01691","citing_submissions":343,"explicit_arxiv_matches":167,"examples":[{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":" Michael Ahn, Anthony Brohan, Noah Brown, et al. Do as I can, not as I say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691, 2022. Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, and Lihua","arxiv_match":true},{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":"r, Linda Luu, Kuang-Huei Lee, Yuheng Kuang, Sally Jesmonth, Nikhil J. Joshi, Kyle Jeffrey, Rosario Jauregui Ruano, Jasmine Hsu, Keerthana Gopalakrishnan, Byron David, Andy Zeng, and Chuyuan Kelly Fu. Do as i can, not as i say: Grounding language in robotic affordances. In Karen Liu, Dana Kulic, and Jeff Ichnowski (eds.), Proceedings of The 6th Conference on Robot Learning, volume 205 of Proceedings of Machine Learning Research, pp. 287–318. PMLR, 14– 18 Dec 2023. ","arxiv_match":false},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":" Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. Do as i can, not as i say: Grounding language in robotic affordances. In Conference on Robot Learning, 2022. arXiv:2204.01691. Anthropic. Claude Opus 4.8 System Card, 2026. URL https://www-cdn.anthropic.com/ 0b4915911bb0d19ec","arxiv_match":true}],"id":"2204.01691","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":302},{"title":"Generative Adversarial Nets","arxiv_id":null,"year":2014,"url":null,"citing_submissions":343,"explicit_arxiv_matches":0,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":" model decoding and training. arXiv preprint arXiv:2309.17179, 2023. Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. Minillm: Knowledge distillation of large language models. In International Conference on ","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"/2025/ hash/a654298de44ec95dec28aa37e8d0af51-Abstract-Conference.html. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, 2014. URL https://proceedings.neurips.cc/paper_files/paper/2014/ hash/f033ed80deb0234979a61f95710dbe25-Abstract.html. Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Mo","arxiv_match":false},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"g, and motion transfer, 2025. URL https://arxiv.org/abs/2510.03342. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, pp. 2672–2680, Cambridge, MA, USA, 2014. MIT Press. Aaron Grattafiori, Abhimanyu Dub","arxiv_match":false}],"id":"2638280479d2844f","metadata_source":"Curated bibliography catalog / publisher metadata","rank":303},{"title":"Diffusion forcing: Next-token prediction meets full-sequence diffusion","arxiv_id":"2407.01392","year":2024,"url":"https://arxiv.org/abs/2407.01392","citing_submissions":342,"explicit_arxiv_matches":55,"examples":[{"id":"Zf68SX9abN","title":"S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation","forum_url":"https://openreview.net/forum?id=Zf68SX9abN","excerpt":"2026. URL https://arxiv.org/ abs/2503.06800. Black Forest Labs. FLUX.2: Analyzing and enhancing the latent space of FLUX – representation comparison, 2025. URL https://bfl.ai/techblog/ representation-comparison. Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. In Advances in Neural Information Processing Systems, 2024. URL https://arxiv.org/abs/2407. 01392. Jorge Díaz Chao, Konpat Preechakul, Yuxi Liu, and Yutong Bai. The seriality gap in video diffusion","arxiv_match":true},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"ets mean seeking for fast long video generation. In International Conference on Machine Learning, 2026. Boyuan Chen, Diego Martı́ Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. In Advances in Neural Information Processing Systems, 2024. Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng Ma, et al. S","arxiv_match":false},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":"pression for reasoning models. Advances in Neural Information Processing Systems, 38:60980–61005, 2025. Boyuan Chen, Diego Martı́ Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. Advances in Neural Information Processing Systems, 37:24081–24125, 2024. Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng","arxiv_match":false}],"id":"2407.01392","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":304},{"title":"Mme: A comprehensive evaluation benchmark for multimodal large language models","arxiv_id":"2306.13394","year":2023,"url":"https://arxiv.org/abs/2306.13394","citing_submissions":342,"explicit_arxiv_matches":123,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"ation learning. In ICCV, 2025. Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, Rongrong Ji, Caifeng Shan, and Ran He. MME: A comprehensive evaluation benchmark for multimodal large language models. In NeurIPS D&B Track, 2025. Fengyi Fu, Mengqi Huang, Shaojin Wu, Yunsheng Jiang, Yufei Huo, Hao Li, Yinghang Song, Fei Ding, Jianzhu Guo, Qian He, Zheren Fu, Zhendong Mao, and Yongdong Zhang. Lance:","arxiv_match":false},{"id":"iBCBaGASs6","title":"HEAT: Hierarchical Elastic Adaptive Token Sparsification via Depth Scaling","forum_url":"https://openreview.net/forum?id=iBCBaGASs6","excerpt":"caldi, Sonia Vanier, and Aymen Shabou. PACT: pruning and clustering-based token reduction for faster visual language models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14582–14592, 2025. Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, and Rongrong Ji. MME: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13394, 2023. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Z","arxiv_match":true},{"id":"EUwi8JmVHj","title":"Evaluating Vision-Language Models on Images of Individuals with Limb Deficiencies","forum_url":"https://openreview.net/forum?id=EUwi8JmVHj","excerpt":"mber 2021. doi: 10.1109/3DV53792.2021.00088. Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, and Rongrong Ji. MME: A comprehensive evaluation benchmark for multimodal large language models. CoRR, abs/2306.13394, 2023. doi: 10.48550/ ARXIV.2306.13394. URL https://doi.org/10.48550/arXiv.2306.13394. Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model u","arxiv_match":false}],"id":"2306.13394","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":305},{"title":"A decoder-only foundation model for time-series forecasting","arxiv_id":"2310.10688","year":2023,"url":"https://arxiv.org/abs/2310.10688","citing_submissions":339,"explicit_arxiv_matches":74,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":"fferent: An Observability Perspective on Time Series Foundation Models. In Conference on Neural Information Processing Systems (NeurIPS), 2025. Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. A Decoder-Only Foundation Model for Time-Series Forecasting. In International Conference on Machine Learning (ICML), 2024. Zhe Du, Haldun Balim, Samet Oymak, and Necmiye Ozay. Can Transformers Learn Optimal Filtering for Unknown Systems? IEEE Control Systems ","arxiv_match":false},{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"trics and hybrid deep learning. IEEE Transactions on Intelligent Transportation Systems, 23(6):5231–5244, 2022. doi: 10.1109/TITS.2021.3052796. Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. A decoder-only foundation model for time-series forecasting. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 10148–10167. PMLR, 2024. URL https://proceedings.mlr.press/v235/d","arxiv_match":false},{"id":"iaMmGPCgNV","title":"Recursive Inference for Test-Time Compute Allocation in Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=iaMmGPCgNV","excerpt":"5, pp. 102783–102802, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/file/ ff414825df833edb8b1839e3d5d495e9-Paper-Conference.pdf. Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. A decoder-only foundation model for time-series forecasting. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 10148–10167. PMLR, 2024. Mostafa Dehghani, Stephan Gouws, Oriol V","arxiv_match":false}],"id":"2310.10688","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":306},{"title":"Transformers are ssms: Generalized models and efficient algorithms through structured state space duality","arxiv_id":"2405.21060","year":2024,"url":"https://arxiv.org/abs/2405.21060","citing_submissions":339,"explicit_arxiv_matches":133,"examples":[{"id":"Y2v536n2IW","title":"3DSMT++: Towards High-Performance Spike-Driven Point Cloud Learning","forum_url":"https://openreview.net/forum?id=Y2v536n2IW","excerpt":"ng, Yi Yang, Kai Yu, Li Yuan, and Yufeng Yue. Pointgpt: Autoregressively generative pre-training from point clouds. Advances in Neural Information Processing Systems, 36, 2024. Tri Dao and Albert Gu. Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=ztn8FCR1td. Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong ","arxiv_match":false},{"id":"OAX2QPoTJg","title":"HyLin: Modality-Aware Hybrid Attention for Efficient Long-Context Vision–Language Models","forum_url":"https://openreview.net/forum?id=OAX2QPoTJg","excerpt":". Flashattention-2: Faster attention with better parallelism and work partitioning. In International Conference on Learning Representations, volume 2024, pp. 35549–35562, 2024. Tri Dao and Albert Gu. Transformers are SSMs: generalized models and efficient algorithms through structured state space duality. In Proceedings of the 41st International Conference on Machine Learning, pp. 10041–10071, 2024. Soham De, Samuel L. Smith, Anushan Fernando, Aleksandar Botev, George Cristian-Muraru, Albert Gu, Ruba","arxiv_match":false},{"id":"0M6c2bonFJ","title":"QuEST: Quality-guided Evidence Selection and Temporal State Control for Event-based Object Detection","forum_url":"https://openreview.net/forum?id=0M6c2bonFJ","excerpt":" activation sparsity for efficient high-resolution vision transformer. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2061–2070. IEEE, 2023. Tri Dao and Albert Gu. Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 10041–10071. PMLR, 2024. URL https://proceedings.mlr.press/v235/d","arxiv_match":false}],"id":"2405.21060","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":307},{"title":"GLU variants improve transformer","arxiv_id":"2002.05202","year":2020,"url":"https://arxiv.org/abs/2002.05202","citing_submissions":338,"explicit_arxiv_matches":316,"examples":[{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"10.1109/FOCS.2006.37. Y. Shamshoum, N. Hodos, Y. Sieradzki, and A. Schuster. Compact: Compressed activations for memory-efficient llm training, 2024. URL https://arxiv.org/abs/2410.15352. N. Shazeer. Glu variants improve transformer, 2020. N. Shazeer and M. Stern. Adafactor: Adaptive learning rates with sublinear memory cost, 2018. M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro. Megatron-lm: Training multi-","arxiv_match":false},{"id":"FumWWB5wLP","title":"Beyond the Estimate: Wiener-Informed Decoding for Easier Training of Pixel-Space Flow Models","forum_url":"https://openreview.net/forum?id=FumWWB5wLP","excerpt":"ber 2023. J. Portilla, V. Strela, M.J. Wainwright, and E.P. Simoncelli. Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Transactions on Image Processing, 12(11):1338–1351, 2003. doi: 10.1109/TIP.2003.818640. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In NeurIPS, 2016. Noam Shazeer. Glu variants improve transformer, 2020. URL https://arxiv.org/abs/ 2002.05202. Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised lear","arxiv_match":true},{"id":"1P00VnlXQ7","title":"Lossy Projection, Exact Expansion: Retrofitting Pretrained Gated Feed-Forward Blocks","forum_url":"https://openreview.net/forum?id=1P00VnlXQ7","excerpt":"ine Learning Research, 21(140):1–67, 2020. URL http: //jmlr.org/papers/v21/20-074.html. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. WinoGrande: An adversarial Winograd schema challenge at scale. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 8732–8740, 2020. doi: 10.1609/aaai.v34i05.6399. Noam Shazeer. GLU variants improve transformer. arXiv preprint arXiv:2002.05202, 2020. URL https://arxiv.org/abs/2002.05202. Mingjie Sun, Zhuang Liu, Anna Bair, and J. Zico Kolter. A simple and effective pruning approach fo","arxiv_match":true}],"id":"2002.05202","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":308},{"title":"Towards deep learning models resistant to adversarial attacks","arxiv_id":"1706.06083","year":2017,"url":"https://arxiv.org/abs/1706.06083","citing_submissions":338,"explicit_arxiv_matches":88,"examples":[{"id":"KY19PuvbEj","title":"Fragility-Aware Noise Training for Robust Split Computing over Noisy Channels","forum_url":"https://openreview.net/forum?id=KY19PuvbEj","excerpt":"ference on Edge Computing and Communications (EDGE), pp. 84–91. IEEE, 2024. Max Losch, Mohamed Omran, David Stutz, Mario Fritz, and Bernt Schiele. On adversarial training without perturbing all examples. In International Conference on Learning Representations, volume 2024, pp. 47995–48015, 2024. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083, 2017. Yoshitomo Matsubara, Marco Levorato, and Francesco Restuccia. Split computing and early exit","arxiv_match":true},{"id":"lHQyU7Hywl","title":"When and Why Adversarial Training Improves Time-Series Forecasting","forum_url":"https://openreview.net/forum?id=lHQyU7Hywl","excerpt":"ting with variable- and time-aware hyper-state. In International Conference on Machine Learning (ICML), 2025. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations (ICLR), 2018. Takeru Miyato, Andrew M. Dai, and Ian Goodfellow. Adversarial training methods for semisupervised text classification. In Intern","arxiv_match":false},{"id":"krx248kCTE","title":"RAC-WAM: Region-Wise Adversarial Consistency for Visual Robustness in World-Action Models","forum_url":"https://openreview.net/forum?id=krx248kCTE","excerpt":" Qiang Zhang, and Jun Ma. GeoSem-WAM: Geometry- and semantic-aware world action models. arXiv preprint, 2026. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In ICLR, 2018. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. Virtual adversarial training: A regularization method for supervised and semi-supervised learning. TPAMI, 2019. Jingwe","arxiv_match":false}],"id":"1706.06083","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":309},{"title":"Are we on the right way for evaluating large vision-language models?","arxiv_id":"2403.20330","year":2024,"url":"https://arxiv.org/abs/2403.20330","citing_submissions":336,"explicit_arxiv_matches":76,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":" BAAI RoboBrain Team. RoboBrain 2.0 Technical Report. arXiv preprint arXiv:2507.02029, 2025. URL https://arxiv.org/abs/2507.02029. Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, and Feng Zhao. Are we on the right way for evaluating large visionlanguage models? In Advances in Neural Information Processing Systems, volume 37, 2024. URL https://arxiv.org/abs/2403.20330. Junbo Cui, Bokai Xu, Chongyi Wang, Tianyu Yu, Weiyue Sun, Yingjing Xu, Tianran Wang, Zhihui He, We","arxiv_match":true},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"al-World Agency, 2026. URL https://arxiv.org/abs/2603.20633v3. Meng Cao, Haoze Zhao, Can Zhang, Xiaojun Chang, Ian Reid, and Xiaodan Liang. Groundr1: Incentivizing grounded visual reasoning via reinforcement learning. arXiv preprint arXiv:2505.20272, 2025. Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024. Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wan","arxiv_match":true},{"id":"Bb2HSkeepN","title":"An Exam for Active Observers","forum_url":"https://openreview.net/forum?id=Bb2HSkeepN","excerpt":"iners as revealed by eye tracking methodologies. Journal of Forensic Identification, 61(1):60–91, 2011. Liang Chen, Weichu Xie, Yiyan Liang, Hongfeng He, Hans Zhao, et al. Babyvision: Visual reasoning beyond language. arXiv preprint arXiv:2601.06521, 2026. Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024. ChildBench Team. Easy for children, hard for ai: The limits of mllms in early learning tasks","arxiv_match":true}],"id":"2403.20330","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":310},{"title":"Discrete diffusion modeling by estimating the ratios of the data distribution","arxiv_id":"2310.16834","year":2023,"url":"https://arxiv.org/abs/2310.16834","citing_submissions":336,"explicit_arxiv_matches":121,"examples":[{"id":"6SzEZJld4O","title":"Watermarking Continuous Diffusion Language Models","forum_url":"https://openreview.net/forum?id=6SzEZJld4O","excerpt":"n for Computational Linguistics. ISBN 979-8-89176-335-7. doi: 10.18653/v1/2025.findings-emnlp.1148. URL https://aclanthology.org/2025.findings-emnlp.1148/. Aaron Lou, Chenlin Meng, and Stefano Ermon. Discrete diffusion modeling by estimating the ratios of the data distribution. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=CNicRIVIPA. Justin Lovelace, Varsha Kishore, Chao Wan, Eliot Seo Shekhtman, and Kilian Q Weinbe","arxiv_match":false},{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"ding via attention for diffusion LLMs. In Proceedings of the 43rd International Conference on Machine Learning, 2026. URL https://arxiv.org/abs/2603.12996. Aaron Lou, Chenlin Meng, and Stefano Ermon. Discrete diffusion modeling by estimating the ratios of the data distribution. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 32819–32848. PMLR, 2024. URL https://proceedings.mlr.press/v235/l","arxiv_match":false},{"id":"6HZCBDqoCa","title":"DisCoMesh: Efficient and Flexible Artist-Mesh Generation via Sparse Computation powered Discrete Diffusion","forum_url":"https://openreview.net/forum?id=6HZCBDqoCa","excerpt":"generation with autoregressive tree sequencing. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 26608–26617. IEEE, 2025. Aaron Lou, Chenlin Meng, and Stefano Ermon. Discrete diffusion modeling by estimating the ratios of the data distribution. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 32819–32848, 2024. URL https://proceedings.mlr.press/v235/lou24a.","arxiv_match":false}],"id":"2310.16834","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":311},{"title":"Prefix-tuning: Optimizing continuous prompts for generation","arxiv_id":"2101.00190","year":2021,"url":"https://arxiv.org/abs/2101.00190","citing_submissions":336,"explicit_arxiv_matches":43,"examples":[{"id":"nSsaEp75xm","title":"EmbTracker: Traceable Black-box Watermarking for Federated Language Models","forum_url":"https://openreview.net/forum?id=nSsaEp75xm","excerpt":"ated optimization in heterogeneous networks. In Proceedings of the Third Conference on Machine Learning and Systems, MLSys 2020, Austin, TX, USA, March 2-4, 2020, 2020. Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Lon","arxiv_match":false},{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":" Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33: 9459–9474, 2020. Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, pp. 4582–4597, 2021. Zeju Li, Yizhou Zhou, and Qiang Xu. Latent context compilation: Distilling long conte","arxiv_match":false},{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"easuring and reducing malicious use with unlearning. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview. net/forum?id=xlr6AUDuJz. Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference","arxiv_match":false}],"id":"2101.00190","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":312},{"title":"Gemini 3.1 Pro Model Card","arxiv_id":null,"year":2026,"url":"https://deepmind.google/models/model-cards/gemini-3-1-pro","citing_submissions":335,"explicit_arxiv_matches":0,"examples":[{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"arXiv:2602.09082, 2026. Xinzge Gao, Chuanrui Hu, Bin Chen, and Teng Li. Chain-of-memory: Enhancing gui agents for cross-application navigation. arXiv preprint arXiv:2506.18158, 2025. Google DeepMind. Gemini 3.1 Pro Model Card. https://storage.googleapis.com/ deepmind-media/Model-Cards/Gemini-3-1-Pro-Model-Card.pdf, February 2026. Published February 2026; updated 19 February 2026. Boyu Gou, Ruohan Wang, Boyuan Zheng, Yanan","arxiv_match":false},{"id":"qOSFb0s2z4","title":"Do 3D Large Language Models Really Use 3D Evidence?","forum_url":"https://openreview.net/forum?id=qOSFb0s2z4","excerpt":"Wei Gao, and Ge Li. 3D instruction ambiguity detection. arXiv preprint arXiv:2601.05991, 2026. DeepSeek-AI. DeepSeek-V3 technical report, 2024. URL https://arxiv.org/abs/2412. 19437. Google DeepMind. Gemini 3.1 Pro model card. Model card, February 2026. URL https: //deepmind.google/models/model-cards/gemini-3-1-pro/. Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, and Chuang Gan. 3D-LLM: Inje","arxiv_match":false},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"ative design-for-robotassembly using VLM supervision, physics simulation, and a robot with reset. In IEEE International Conference on Robotics and Automation, 2025. arXiv:2409.17126. Google DeepMind. Gemini 3.1 Pro Model Card, 2026. URL //storage.googleapis.com/deepmind-media/Model-Cards/ Gemini-3-1-Pro-Model-Card.pdf. https: Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoen","arxiv_match":false}],"id":"0c610ca3a8082131","metadata_source":"Curated bibliography catalog / publisher metadata","rank":313},{"title":"Learn to explain: Multimodal reasoning via thought chains for science question answering","arxiv_id":"2209.09513","year":2022,"url":"https://arxiv.org/abs/2209.09513","citing_submissions":332,"explicit_arxiv_matches":36,"examples":[{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":" Advances in neural information processing systems, 36:21558–21572, 2023. Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in neural information processing systems, 35:2507–2521, 2022. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, KaiWei Chang, Michel Galley, and Ji","arxiv_match":false},{"id":"W1MVAZmjG8","title":"ReCAP: Retrieval-Guided Capability Reuse for Multimodal Continual Instruction Tuning","forum_url":"https://openreview.net/forum?id=W1MVAZmjG8","excerpt":"ing and visual language reasoning. arXiv preprint arXiv:2110.13214, 2021. Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507–2521, 2022. Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and comprehension of un","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"nified tokenizer for vision, 2025. URL https://arxiv.org/ abs/2509.14476. Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. In NeurIPS, 2022b. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reas","arxiv_match":false}],"id":"2209.09513","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":314},{"title":"How powerful are graph neural networks?","arxiv_id":"1810.00826","year":2018,"url":"https://arxiv.org/abs/1810.00826","citing_submissions":331,"explicit_arxiv_matches":71,"examples":[{"id":"jjf3FKJp3k","title":"MetaBrainPro: A Graph Learning Approach for Enterprise Metadata Lineage Retrieval","forum_url":"https://openreview.net/forum?id=jjf3FKJp3k","excerpt":"lar properties with graph neural networks. arXiv preprint arXiv:1905.13662, 2019. Sinong Wang, Xiaodong Li, Shuai Xuan, and Others. Gqa: A new dataset for multi-hop question answering in context. arXiv preprint arXiv:2205.14767, 2022. Yifei Wang et al. Multi-head attention with adaptive scaling for graph learning. arXiv preprint arXiv:2306.12345, 2023. Keyulu Xu, Weihua Hu, Jure Leskovec, and Sonali Sathe. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018. Zhiqiang Yan et al. Multi-task learning for graph neural networks. arXiv preprint arXiv:2106","arxiv_match":true},{"id":"bQRrUu8i0q","title":"Spectralization: Generalizing Spectral Invariants for Expressive Graph Learning","forum_url":"https://openreview.net/forum?id=bQRrUu8i0q","excerpt":"neesh S Pappu, Karl Leswing, and Vijay Pande. Moleculenet: a benchmark for molecular machine learning. Chemical science, 9(2):513–530, 2018. Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In International Conference on Learning Representations, 2019. URL https: //openreview.net/forum?id=ryGs6iA5Km. Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen","arxiv_match":false},{"id":"8aLXPIB9Ru","title":"A Structural Audit of Missing Propagated Evidence in Temporal Link Prediction","forum_url":"https://openreview.net/forum?id=8aLXPIB9Ru","excerpt":"nd Kannan Achan. Inductive representation learning on temporal graphs. In International Conference on Learning Representations (ICLR), 2020. Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In International Conference on Learning Representations (ICLR), 2019. Le Yu, Leilei Sun, Bowen Du, and Weifeng Lv. Towards better dynamic graph learning: New architecture and unified library. In Adv","arxiv_match":false}],"id":"1810.00826","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":315},{"title":"Diffusion posterior sampling for general noisy inverse problems","arxiv_id":"2209.14687","year":2022,"url":"https://arxiv.org/abs/2209.14687","citing_submissions":330,"explicit_arxiv_matches":94,"examples":[{"id":"zPfUFbzLtK","title":"Numerical Calibration for Physics-Informed Diffusion Posterior Sampling","forum_url":"https://openreview.net/forum?id=zPfUFbzLtK","excerpt":". Physics-informed diffusion models. In International Conference on Learning Representations, volume 2025, pp. 3360–3385, 2025. Yi Bing, Liu Jia, Fu Jinyang, and Peng Xiang. Diffusion models with physics-guided inference for solving partial differential equations. arXiv preprint arXiv:2604.01242, 2026. Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. arXiv preprint arXiv:2209.14687, 2022. Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai, Yuki Mitsufuji, Jong Chul Ye, Peyman Milanfar","arxiv_match":true},{"id":"gsDsA2ZjkO","title":"PhysDEM: Physics-Defined Energy-Matching Diffusion for Spatiotemporal Field Generation under Scarce Measurements","forum_url":"https://openreview.net/forum?id=gsDsA2ZjkO","excerpt":"project: A comparison of upscaling techniques. SPE Reservoir Evaluation & Engineering, 4(04):308–317, 2001. Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. In The Eleventh International Conference on Learning Representations, 2023. Imre Csiszár. I-divergence geometry of probability distributions and minimization problems. The annals of probability, pp","arxiv_match":false},{"id":"2N8aFXk52A","title":"How Much to Trust the Eyes? Reliability-Guided Inference for Diffusion-Based Audio-Visual Speech Enhancement","forum_url":"https://openreview.net/forum?id=2N8aFXk52A","excerpt":"es Robins. Double/Debiased Machine Learning for Treatment and Structural Parameters. The Econometrics Journal, 2018. Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc L. Klasky, and Jong Chul Ye. Diffusion Posterior Sampling for General Noisy Inverse Problems. In International Conference on Learning Representations, 2023. Martin Cooke, Jon Barker, Stuart Cunningham, and Xu Shao. An Audio-Visual Corpus for Speech Perception and Automatic Speech Recognition","arxiv_match":false}],"id":"2209.14687","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":316},{"title":"LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels","arxiv_id":"2603.19312","year":2026,"url":"https://arxiv.org/abs/2603.19312","citing_submissions":330,"explicit_arxiv_matches":303,"examples":[{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"Dan Haramati, Damien Scieur, Yann LeCun, and Randall Balestriero. stable-worldmodel: A platform for reproducible world modeling research and evaluation. arXiv preprint arXiv:2605.21800, 2026a. URL https://arxiv.org/abs/2605.21800. Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, and Randall Balestriero. LeWorldModel: Stable end-to-end joint-embedding predictive architecture from pixels. arXiv preprint arXiv:2603.19312, 2026b. URL https://arxiv.org/abs/2603.19312. Vincent Micheli, Eloi Alonso, and Fran ccois Fleuret. Transformers are sample-efficient world mode","arxiv_match":true},{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":"Dan Haramati, Damien Scieur, Yann LeCun, and Randall Balestriero. stable-worldmodel: A platform for reproducible world modeling research and evaluation. arXiv preprint arXiv:2605.21800, 2026a. URL https://arxiv.org/abs/2605.21800. Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, and Randall Balestriero. LeWorldModel: Stable end-to-end joint-embedding predictive architecture from pixels. arXiv preprint arXiv:2603.19312, 2026b. URL https://arxiv.org/abs/2603.19312. Vivek Myers, Chongyi Zheng, Anca Dragan, Sergey Levine, and Benjamin Eysenbach. Learning temporal ","arxiv_match":true},{"id":"P6EnVJlhxB","title":"What Can Latent World Models Know? Physical Information in Multimodal Predictive Representations","forum_url":"https://openreview.net/forum?id=P6EnVJlhxB","excerpt":"otic planning. arXiv preprint arXiv:2609.03565, 2026c. Lennart Ljung. System Identification: Theory for the User. Prentice Hall PTR, Upper Saddle River, NJ, 2nd edition, 1999. ISBN 978-0-13-656695-3. Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, and Randall Balestriero. LeWorldModel: Stable end-to-end joint-embedding predictive architecture from pixels. arXiv preprint arXiv:2603.19312, 2026. doi: 10.48550/arXiv.2603.19312. URL https://arxiv.org/ abs/2603.19312. Aman Mehta and Riya Baviskar. CALIPER: Clean scenes cannot rank physical inference in pretrained v","arxiv_match":true}],"id":"2603.19312","metadata_source":"Cached arXiv API metadata","rank":317},{"title":"CRITIC: Large language models can self-correct with tool-interactive critiquing","arxiv_id":"2305.11738","year":2023,"url":"https://arxiv.org/abs/2305.11738","citing_submissions":329,"explicit_arxiv_matches":73,"examples":[{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":" et al. Magentic-one: A generalist multi-agent system for solving complex tasks. arXiv preprint arXiv:2411.04468, 2024. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yujiu Yang, Nan Duan, Weizhu Chen, et al. Critic: Large language models can self-correct with tool-interactive critiquing. In International Conference on Learning Representations, volume 2024, pp. 57734–57811, 2024. Bernal J Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspir","arxiv_match":false},{"id":"0LJ106OAjs","title":"Forecasting Before the Outcome: Catalogue Gated Prequential Cognitive Performance under Participant and Domain Shift","forum_url":"https://openreview.net/forum?id=0LJ106OAjs","excerpt":"ily of open time-series foundation models. arXiv preprint arXiv:2402.03885, 2024. URL https://arxiv.org/abs/2402.03885. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yujiu Yang, Nan Duan, Weizhu Chen, et al. Critic: Large language models can self-correct with tool-interactive critiquing. In International Conference on Learning Representations, volume 2024, pp. 57734–57811, 2024. Shurui Gui, Xiner Li, and Shuiwang Ji. Active test-time adaptation: Theoretical analyses and an algorithm","arxiv_match":false},{"id":"ZMK44fI9vu","title":"Sleep as a Window: Auditable LLM Decoding of Aging Signatures from Integrated Sleep Profiles","forum_url":"https://openreview.net/forum?id=ZMK44fI9vu","excerpt":"hods in Natural Language Processing, pp. 10932–10952, 2023. https://doi.org/10. 18653/v1/2023.emnlp-main.674. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen. CRITIC: Large language models can self-correct with tool-interactive critiquing. In International Conference on Learning Representations, 2024. https://openreview.net/forum?id= Sx038qxjek. Paul Hager, Friederike Jungmann, Robbie Holland, Kunal Bhagat, Inga Hubrecht, Manuel Knaue","arxiv_match":false}],"id":"2305.11738","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":318},{"title":"GLUE: A multi-task benchmark and analysis platform for natural language understanding","arxiv_id":"1804.07461","year":2018,"url":"https://arxiv.org/abs/1804.07461","citing_submissions":329,"explicit_arxiv_matches":38,"examples":[{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"Jaggi. Powersgd: Practical low-rank gradient compression for distributed optimization, 2020. URL https://arxiv.org/abs/1905.13727. H. Wang, S. Sievert, Z. Charles, S. Liu, S. Wright, and D. Papailiopoulos. Atomo: Communicationefficient learning via atomic sparsification, 2018. A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding, 2019. URL https://arxiv.org/ abs/1804.07461. P.-Å. Wedin. Perturbation bounds in connection with singular value decomposition. BIT Numerical Ma","arxiv_match":true},{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"ria, July 21-27, 2024, Proceedings of Machine Learning Research, pp. 48630–48656. PMLR / OpenReview.net, 2024. Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019. Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. Codet5: Iden","arxiv_match":false},{"id":"vFk3Vx7BZt","title":"TriBLoRA: Tri-Factor Blockwise Low-Rank Adaptation","forum_url":"https://openreview.net/forum?id=vFk3Vx7BZt","excerpt":" Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 5797–5808, 2019. Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In International Conference on Learning Representations, 2019. URL https://openreview. net/forum?id=rJ4km2R5t7. Taiqiang Wu, Jiahao Wang, Zhe Zhao, and Ngai Wong. Mixture-of-subspaces in low-rank ad","arxiv_match":false}],"id":"1804.07461","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":319},{"title":"VICReg: Variance-invariance-covariance regularization for self-supervised learning","arxiv_id":"2105.04906","year":2021,"url":"https://arxiv.org/abs/2105.04906","citing_submissions":329,"explicit_arxiv_matches":88,"examples":[{"id":"mlVqTuV7b8","title":"Closing the Loop on Masking Difficulty for Joint-Embedding Predictive Learning","forum_url":"https://openreview.net/forum?id=mlVqTuV7b8","excerpt":" learning with masked autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14507–14517, June 2023. Adrien Bardes, Jean Ponce, and Yann LeCun. Vicreg: Variance-invariance-covariance regularization for self-supervised learning. In International Conference on Learning Representations (ICLR), 2022. BARL-SSL. Reptrix: α-ReQ implementation. https://github.com/BARL-SSL/reptrix /blob/main/reptrix/alpha.py, n.d. Mathilde Caron, H","arxiv_match":false},{"id":"4AEJkVBst5","title":"What Should Consistency Preserve? Readout-Geometric Information Transport for Semi-Supervised Regression","forum_url":"https://openreview.net/forum?id=4AEJkVBst5","excerpt":" Nabiha Asghar. Yelp dataset challenge: Review rating prediction. arXiv preprint arXiv:1605.05362, 2016. Adrien Bardes, Jean Ponce, and Yann LeCun. Vicreg: Variance-invariance-covariance regularization for self-supervised learning. arXiv preprint arXiv:2105.04906, 2021. Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. Manifold regularization: A geometric fra","arxiv_match":true},{"id":"itoxkUzY2n","title":"MEND: Measuring and Constraining Latent Dynamics in World Models","forum_url":"https://openreview.net/forum?id=itoxkUzY2n","excerpt":"0. Randall Balestriero and Yann LeCun. Lejepa: Provable and scalable self-supervised learning without the heuristics. arXiv preprint arXiv:2511.08544, 2025. Adrien Bardes, Jean Ponce, and Yann LeCun. VICReg: Variance-invariance-covariance regularization for self-supervised learning. International Conference on Learning Representations (ICLR), 2022. Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, and Nicolas Ballas. V-JEPA: Laten","arxiv_match":false}],"id":"2105.04906","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":320},{"title":"Qwen2.5-Omni Technical Report","arxiv_id":"2503.20215","year":2025,"url":"https://arxiv.org/abs/2503.20215","citing_submissions":328,"explicit_arxiv_matches":290,"examples":[{"id":"do6Wfg04EK","title":"ATTEST: Evidence-Grounded Reinforcement Learning for Long-Audio Temporal Understanding","forum_url":"https://openreview.net/forum?id=do6Wfg04EK","excerpt":"d Mengyue Wu. Audiotime: A temporally-aligned audio-text benchmark dataset. In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5, 2025. doi: 10.1109/ ICASSP49660.2025.10889879. Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, and Junyang Lin. Qwen2.5-omni technical report, 2025a. URL https://arxiv.org/abs/2503.20215. Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting ","arxiv_match":true},{"id":"eeqPXcVTnJ","title":"MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy","forum_url":"https://openreview.net/forum?id=eeqPXcVTnJ","excerpt":"uayang Li, Jialu Xu, Yan Wang, and Deng Cai. Pandagpt: One model to instruction-follow them all. In TLLM, 2023. Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun Ma, and Chao Zhang. Salmonn: Towards generic hearing abilities for large language models. In ICLR, 2024. Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, et al. Qwen2.5-omni technical report. arXiv preprint arXiv:2503.20215, 2025a. Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi","arxiv_match":true},{"id":"7EtSASfnVn","title":"OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning","forum_url":"https://openreview.net/forum?id=7EtSASfnVn","excerpt":"Xiv preprint arXiv:2603.19217, 2026. Ziyue Wang, Sheng Jin, Zhongrong Zuo, Jiawei Wu, Han Qiu, Qi She, Hao Zhang, and Xudong Jiang. Video-KTR: Reinforcing Video Reasoning via Key Token Attribution. arXiv preprint arXiv:2601.19686, 2026. Xiaomi. Introducing MiMo-V2.5, April 2026. mimo-v2-5/. URL https://mimo.xiaomi.com/ Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, et al. Qwen2.5-Omni Technical Report. arXiv preprint arXiv:2503.20215, 2025a. Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi","arxiv_match":true}],"id":"2503.20215","metadata_source":"Cached arXiv API metadata","rank":321},{"title":"The linear representation hypothesis and the geometry of large language models","arxiv_id":"2311.03658","year":2023,"url":"https://arxiv.org/abs/2311.03658","citing_submissions":328,"explicit_arxiv_matches":112,"examples":[{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"plications-for-2026/. Alexander Pan, Lijie Chen, and Jacob Steinhardt. LatentQA: Teaching LLMs to decode activations into natural language. In ICLR, 2026. Kiho Park, Yo Joong Choe, and Victor Veitch. The linear representation hypothesis and the geometry of large language models. In ICML, 2024. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-","arxiv_match":false},{"id":"fXt5rH6mod","title":"Position: The Linear Representation Hypothesis Needs a Group Action","forum_url":"https://openreview.net/forum?id=fXt5rH6mod","excerpt":"ained environments. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025a. URL https://openreview.net/forum?id=uBaFH7aQnC. Kiho Park, Yo Joong Choe, and Victor Veitch. The linear representation hypothesis and the geometry of large language models. In Proceedings of the 41st International Conference on Machine Learning, pp. 39643–39666, 2024. URL https://proceedings.mlr.press/v235/ park24c.html. Kiho Park, Yo Joong Choe, Yibo Jiang, and Victor","arxiv_match":false},{"id":"SjdbGaqsvq","title":"TADA! Tuning Audio Diffusion Models through Activation Steering","forum_url":"https://openreview.net/forum?id=SjdbGaqsvq","excerpt":", Qihui Yang, and Randal Leistikow. Learning interpretable features in audio latent spaces via sparse autoencoders. arXiv preprint arXiv:2510.23802, 2025. Kiho Park, Yo Joong Choe, and Victor Veitch. The linear representation hypothesis and the geometry of large language models. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=UGpGkLzwpP. Judea Pearl. Direct and indirect effects. In Proceedings of the Seventeenth Confere","arxiv_match":false}],"id":"2311.03658","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":322},{"title":"TIES-Merging: Resolving interference when merging models","arxiv_id":"2306.01708","year":2023,"url":"https://arxiv.org/abs/2306.01708","citing_submissions":327,"explicit_arxiv_matches":38,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"VR capabilities across domains: A deep dive into fusion paradigms, 2026b. URL https://arxiv.org/abs/26 08.27409v1. Version 1. Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal. TIES-merging: Resolving interference when merging models. In Advances in Neural Information Processing Systems, volume 36, pp. 7093–7115. Curran Associates, Inc., 2023. doi: 10.52202/075280-0310. URL https://proceedings.neurips.cc/paper_files/paper/2023/fi","arxiv_match":false},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"Zheng, Jie Song, Li Sun, and Mingli Song. Training-free heterogeneous model merging. arXiv preprint arXiv:2501.00061, 2024. Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal. Ties-merging: Resolving interference when merging models. Advances in neural information processing systems, 36:7093–7115, 2023. Sihan Yang, Kexuan Shi, and Weiyang Liu. arXiv:2602.05943, 2026. Orthogonal model merging. arXiv preprint Kotaro Yoshida, Yuji ","arxiv_match":false},{"id":"OFVWcaGfdi","title":"The Delta Is Not the Skill: Zero-Prior Elicitation Transfers LoRA Behavior Across Model Families","forum_url":"https://openreview.net/forum?id=OFVWcaGfdi","excerpt":"ing aligned llms with nothing. In International Conference on Learning Representations, volume 2025, pp. 76346–76382, 2025. Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal. Ties-merging: Resolving interference when merging models. Advances in neural information processing systems, 36: 7093–7115, 2023. Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, Joseph E Gonzalez, and Ion Stoica. Rethinking benchmark and contamination for langua","arxiv_match":false}],"id":"2306.01708","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":323},{"title":"InjecAgent: Benchmarking indirect prompt injections in tool-integrated large language model agents","arxiv_id":"2403.02691","year":2024,"url":"https://arxiv.org/abs/2403.02691","citing_submissions":325,"explicit_arxiv_matches":47,"examples":[{"id":"TO4R4c6u7V","title":"From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents","forum_url":"https://openreview.net/forum?id=TO4R4c6u7V","excerpt":"ng Dong. Zombie agents: Persistent control of self-evolving llm agents via self-reinforcing injections. arXiv preprint arXiv:2602.15654, 2026. Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. InjecAgent: Benchmarking indirect prompt injections in tool-integrated large language model agents. In Findings of the Association for Computational Linguistics: ACL 2024, pp. 10471–10506. Association for Computational Linguistics, 2024. doi: 10.18653/v1/2024.findings- acl.624. URL https: //aclant","arxiv_match":false},{"id":"Uowq0ts0EJ","title":"AdaGuard: An Adaptive Guard Model with User-defined Policies","forum_url":"https://openreview.net/forum?id=Uowq0ts0EJ","excerpt":"yush Kumar, Bhaktipriya Radharapu, et al. Shieldgemma: Generative ai content moderation based on gemma. arXiv preprint arXiv:2407.21772, 2024. Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents. In Findings of the Association for Computational Linguistics: ACL 2024, pp. 10471–10506, 2024. Haiquan Zhao, Chenhan Yuan, Fei Huang, Xiaomeng Hu, Yichang Zhang, An Yang, Bowen Yu, Dayiheng Liu, Jin","arxiv_match":false},{"id":"oJ7JcFgeXE","title":"Recovering Structural and Semantic Constraints for LLM Agent Anomaly Detection","forum_url":"https://openreview.net/forum?id=oJ7JcFgeXE","excerpt":"un Zeng, Xiang Wang, Jiahao Liu, Yinfang Chen, Zhenkai Liang, Tat-Seng Chua, and Zheng Leong Chua. Shadewatcher: Recommendation-guided cyber threat analysis using system audit records. In 2022 IEEE Symposium on Security and Privacy (SP), pp. 489–506, 2022. doi: 10.1109/ SP46214.2022.9833669. Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents. arXiv preprint arXiv:2403.02691, 2024. Yusheng Zheng, Yanpeng Hu, Tong Yu, and Andi Quinn. Agentsight: System-level observability f","arxiv_match":true}],"id":"2403.02691","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":324},{"title":"CAMEL: Communicative agents for “mind” exploration of large language model society","arxiv_id":"2303.17760","year":2023,"url":"https://arxiv.org/abs/2303.17760","citing_submissions":324,"explicit_arxiv_matches":52,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"t social intelligence risks in generative multi-agent systems, 2026b. URL https://arxiv.org/abs/2603.27771. Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. CAMEL: Communicative agents for “Mind” exploration of large language model society. In Advances in Neural Information Processing Systems 36, pp. 51991–52008, 2023. doi: 10.52202/075280-2264. URL https://proceedings.neurips.cc/paper_files/paper/2023/hash/ 9d23c60a0f9f4fa0dd0f5a0c0c7","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"t: A million-scale, highquality multimodal dataset for robust chart understanding. arXiv preprint arXiv:2603.27064, 2026. Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for” mind” exploration of large language model society. Advances in neural information processing systems, 36:51991–52008, 2023. Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen, Dongmei Jiang, and Liqiang Nie. Optimus-1: Hybrid multimodal memory empowered","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"an Fischer. A human-inspired reading agent with gist memory of very long contexts. arXiv preprint arXiv:2402.09727, 2024. Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for\" mind\" exploration of large language model society. Advances in neural information processing systems, 36:51991–52008, 2023. Hanchen Li, Runyuan He, Qizheng Zhang, Changxiu Ji, Qiuyang Mang, Xiaokun Chen, Lakshya A Agrawal, Wei-Liang Liao, Eric Yang,","arxiv_match":false}],"id":"2303.17760","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":325},{"title":"Droid: A large-scale in-the-wild robot manipulation dataset","arxiv_id":"2403.12945","year":2024,"url":"https://arxiv.org/abs/2403.12945","citing_submissions":324,"explicit_arxiv_matches":171,"examples":[{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"ctions for robot policy learning. In CoRL, 2026. Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, et al. DROID: A large-scale in-the-wild robot manipulation dataset. In RSS, 2024. Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin","arxiv_match":false},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":". Wang, Yilin Wu, Annie Xie, Jingyun Yang, Patrick Yin, Yunchu Zhang, Osbert Bastani, Glen Berseth, Jeannette Bohg, Ken Goldberg, Abhinav Gupta, Abhishek Gupta, Dinesh Jayaraman, Joseph J Lim, Jitendra Malik, Roberto Martı́n-Martı́n, Subramanian Ramamoorthy, Dorsa Sadigh, Shuran Song, Jiajun Wu, Michael C. Yip, Yuke Zhu, Thomas Kollar, Sergey Levine, and Chelsea Finn. Droid: A large-scale in-the-wild robot manipulation dataset, 2025. URL https://arxiv.org/abs/ 2403.12945. Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael ","arxiv_match":true},{"id":"dVXFFMVuTo","title":"ManiUnit: A Manipulation Skill Dataset and Benchmark for Long-Horizon Tasks","forum_url":"https://openreview.net/forum?id=dVXFFMVuTo","excerpt":"ng language in robotic affordances. In Proceedings of The 6th Conference on Robot Learning, volume 205 of Proceedings of Machine Learning Research, pp. 287–318. PMLR, 2023. Alexander Khazatsky et al. DROID: A large-scale in-the-wild robot manipulation dataset. In Proceedings of Robotics: Science and Systems, 2024. Moo Jin Kim et al. OpenVLA: An open-source vision-language-action model. In Proceedings of The 8th Conference on Robot Learning, volume 270 of ","arxiv_match":false}],"id":"2403.12945","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":326},{"title":"Kimi K2.5: Visual Agentic Intelligence","arxiv_id":"2602.02276","year":2026,"url":"https://arxiv.org/abs/2602.02276","citing_submissions":323,"explicit_arxiv_matches":292,"examples":[{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"efficient rlhf framework. arXiv preprint arXiv: 2409.19256, 2024. Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-lm: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053, 2019. Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, SH Cai, Yuan Cao, Y Charles, HS Che, Cheng Chen, Guanduo Chen, et al. Kimi k2.5: Visual agentic intelligence. arXiv preprint arXiv:2602.02276, 2026. Lev Telyatnikov, Guillermo Bernardez, Marco Montagna, Mustafa Hajij, Martin Carrasco, Pavlo ","arxiv_match":true},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"arlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan. SWE-bench: Can language models resolve real-world github issues? In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview. net/forum?id=VTF8yNQM66. Kimi, Tongtong Bai, Yifan Bai, Yiping Bao, SH Cai, Yuan Cao, Y Charles, HS Che, Cheng Chen, Guanduo Chen, et al. Kimi k2. 5: Visual agentic intelligence. arXiv preprint arXiv:2602.02276, 2026. Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta, John Canny, and Ian Fischer. A human-inspired re","arxiv_match":true},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"irshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR, 2017. Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1601– 1611, 2017. K2.5. Kimi k2.5: Visual agentic intelligence. arXiv preprint arXiv:2602.02276, 2026. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris ","arxiv_match":true}],"id":"2602.02276","metadata_source":"Cached arXiv API metadata","rank":327},{"title":"Mean Flows for One-step Generative Modeling","arxiv_id":"2505.13447","year":2025,"url":"https://arxiv.org/abs/2505.13447","citing_submissions":322,"explicit_arxiv_matches":113,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"ter parallelism and work partitioning. In International Conference on Learning Representations, volume 2024, pp. 35549–35562, 2024. Juechu Dong, Boyuan Feng, Driss Guessous, Yanbo Liang, and Horace He. Flex attention: A programming model for generating optimized attention kernels. arXiv preprint arXiv:2412.05496, 2(3):4, 2024. Zhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling. arXiv preprint arXiv:2505.13447, 2025. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. G","arxiv_match":true},{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"n augmented end-to-end planning. In International Conference on Learning Representations, volume 2026, pp. 37722–37737, 2026. Zhengyang Geng, Mingyang Deng, Xingjian Bai, Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling. Advances in Neural Information Processing Systems, 38:75460– 75482, 2026. Yanjiang Guo, Yucheng Hu, Jianke Zhang, Yen-Jen Wang, Xiaoyu Chen, Chaochao Lu, and Jianyu Chen. Prediction with action: Vis","arxiv_match":false},{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":" Shubham Tulsiani, Bryan Russell, and Angjoo Kanazawa. Monocular dynamic view synthesis: A reality check. In NeurIPS, 2022. Zhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=uWj4s7rMnR. Zhengyang Geng, Yiyang Lu, Zongze Wu, Eli Shechtman, J Zico Kolt","arxiv_match":false}],"id":"2505.13447","metadata_source":"Cached arXiv API metadata","rank":328},{"title":"Causal World Modeling for Robot Control","arxiv_id":"2601.21998","year":2026,"url":"https://arxiv.org/abs/2601.21998","citing_submissions":321,"explicit_arxiv_matches":246,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"cy Liang, Shuran Song, Ming-Yu Liu, Chelsea Finn, and Jinwei Gu. Cosmos policy: Fine-tuning video models for visuomotor control and planning. arXiv preprint arXiv:2601.16163, 2026. URL https://arxiv.org/abs/2601.16163. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, Yujun Shen, and Yinghao Xu. Causal world modeling for robot control. arXiv preprint arXiv:2601.21998, 2026a. URL https://arxiv.org/abs/2601.21998. Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model. In Proceedings o","arxiv_match":true},{"id":"FZ7Aq4uPre","title":"UniWAM: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision","forum_url":"https://openreview.net/forum?id=FZ7Aq4uPre","excerpt":"Technical report, 2025. URL https://internrobotics.github.io/ internvla-n1.github.io/. Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin, Yen-Chen Lin, Yunhao Ge, Grace Lam, Percy Liang, Shuran Song, et al. Cosmos policy: Fine-tuning video models for visuomotor control and planning. arXiv preprint arXiv:2601.16163, 2026. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, et al. Causal world modeling for robot control. arXiv preprint arXiv:2601.21998, 2026. Weixin Liang, Lili Yu, Liang Luo, Srinivasan Iyer, Ning Dong, Chunting Zhou, et al. Mixtureo","arxiv_match":true},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":". arXiv preprint arXiv:2502.19645, 2025. Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin, Yen-Chen Lin, Yunhao Ge, Grace Lam, Percy Liang, Shuran Song, Ming-Yu Liu, Chelsea Finn, et al. Cosmos policy: Fine-tuning video models for visuomotor control and planning. arXiv preprint arXiv:2601.16163, 2026. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, et al. Causal world modeling for robot control. arXiv preprint arXiv:2601.21998, 2026. Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sich","arxiv_match":true}],"id":"2601.21998","metadata_source":"Cached arXiv API metadata","rank":329},{"title":"Efficiently modeling long sequences with structured state spaces","arxiv_id":"2111.00396","year":2021,"url":"https://arxiv.org/abs/2111.00396","citing_submissions":320,"explicit_arxiv_matches":125,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"yi Li, Tianqing Fang, Hongming Zhang, Haitao Mi, Dong Yu, and Zhicheng Dou. Unigist: Towards general and hardware-aligned sequence-level long context compression. arXiv preprint arXiv:2509.15763, 2025. Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arxiv. arXiv preprint arXiv:2312.00752, 10, 2023. Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Z","arxiv_match":true},{"id":"mlS6HwwCm6","title":"SCOPE: Spectral-Conditioned Omic-Pathology Encoder for Interpretable Multimodal Cancer Survival Analysis","forum_url":"https://openreview.net/forum?id=mlS6HwwCm6","excerpt":"D692, 2022. Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. In First Conference on Language Modeling (COLM), 2024. Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. In International Conference on Learning Representations (ICLR), 2022. Maximilian Ilse, Jakub M. Tomczak, and Max Welling. Attention-based deep multiple instance learning. In Proceedings of the 35th ","arxiv_match":false},{"id":"0M6c2bonFJ","title":"QuEST: Quality-guided Evidence Selection and Temporal State Control for Event-based Object Detection","forum_url":"https://openreview.net/forum?id=0M6c2bonFJ","excerpt":"Linear-time sequence modeling with selective state spaces. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id= tEYskw1VY2. Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. In International Conference on Learning Representations, 2022. URL https: //openreview.net/forum?id=uYLFoz1vlAC. Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji La","arxiv_match":false}],"id":"2111.00396","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":330},{"title":"Improving and generalizing flow-based generative models with minibatch optimal transport","arxiv_id":"2302.00482","year":2023,"url":"https://arxiv.org/abs/2302.00482","citing_submissions":320,"explicit_arxiv_matches":106,"examples":[{"id":"jJbc2wpXoo","title":"Scaling Online Optimal Transport Minibatches for Flow Matching","forum_url":"https://openreview.net/forum?id=jJbc2wpXoo","excerpt":"ration error for flow matching. arXiv preprint arXiv:2605.06680, 2026. Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid RectorBrooks, Guy Wolf, and Yoshua Bengio. Improving and generalizing flow-based generative models with minibatch optimal transport. In TMLR, 2024. Jonathan Weed. An explicit analysis of the entropic penalty in linear programming. In COLT, 2018. Felix X-F Ye, Xingjie Li, An Yu, Ming-Ching Chang, Linsong Chu, and Davis Wertheimer.","arxiv_match":false},{"id":"z7tlMaVseU","title":"Prototype-Routed Flow Matching for Incomplete Multi-View Clustering","forum_url":"https://openreview.net/forum?id=z7tlMaVseU","excerpt":"rnational conference on machine learning, pp. 21090–21110. PMLR, 2022. Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid RectorBrooks, Guy Wolf, and Yoshua Bengio. Improving and generalizing flow-based generative models with minibatch optimal transport. Transactions on Machine Learning Research, 2024. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. Q","arxiv_match":false},{"id":"yobwGzqlyW","title":"Efficient Point Cloud Upsampling via Local Flow Matching","forum_url":"https://openreview.net/forum?id=yobwGzqlyW","excerpt":" matrices. The Annals of Mathematical Statistics, 35(2):876–879, 1964. Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid RectorBrooks, Guy Wolf, and Yoshua Bengio. Improving and generalizing flow-based generative models with minibatch optimal transport. Transactions on Machine Learning Research, 2024. Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. Dynamic graph cnn for learning on point clouds. ACM T","arxiv_match":false}],"id":"2302.00482","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":331},{"title":"Token merging: Your vit but faster","arxiv_id":"2210.09461","year":2022,"url":"https://arxiv.org/abs/2210.09461","citing_submissions":318,"explicit_arxiv_matches":111,"examples":[{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":" memory for robot navigation. In 2025 IEEE International Conference on Robotics and Automation (ICRA), pp. 2838–2845. IEEE, 2025. Leonard Bärmann, Joana Plewnia, Alex Waibel, and Tamim Asfour. Learning to forget–hierarchical episodic memory for lifelong robot deployment. arXiv preprint arXiv:2604.11306, 2026. Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster. arXiv preprint arXiv:2210.09461, 2022. Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. A","arxiv_match":true},{"id":"QiYD616GNr","title":"Fast-ZSAD: Patch-wise Adaptive Inference for Efficient Zero-Shot Anomaly Detection","forum_url":"https://openreview.net/forum?id=QiYD616GNr","excerpt":"nce on Computer Vision and Pattern Recognition (CVPR), June 2019. Benjamin Bergner, Christoph Lippert, and Aravindh Mahendran. Token cropr: Faster vits for quite a few tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9740–9750, June 2025. Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster, 2023. URL https://arxiv.org/abs/ 2210.09461. Han Cai, Junyan Li, Muyan Hu, Chuang Gan, and Song Han. Efficientvit: Lightweight multi-scale atte","arxiv_match":true},{"id":"YzI2kFui6F","title":"CoMo: Efficient Video Transformer via Codec Residual and Motion-Field Geometry","forum_url":"https://openreview.net/forum?id=YzI2kFui6F","excerpt":"dings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4599–4603, 2023. Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your ViT but faster. In International Conference on Learning Representations, 2023. Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in se","arxiv_match":false}],"id":"2210.09461","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":332},{"title":"Training diffusion models with reinforcement learning","arxiv_id":"2305.13301","year":2023,"url":"https://arxiv.org/abs/2305.13301","citing_submissions":318,"explicit_arxiv_matches":97,"examples":[{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":", Tianshi Cao, Elizabeth Cha, Yu-Wei Chao, et al. World simulation with video foundation models for physical ai. arXiv, 2025. Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. In ICLR, 2024. Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Sc","arxiv_match":false},{"id":"qNhfYunjC7","title":"FASE: Factor-Aware Spectral Evolution for Gradient-Free Continuation of Diffusion Post-Training","forum_url":"https://openreview.net/forum?id=qNhfYunjC7","excerpt":" Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. In International Conference on Learning Representations, volume 2024, pp. 4965–4987, 2024. Yuning Du, Chenxia Li, Ruoyu Guo, Xiaoting Yin, Weiwei Liu, Jun Zhou, Yifan Bai, Zilin Yu, Yehua Yang, Qing","arxiv_match":false},{"id":"tSpuyBaWWf","title":"Displacement Matching: One Objective for Reward Learning and Distillation in Few-Step Generators","forum_url":"https://openreview.net/forum?id=tSpuyBaWWf","excerpt":"ich, and Jakiw Pidstrigach. Reinforce adjoint matching: Scaling rl post-training of diffusion and flow-matching models, 2026. Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. In International Conference on Learning Representations, 2024. Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet. Directly fine-tuning diffusion models on differentiable rewards. arXiv prepr","arxiv_match":false}],"id":"2305.13301","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":333},{"title":"Picka-Pic: An Open Dataset of User Preferences for Text-to-Image Generation","arxiv_id":"2305.01569","year":2023,"url":"https://arxiv.org/abs/2305.01569","citing_submissions":317,"explicit_arxiv_matches":43,"examples":[{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 18399–18410, 2025. Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Picka-pic: An open dataset of user preferences for text-to-image generation. Advances in neural information processing systems, 36:36652–36663, 2023. Binxu Li, Minkai Xu, Jiaqi Han, Meihua Dang, and Stefano Ermon. Divergence minimization preference optimization for diffusion","arxiv_match":false},{"id":"qNhfYunjC7","title":"FASE: Factor-Aware Spectral Evolution for Gradient-Free Continuation of Diffusion Post-Training","forum_url":"https://openreview.net/forum?id=qNhfYunjC7","excerpt":"g, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Picka-pic: An open dataset of user preferences for text-to-image generation. Advances in neural information processing systems, 36:36652–36663, 2023. Daria Korotyshova, Boris Shaposhnikov, Alexey Malakhov, Alexey Khokhulin, Nikita Surnachev, Kirill Ovcharenko, George Bredis,","arxiv_match":false},{"id":"tSpuyBaWWf","title":"Displacement Matching: One Objective for Reward Learning and Distillation in Few-Step Generators","forum_url":"https://openreview.net/forum?id=tSpuyBaWWf","excerpt":"efano Ermon. Consistency trajectory models: Learning probability flow ODE trajectory of diffusion. ICLR, 2024. Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Picka-pic: An open dataset of user preferences for text-to-image generation. In Advances in Neural Information Processing Systems, 2023. Branislav Kveton, Anup Rao, Subhojyoti Mukherjee, Krishna Kumar Singh, and Viet Dac Lai. Advantageflow: Advantage-weighted least squares f","arxiv_match":false}],"id":"2305.01569","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":334},{"title":"FAST: Efficient Action Tokenization for Vision-Language-Action Models","arxiv_id":"2501.09747","year":2025,"url":"https://arxiv.org/abs/2501.09747","citing_submissions":316,"explicit_arxiv_matches":220,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"91. IEEE, 2025. Alexander Nikulin, Ilya Zisman, Denis Tarasov, Nikita Lyubaykin, Andrei Polubarov, Igor Kiselev, and Vladislav Kurenkov. Latent action learning requires supervision in the presence of distractors. arXiv preprint arXiv:2502.00379, 2025. Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action models. arXiv preprint arXiv:2501.09747, 2025. Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou, and Jeannette Bohg. Consistency policy: Acc","arxiv_match":true},{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"anzhi Wang, Zu Wang, Jing Wang, Qi Wang, Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhenjia Xu, Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, and Yuke Zhu. Gr00t n1: An open foundation model for generalist humanoid robots, 2025. Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action models. arXiv preprint arXiv:2501.09747, 2025. Yiran Qin, Li Kang, Xiufeng Song, Zhenfei Yin, Xiaohong Liu, Xihui Liu, Ruimao Zhang, and Le","arxiv_match":true},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":", pp. 6892–6903. IEEE, 2024. Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. FiLM: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018. Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. FAST: Efficient action tokenization for vision-language-action models. arXiv preprint arXiv:2501.09747, 2025. Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang, JiaYuan Gu","arxiv_match":true}],"id":"2501.09747","metadata_source":"Cached arXiv API metadata","rank":335},{"title":"Improved distribution matching distillation for fast image synthesis","arxiv_id":"2405.14867","year":2024,"url":"https://arxiv.org/abs/2405.14867","citing_submissions":316,"explicit_arxiv_matches":48,"examples":[{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":" vs. generation: Taming optimization dilemma in latent diffusion models. In CVPR, 2025. Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and William T. Freeman. Improved distribution matching distillation for fast image synthesis. In NeurIPS, 2024a. URL https://openreview.net/forum?id=tQukGCDaNT. Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Frédo Durand, William T Freeman, and Taesung Park. One-step diffusion ","arxiv_match":false},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"ideo diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024. Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and William T Freeman. Improved distribution matching distillation for fast image synthesis. Advances in neural information processing systems, 37:47455–47487, 2024a. Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diff","arxiv_match":false},{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":"anardag. Dynamic view synthesis as an inverse problem. arXiv preprint arXiv:2506.08004, 2025. Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and Bill Freeman. Improved distribution matching distillation for fast image synthesis. Advances in neural information processing systems, 37:47455–47487, 2024a. Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diff","arxiv_match":false}],"id":"2405.14867","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":336},{"title":"Meta-Harness: End-to-End Optimization of Model Harnesses","arxiv_id":"2603.28052","year":2026,"url":"https://arxiv.org/abs/2603.28052","citing_submissions":315,"explicit_arxiv_matches":263,"examples":[{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"ge model serving with pagedattention. In Proceedings of the 29th Symposium on Operating Systems Principles, SOSP ’23, pp. 611–626, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400702297. doi: 10.1145/3600006.3613165. URL https://doi.org/ 10.1145/3600006.3613165. Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, and Chelsea Finn. MetaHarness: End-to-End Optimization of Model Harnesses, 2026. URL https://arxiv.org/ abs/2603.28052. arXiv preprint. Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Josep","arxiv_match":true},{"id":"YAVRc0xfa9","title":"Game-Evolver: Dual-Loop Evolution Elicits Better End-to-End Game Orchestration","forum_url":"https://openreview.net/forum?id=YAVRc0xfa9","excerpt":"e on Learning Representations, volume 2026, pp. 23553–23591, 2026. Changxin Lao, Fei Pan, Guozhuang Ma, Han Li, Huihuang Lin, Jijun Shi, Kangzhi Zhao, Kun Gai, Mo Zhou, Qinqin Zhou, et al. Agentx: Towards agent-driven self-iteration of industrial recommender systems. arXiv preprint arXiv:2606.26859, 2026. Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, and Chelsea Finn. Metaharness: End-to-end optimization of model harnesses. arXiv preprint arXiv:2603.28052, 2026. Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Xuanjing Huang, Hang Yan, Zhe","arxiv_match":true},{"id":"1Kxk30PvqF","title":"From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery","forum_url":"https://openreview.net/forum?id=1Kxk30PvqF","excerpt":"tency and token-aware test-time compute. arXiv preprint arXiv:2509.09864, 2025. Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. Kimi k1.5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501.12599, 2025. Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, and Chelsea Finn. Meta-harness: End-to-end optimization of model harnesses. arXiv preprint arXiv:2603.28052, 2026. Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, and Kan","arxiv_match":true}],"id":"2603.28052","metadata_source":"Cached arXiv API metadata","rank":337},{"title":"Stable video diffusion: Scaling latent video diffusion models to large datasets","arxiv_id":"2311.15127","year":2023,"url":"https://arxiv.org/abs/2311.15127","citing_submissions":314,"explicit_arxiv_matches":287,"examples":[{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"i Zhang, Weiyang Jin, Yaowei Li, Junhao Zhuang, Haoran Li, Jie Huang, Haoyang Huang, et al. Echo-infinity: Learning evolving memory for real-time infinite video generation. arXiv preprint arXiv:2606.04527, 2026. Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe ","arxiv_match":true},{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":"ith reinforcement learning. In ICLR, 2024. Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv, 2023a. Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion mo","arxiv_match":false},{"id":"9kxTLJwbxB","title":"TraceWorld: Trajectory-Indexed Memory for Spatially Consistent Video World Model","forum_url":"https://openreview.net/forum?id=9kxTLJwbxB","excerpt":"wen Cao, Zuozhu Liu, Haoji Hu, Xiang Bai, Pengfei Wan, et al. Recammaster: Camera-controlled generative rendering from a single video. In International Conference on Computer Vision, pp. 14834–14844. IEEE, 2025. Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. Boyuan Chen, Diego Martı́ Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzma","arxiv_match":true}],"id":"2311.15127","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":338},{"title":"GQA: Training generalized multi-query transformer models from multi-head checkpoints","arxiv_id":"2305.13245","year":2023,"url":"https://arxiv.org/abs/2305.13245","citing_submissions":313,"explicit_arxiv_matches":54,"examples":[{"id":"okGS6velwf","title":"Focus-KV: Head-Wise KV Cache Compression Focus On Reasoning Heads","forum_url":"https://openreview.net/forum?id=okGS6velwf","excerpt":" Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. Gqa: Training generalized multi-query transformer models from multi-head checkpoints, 2023. URL https://arxiv.org/abs/2305.13245. Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, and Alexey Tumanov. Rocketkv: Ac","arxiv_match":true},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":" Joshua Ainslie, James Lee-Thorp, Michiel De Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. Gqa: Training generalized multi-query transformer models from multi-head checkpoints. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 4895–4901, 2023. AlphaEvolve team. AlphaEvolve: A Gemini-powered coding agent for designing advanced al","arxiv_match":false},{"id":"hAJ2b6a8w1","title":"CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning","forum_url":"https://openreview.net/forum?id=hAJ2b6a8w1","excerpt":" Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. GQA: Training generalized multi-query transformer models from multi-head checkpoints. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023. Shun-Ichi Amari. Natural gradient works efficiently in learning. Neural Computation, 10(2):25","arxiv_match":false}],"id":"2305.13245","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":339},{"title":"Hipporag: Neurobiologically inspired long-term memory for large language models","arxiv_id":"2405.14831","year":2024,"url":"https://arxiv.org/abs/2405.14831","citing_submissions":312,"explicit_arxiv_matches":44,"examples":[{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"abs/2607. 02770. GLM-5 Team. Glm-5: from vibe coding to agentic engineering, 2026. URL https://arxiv. org/abs/2602.15763. Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspired long-term memory for large language models. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 59532–59569. Curran Associates, Inc., 2","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"t with tool-interactive critiquing. In International Conference on Learning Representations, volume 2024, pp. 57734–57811, 2024. Bernal J Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspired long-term memory for large language models. Advances in neural information processing systems, 37:59532–59569, 2024. Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Steven Yau, Zijuan Lin, Liyan","arxiv_match":false},{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"huofei Qiao, et al. LightMem: Lightweight and efficient memory-augmented generation. arXiv preprint arXiv:2510.18866, 2025. Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. HippoRAG: Neurobiologically inspired long-term memory for large language models. In Advances in Neural Information Processing Systems (NeurIPS), 2024. Zexue He, Yu Wang, Churan Zhi, et al. MemoryArena: Benchmarking agent memory in interdependent multi-session agentic tasks. arXi","arxiv_match":false}],"id":"2405.14831","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":340},{"title":"FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space","arxiv_id":"2506.15742","year":2025,"url":"https://arxiv.org/abs/2506.15742","citing_submissions":311,"explicit_arxiv_matches":270,"examples":[{"id":"41Axobxx7a","title":"Scaling Muon for Diffusion Transformers","forum_url":"https://openreview.net/forum?id=41Axobxx7a","excerpt":", Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Improved precision and recall metric for assessing generative models. Advances in neural information processing systems, 32, 2019. Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, et al. Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space. arXiv preprint arXiv:2506.15742, 2025. Tim Tsz-Kit Lau and Weijie Su. Symmetry-compatible principle for optimizer design: Embedding","arxiv_match":true},{"id":"yrfvyxmuVH","title":"ByteMark: Multi-Bit Watermark for Bit-wise Image Generators","forum_url":"https://openreview.net/forum?id=yrfvyxmuVH","excerpt":"penreview.net/forum?id=AiBUm6iKBf. Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas Müller, Dustin Podell, Robin Rombach, Harry Saini, Axel Sauer, and Luke Smith. Flux.1 kontext: Flow matching for in-context image generation and editing in latent space, 2025. URL https://arxiv.org/abs/2506.15742. Olivier Ledoit and Michael Wolf. A well-conditioned estimator for large-dimensional covariance mat","arxiv_match":true},{"id":"ncOxUOWsiS","title":"Frequency-Steered VAEs: Restoring Fine Details in Low-Resolution Latents","forum_url":"https://openreview.net/forum?id=ncOxUOWsiS","excerpt":"mlr.press/v267/kouzelis25a. html. Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas Müller, Dustin Podell, Robin Rombach, Harry Saini, Axel Sauer, and Luke Smith. Flux.1 kontext: Flow matching for in-context image generation and editing in latent space, 2025. URL https://arxiv.org/abs/2506.15742. Bolin Lai, XuDong Wang, Saketh Rambhatla, James M. Rehg, Zsolt Kira, Rohit Girdhar, and Ishan Misr","arxiv_match":true}],"id":"2506.15742","metadata_source":"Cached arXiv API metadata","rank":341},{"title":"Mastering diverse domains through world models","arxiv_id":"2301.04104","year":2023,"url":"https://arxiv.org/abs/2301.04104","citing_submissions":311,"explicit_arxiv_matches":275,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"1803.10122, 2018b. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019. Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193, 2020. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104, 2023. Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, and Jing-Yu Yang. A simple yet effective","arxiv_match":true},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"d James Davidson. Learning latent dynamics for planning from pixels. In International conference on machine learning, pp. 2555–2565. PMLR, 2019. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. In International Conference on Learning Representations, 2020. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104, 2023. Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corrup","arxiv_match":true},{"id":"2HVofZMbnY","title":"PROSE: Learning Progress from Cross-Trajectory Outcomes for Long-Horizon Embodied Tasks","forum_url":"https://openreview.net/forum?id=2HVofZMbnY","excerpt":"dvances in Neural Information Processing Systems, 38:46375–46408, 2026. David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2(3):440, 2018. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104, 2023. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse control","arxiv_match":true}],"id":"2301.04104","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":342},{"title":"Deep Reinforcement Learning at the Edge of the Statistical Precipice","arxiv_id":"2108.13264","year":2021,"url":"https://arxiv.org/abs/2108.13264","citing_submissions":308,"explicit_arxiv_matches":35,"examples":[{"id":"YoE7UvVhnc","title":"AvatarPilot: Learning a Camera Policy for Active 4D Human Avatar Reconstruction","forum_url":"https://openreview.net/forum?id=YoE7UvVhnc","excerpt":" Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare. Deep reinforcement learning at the edge of the statistical precipice. Advances in neural information processing systems, 34:29304–29320, 2021. Rogerio Bonatti, Wenshan Wang, Cherie Ho, Aayush Ahuja, Mirko Gschwindt, Efe Camci, Erdal Kayacan, Sanjiban Choudhury, and Se","arxiv_match":false},{"id":"cKgE4AvBla","title":"Exploring What Matters: Value-Aware Exploration for Dreamer","forum_url":"https://openreview.net/forum?id=cKgE4AvBla","excerpt":" Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare. Deep reinforcement learning at the edge of the statistical precipice. Advances in neural information processing systems, 34:29304–29320, 2021. Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos. Unifying count-based exploratio","arxiv_match":false},{"id":"1dYqONTHJM","title":"SCOPE: Scaling Counterfactual, Observation-Preserving Environment Families for Terminal Agents","forum_url":"https://openreview.net/forum?id=1dYqONTHJM","excerpt":" Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, and Marc G. Bellemare. Deep reinforcement learning at the edge of the statistical precipice. In Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (eds.), Advances in Neural Information Processing Systems 34: Annual Conference on Neural Infor","arxiv_match":false}],"id":"2108.13264","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":343},{"title":"Emerging Properties in Unified Multimodal Pretraining","arxiv_id":"2505.14683","year":2025,"url":"https://arxiv.org/abs/2505.14683","citing_submissions":306,"explicit_arxiv_matches":297,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"0.14168, 2021. Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, et al. Instructblip: Towards general-purpose vision-language models with instruction tuning. Advances in neural information processing systems, 36:49250–49267, 2023. Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505.14683, 2025. Haiwen Diao, Penghao Wu, Hanming Deng, Jiahao Wang, Shihao Bai, Silei Wu, Weichen Fan, Wenji","arxiv_match":true},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"ai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, et al. Emu: Enhancing image generation models using photogenic needles in a haystack. arXiv preprint arXiv:2309.15807, 2023. Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, Guang Shi, and Haoqi Fan. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505.14683, 2025. Haiwen Diao, Penghao Wu, Hanming Deng, Jiahao Wang, Shihao Bai, Silei Wu, Weichen Fan, Wenji","arxiv_match":true},{"id":"1ds3a38Ku9","title":"Geo-REPA: Geometry-Aware Representation Alignment Enhances Native Spatial Intelligence in MLLMs","forum_url":"https://openreview.net/forum?id=1ds3a38Ku9","excerpt":"g. arXiv preprint arXiv:2412.05271, 2024c. Zhenyu Chen, Ali Gholami, Matthias Nießner, and Angel X Chang. Scan2cap: Context-aware dense captioning in rgb-d scans. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3193–3203, 2021. Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505.14683, 2025a. Jiajun Deng, Tianyu He, Li Jiang, Tianyu Wang, Feras Dayoub, and Ian Reid. 3d-llava: Toward","arxiv_match":true}],"id":"2505.14683","metadata_source":"Cached arXiv API metadata","rank":344},{"title":"Towards understanding sycophancy in language models","arxiv_id":"2310.13548","year":2023,"url":"https://arxiv.org/abs/2310.13548","citing_submissions":306,"explicit_arxiv_matches":97,"examples":[{"id":"UeO5A1vhy1","title":"Correcting Is Not Reusing: What a Failed Attempt Contributes Beyond the Verdict","forum_url":"https://openreview.net/forum?id=UeO5A1vhy1","excerpt":" preprint arXiv:2511.02309, 2025. Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez. Towards understanding sycophancy in language models. In International Conference on Learning Representations (ICLR), 2024. arXiv:2310.13548. Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. ","arxiv_match":true},{"id":"L8JbHpIlSq","title":"\"You're Right, Let Me Fix It\": How LLM Agents Damage Correct Work When Falsely Accused","forum_url":"https://openreview.net/forum?id=L8JbHpIlSq","excerpt":". Bowman, Esin Durmus, Zac Hatfield-Dodds, Scott R Johnston, Shauna M Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez. Towards understanding sycophancy in language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum ?id=tvhaxkMKAn. Kean Shi, Zihang Li, Tianyi Ma, Zengji Tu, Jialong Wu, Xinbo Xu, Qingyao Y","arxiv_match":false},{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":"e models. Nature, 623:493–498, 2023. Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, et al. Towards understanding sycophancy in language models. In ICLR, 2024. Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. Do anything now: Characterizing and evaluating in-the-wild jailbreak prompts on large language models. arXiv prepri","arxiv_match":false}],"id":"2310.13548","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":345},{"title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","arxiv_id":"1806.07572","year":2018,"url":"https://arxiv.org/abs/1806.07572","citing_submissions":305,"explicit_arxiv_matches":33,"examples":[{"id":"YedwDyhTVC","title":"Why Ghost Outputs Teach: A Kernel-Based Understanding of Subliminal Learning","forum_url":"https://openreview.net/forum?id=YedwDyhTVC","excerpt":"local elasticity of neural networks. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id= HJxMYANtPH. Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural tangent kernel: Convergence and generalization in neural networks. Advances in Neural Information Processing Systems, 31, 2018. Chayanon Kitkana and Shivam Arora. Sustained gradient alignment mediates subliminal learning in a multi-step setting: Evidence from mnist","arxiv_match":false},{"id":"HsVELVeqrx","title":"Relative Weight Scales Control Feature Specialization in ReLU Networks","forum_url":"https://openreview.net/forum?id=HsVELVeqrx","excerpt":"g sparse, trainable neural networks. In International Conference on Learning Representations, 2019. URL https:// openreview.net/forum?id=rJl-b3RcF7. Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural tangent kernel: Convergence and generalization in neural networks. In Advances in Neural Information Processing Systems, volume 31, 2018. Daniel Kunin, Allan Raventós, Clémentine Dominé, Feng Chen, David Klindt, Andrew Saxe, and Surya Ganguli. Get rich quick: Exact","arxiv_match":false},{"id":"ToTuhzZPcv","title":"How Does Loss Landscape Geometry Transfer Across Scales?","forum_url":"https://openreview.net/forum?id=ToTuhzZPcv","excerpt":" need permutations? Impact of model width on linear mode connectivity. In The Fourteenth International Conference on Learning Representations, 2026. Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural Tangent Kernel: Convergence and generalization in neural networks. In Advances in Neural Information Processing Systems, volume 31, 2018. Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng. Dataless knowledge fusion by merging weights of language mod","arxiv_match":false}],"id":"1806.07572","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":346},{"title":"In-context learning and induction heads","arxiv_id":"2209.11895","year":2022,"url":"https://arxiv.org/abs/2209.11895","citing_submissions":304,"explicit_arxiv_matches":163,"examples":[{"id":"WmdhapRKFO","title":"Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits","forum_url":"https://openreview.net/forum?id=WmdhapRKFO","excerpt":"guli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. In-context learning and induction heads. Transformer Circuits Thread, 2022. URL https://transformer-circuits.pub/2022/ in-context-learning-and-induction-heads/index.html. Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, B","arxiv_match":false},{"id":"x6oIXJYZ7g","title":"LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs","forum_url":"https://openreview.net/forum?id=x6oIXJYZ7g","excerpt":"rk. Self-refine: Iterative refinement with self-feedback. In Advances in Neural Information Processing Systems, 2023. Paul Michel, Omer Levy, and Graham Neubig. Are sixteen heads really better than one? In Advances in Neural Information Processing Systems, 2019. Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. In-context learning and induction heads. arXiv preprint arXiv:2209.11895, 2022. Matt Post and David Vilar. Fast lexically constrained decoding with dynamic beam allocation ","arxiv_match":true},{"id":"w3eqY0WKjo","title":"Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head","forum_url":"https://openreview.net/forum?id=w3eqY0WKjo","excerpt":"guli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, John Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom B. Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. In-context learning and induction heads. ArXiv, abs/2209.11895, 2022. Guilherme Penedo, Hynek Kydlı́cek, Loubna Ben Allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro von Werra, and Thomas Wolf. The fineweb datasets: Decanting ","arxiv_match":false}],"id":"2209.11895","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":347},{"title":"KIVI: A tuning-free asymmetric 2bit quantization for KV cache","arxiv_id":"2402.02750","year":2024,"url":"https://arxiv.org/abs/2402.02750","citing_submissions":304,"explicit_arxiv_matches":101,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"2–76366, 2025. Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, and Deming Chen. Snapkv: Llm knows what you are looking for before generation. Advances in Neural Information Processing Systems, 37:22947–22970, 2024. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. Kivi: A tuning-free asymmetric 2bit quantization for kv cache. arXiv preprint arXiv:2402.02750, 2024. Asit Mishra, Jorge Albericio Latorre, Jeff Pool, Darko Stosic, Dusan Stosic, Ganesh Venkates","arxiv_match":true},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"n Processing Systems, 36:52342–52364, 2023. URL https://openreview.net/forum?id=JZfg6wGi6g. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. KIVI: A tuning-free asymmetric 2bit quantization for KV cache. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Proceedings of the 41st International Conference on Machine Learn","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"on of the KV cache tells which tokens are important. arXiv preprint arXiv:2504.04704, 2025. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. KIVI: A tuning-free asymmetric 2bit quantization for KV cache. In International Conference on Machine Learning, pp. 32332–32344. PMLR, 2024. Stephanie Lin, Jacob Hilton, and Owain Evans. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of","arxiv_match":false}],"id":"2402.02750","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":348},{"title":"Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach","arxiv_id":"2502.05171","year":2025,"url":"https://arxiv.org/abs/2502.05171","citing_submissions":304,"explicit_arxiv_matches":107,"examples":[{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":"Implicit Thinking (LIT), 2026. URL https://openreview.net/forum?id=kKps9W1K7n. Quoted from the published abstract; full text not retrieved. Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. Scaling up test-time compute with latent reasoning: A recurrent depth approach. In Advances in Neural Information Processing Systems (NeurIPS), 2025. URL https://arxiv.org/abs/2502.05171. Alex Graves. Adaptive computation time for recurrent neural networks. arXiv preprint arXiv:1603.08","arxiv_match":true},{"id":"4A9bWVkxOC","title":"Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation","forum_url":"https://openreview.net/forum?id=4A9bWVkxOC","excerpt":"in neural information processing systems, 30, 2017. Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. Scaling up test-time compute with latent reasoning: A recurrent depth approach. Advances in Neural Information Processing Systems, 38: 41340–41391, 2026. Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can lan","arxiv_match":false},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"he loopies! arXiv preprint arXiv:2607.16051, 2026b. Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. Scaling up test-time compute with latent reasoning: A recurrent depth approach. Advances in Neural Information Processing Systems, 38: 41340–41391, 2026. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason E Weston, and Yuandong Tian. Training large language m","arxiv_match":false}],"id":"2502.05171","metadata_source":"Cached arXiv API metadata","rank":349},{"title":"The power of scale for parameter-efficient prompt tuning","arxiv_id":"2104.08691","year":2021,"url":"https://arxiv.org/abs/2104.08691","citing_submissions":303,"explicit_arxiv_matches":39,"examples":[{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":"ed inference of large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 13358–13376, 2023. Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3045–3059, 2021. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Nama","arxiv_match":false},{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"s, volume 32. Curran Associates, Inc., 2019. URL https://proceedings.neurips.cc/paper_files/paper/2019/ file/9d8df73a3cfbf3c5b47bc9b50f214aff-Paper.pdf. Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wentau Yih (eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3045–3059, Online and ","arxiv_match":false},{"id":"hItPV7ccOB","title":"Time-series Instruction Following: An Endogenous Control Language","forum_url":"https://openreview.net/forum?id=hItPV7ccOB","excerpt":"Cheng Chang, Yiming Yang, and Hanxiao Liu. Modeling long- and short-term temporal patterns with deep neural networks. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 95–104, 2018. doi: 10.1145/3209978.3210006. URL https://doi.org/10.1145/3209978.3210006. Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning, 2021. URL https://arxiv.org/abs/2104.08691. EMNLP 2021. Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for genera","arxiv_match":true}],"id":"2104.08691","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":350},{"title":"Model soups: Averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","arxiv_id":"2203.05482","year":2022,"url":"https://arxiv.org/abs/2203.05482","citing_submissions":301,"explicit_arxiv_matches":30,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"r 16, 2026. Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 23965–23998. PMLR, 2022. URL https://proceedings.mlr.press/v162/w","arxiv_match":false},{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"2302.13971. Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (eds.), Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings","arxiv_match":false},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":", pp. 61571–61595, 2026. Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International conference on machine learning, pp. 23965–23998. Pmlr, 2022. Zhengqi Xu, Han Zheng, Jie Song, Li Sun, and Mingli Song. Training-free heterogeneous model merging. arXiv preprint arXi","arxiv_match":false}],"id":"2203.05482","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":351},{"title":"Quarot: Outlier-free 4-bit inference in rotated llms","arxiv_id":"2404.00456","year":2024,"url":"https://arxiv.org/abs/2404.00456","citing_submissions":297,"explicit_arxiv_matches":52,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"flows with stochastic interpolants. In ICLR, 2023. Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. QuaRot: Outlier-free 4-bit inference in rotated LLMs. In NeurIPS, 2024. Black Forest Labs. Flux.2: Frontier visual intelligence. https://bfl.ai/blog/flux-2, 2025. Ollin Boer Bohan. Megalith-10M. madebyollin/megalith-10m, 2024. https://huggingface.co/da","arxiv_match":false},{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":" Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free 4-bit inference in rotated llms. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024, 2024. Jacob Aus","arxiv_match":false},{"id":"PCAODQ61Qw","title":"BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation","forum_url":"https://openreview.net/forum?id=PCAODQ61Qw","excerpt":"ompression. arXiv preprint arXiv:2602.02848, 2026. Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free 4-bit inference in rotated llms. In Advances in Neural Information Processing Systems, 2024. Haolei Bai, Siyong Jian, Tuo Liang, Yu Yin, and Huan Wang. Erc-svd: Error-controlled svd for large language model compression. arXiv prepr","arxiv_match":false}],"id":"2404.00456","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":352},{"title":"Offline reinforcement learning with implicit qlearning","arxiv_id":"2110.06169","year":2021,"url":"https://arxiv.org/abs/2110.06169","citing_submissions":296,"explicit_arxiv_matches":91,"examples":[{"id":"TIpxpT8trX","title":"Auto-Bidding with Disentangled Advertiser Profiles and Train-Free Adaptation","forum_url":"https://openreview.net/forum?id=TIpxpT8trX","excerpt":". Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In ICML, pp. 9902–9915, 2022. Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit q-learning. In ICLR, pp. 1–13, 2022. Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. In NeurIPS, pp. 1179–1191, 2020. Yu Lei, Jiayang Zha","arxiv_match":false},{"id":"RIpEtbedmF","title":"LeWM++: Repairing the Planning Interface of Latent World Models","forum_url":"https://openreview.net/forum?id=RIpEtbedmF","excerpt":"ao Su, and Xiaolong Wang. TD-MPC2: Scalable, robust world models for continuous control. In International Conference on Learning Representations, 2024. Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit Qlearning. In International Conference on Learning Representations, 2022. Qiyang Li, Zhiyuan Paul Zhou, and Sergey Levine. Reinforcement learning with action chunking. Advances in Neural Information Processing","arxiv_match":false},{"id":"U6seufWPDg","title":"AMINO: Generative Auto-bidding with Multimodal Trajectory Modeling and Initial Noise Optimization","forum_url":"https://openreview.net/forum?id=U6seufWPDg","excerpt":"ain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, volume 33, pp. 6840–6851, 2020. Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit qlearning. In International Conference on Learning Representations, 2022. Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. In Advances in","arxiv_match":false}],"id":"2110.06169","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":353},{"title":"Mind2Web: Towards a generalist agent for the web","arxiv_id":"2306.06070","year":2023,"url":"https://arxiv.org/abs/2306.06070","citing_submissions":295,"explicit_arxiv_matches":50,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"4.19413, 2025. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36:28091–28114, 2023. Gemma Team. Gemma 4 technical report, 2026. URL https://arxiv.org/abs/2607. 02770. Hang Gao and Yongfeng Zhang. Memory sharin","arxiv_match":false},{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":" injection attacks and defenses for LLM agents. In Advances in Neural Information Processing Systems, 2024. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36:28091–28114, 2023. Google DeepMind. Gemini 3.8 Flash Model Card. https://deepmind.google/models/ model-cards/gemini-3-8-flash/, 2026. Kilem Li G","arxiv_match":false},{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"g. Mga: Memory-driven gui agent for observation-centric interaction. arXiv preprint arXiv:2510.24168, 2025. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36:28091–28114, 2023. Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. Memp:","arxiv_match":false}],"id":"2306.06070","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":354},{"title":"Muon is Scalable for LLM Training","arxiv_id":"2502.16982","year":2025,"url":"https://arxiv.org/abs/2502.16982","citing_submissions":294,"explicit_arxiv_matches":270,"examples":[{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"i, Y. Du, Y. Qin, W. Xu, E. Lu, J. Yan, Y. Chen, H. Zheng, Y. Liu, S. Liu, B. Yin, W. He, H. Zhu, Y. Wang, J. Wang, M. Dong, Z. Zhang, Y. Kang, H. Zhang, X. Xu, Y. Zhang, Y. Wu, X. Zhou, and Z. Yang. Muon is scalable for llm training, 2025. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. Roberta: A robustly optimized bert pretraining approach, 2019. URL https: //arxiv.org/ab","arxiv_match":false},{"id":"41Axobxx7a","title":"Scaling Muon for Diffusion Transformers","forum_url":"https://openreview.net/forum?id=41Axobxx7a","excerpt":"and Tuo Zhao. NorMuon: Making muon more efficient and scalable. arXiv preprint arXiv:2510.05491, 2025. Zhengyang Liang, Hao He, Ceyuan Yang, and Bo Dai. Scaling laws for diffusion transformers. In International Conference on Learning Representations, volume 2026, pp. 84488–84521, 2026. Jingyuan Liu, Jianlin Su, Xingcheng Yao, Zhejun Jiang, Guokun Lai, Yulun Du, Yidao Qin, Weixin Xu, Enzhe Lu, Junjie Yan, et al. Muon is scalable for llm training. arXiv preprint arXiv:2502.16982, 2025. Ilya Loshchilov and Frank Hutter. arXiv:1711.05101, 2017. Decoupled weight decay regularizat","arxiv_match":true},{"id":"LxwIDZer16","title":"When Cleaner Caches Break: Training Recipes Shape MLA Cache Quantizability","forum_url":"https://openreview.net/forum?id=LxwIDZer16","excerpt":"f post-training quantization by block reconstruction. In ICLR, 2021. Jingyuan Liu, Jianlin Su, Xingcheng Yao, Zhejun Jiang, Guokun Lai, Yulun Du, Yidao Qin, Weixin Xu, Enzhe Lu, Junjie Yan, Yanru Chen, Huabin Zheng, Yibo Liu, Shaowei Liu, Bohong Yin, Weiran He, Han Zhu, Yuzhi Wang, Jianzhou Wang, Mengnan Dong, Zheng Zhang, Yongsheng Kang, Hao Zhang, Xinran Xu, Yutao Zhang, Yuxin Wu, Xinyu Zhou, and Zhilin Yang. Muon is scalable for LLM training. arXiv preprint arXiv:2502.16982, 2025. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Ch","arxiv_match":true}],"id":"2502.16982","metadata_source":"Cached arXiv API metadata","rank":355},{"title":"Accelerating large language model decoding with speculative sampling","arxiv_id":"2302.01318","year":2023,"url":"https://arxiv.org/abs/2302.01318","citing_submissions":293,"explicit_arxiv_matches":273,"examples":[{"id":"e9RG7RBDSX","title":"Cadence: Regulating the Speculation Externality in LLM Serving","forum_url":"https://openreview.net/forum?id=e9RG7RBDSX","excerpt":"n D. Lee, Deming Chen, and Tri Dao. Medusa: Simple LLM inference acceleration framework with multiple decoding heads. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 5209–5235. PMLR, 2024. Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318, 2023. Siyuan Chen, Zhipeng Jia, Samira Khan, Arvind Krishnamurthy, and Phillip B. Gibbons. SLOs-Se","arxiv_match":true},{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"nov. Small language models are the future of agentic ai, 2025. URL https://arxiv.org/abs/2506.02153. Qinnan Cai, Yibo Zhao, and Xiang Li. Think big, search small: Where capacity matters in hierarchical search agents? arXiv preprint arXiv:2607.07548, 2026. Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling, 2023. URL https://arxiv.org/abs/2302.01318. Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, and Xuemin Lin. Beyond static models: An evolvi","arxiv_match":true},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"ion for llm reasoning. arXiv preprint arXiv:2608.05643, 2026. Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D Lee, Deming Chen, and Tri Dao. Medusa: Simple llm inference acceleration framework with multiple decoding heads. arXiv preprint arXiv:2401.10774, 2024. Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318, 2023a. Lingjiao Chen, Matei Zaharia, and James Zou. Frugalgpt: How to use large language models wh","arxiv_match":true}],"id":"2302.01318","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":356},{"title":"Longvideobench: A benchmark for long-context interleaved video-language understanding","arxiv_id":"2407.15754","year":2024,"url":"https://arxiv.org/abs/2407.15754","citing_submissions":293,"explicit_arxiv_matches":46,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"on, Feng Cheng, Gedas Bertasius, and Mohit Bansal. Videotree: Adaptive tree-based video representation for llm reasoning on long videos. In CVPR, 2025. Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-context interleaved video-language understanding. In NeurIPS, 2024. Yuan Xie, Tianshui Chen, Zheng Ge, and Lionel Ni. Video-mtr: Reinforced multi-turn reasoning for long video understanding. arXiv:2508.20478, 2025. An Yang, Baosong Yang, Binyuan Hu","arxiv_match":false},{"id":"w8aZY88Jbh","title":"ReVA: A New Scene-Centric Dataset for Remote Sensing Video Question Answering","forum_url":"https://openreview.net/forum?id=w8aZY88Jbh","excerpt":"enfang Chen, Joshua B Tenenbaum, and Chuang Gan. Star: A benchmark for situated reasoning in real-world videos. arXiv preprint arXiv:2405.09711, 2024a. Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-context interleaved video-language understanding. Advances in Neural Information Processing Systems, 37:28828–28857, 2024b. Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of questionanswering to explaining temporal act","arxiv_match":false},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"an. STAR: A benchmark for situated reasoning in real-world videos. In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021. Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-context interleaved video-language understanding. Advances in Neural Information Processing Systems, 37:28828–28857, 2024. Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of questionanswering to explaining temporal acti","arxiv_match":false}],"id":"2407.15754","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":357},{"title":"Accurate Predictions on Small Data with a Tabular Foundation Model","arxiv_id":null,"year":2025,"url":"https://www.nature.com/articles/s41586-024-08328-6","citing_submissions":291,"explicit_arxiv_matches":0,"examples":[{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":"International Conference on Learning Representations, 2023. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. Accurate Predictions on Small Data with a Tabular Foundation Model. Nature, 01 2025. doi: 10.1038/s41586-024-08328-6. Manuel Hülskamp, Julius Kobialka, Emanuel Sommer, and David Rügamer. On the Uncertainty in Prior-Data Fitted Network Pretraining. In 2nd ICML Work","arxiv_match":false},{"id":"mZ3FfjH6Gm","title":"Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=mZ3FfjH6Gm","excerpt":"International Conference on Learning Representations, 2023. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. Accurate predictions on small data with a tabular foundation model. Nature, 637(8045):319–326, 2025. Rasa Hosseinzadeh, Alex Labach, Zexin Xue, Shuyi Han, Valentin Thomas, and Anthony L. Caterini. TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction. a","arxiv_match":false},{"id":"wSHvuy1308","title":"Training Fair Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=wSHvuy1308","excerpt":"n International Conference on Learning Representations, 2023. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. Accurate predictions on small data with a tabular foundation model. Nature, 637(8045):319–326, 2025. Faisal Kamiran and Toon Calders. Classifying without discriminating. In 2009 2nd International Conference on Computer, Control and Communication, pp. 1–6, 2009. doi:","arxiv_match":false}],"id":"9244a9aa8f929311","metadata_source":"Curated bibliography catalog / publisher metadata","rank":358},{"title":"Learning to summarize from human feedback","arxiv_id":"2009.01325","year":2020,"url":"https://arxiv.org/abs/2009.01325","citing_submissions":291,"explicit_arxiv_matches":44,"examples":[{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"tic differential equations. arXiv preprint arXiv:2011.13456, 2020. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. Advances in neural information processing systems, 33:3008–3021, 2020. LL Thurstone. A law of comparative judgment. Psychological Review, 34(4):273–286, 1927. Bram Wallace, Meihua Dang, Rafael Rafai","arxiv_match":false},{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":"ings of Machine Learning Research, pp. 3245– 3263. PMLR, 2024. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano. Learning to summarize with human feedback. In Advances in Neural Information Processing Systems, volume 33, pp. 3008–3021, 2020. Guangzhi Sun, Anmol Kagrecha, Potsawee Manakul, Phil Woodland, and Mark Gales. SkillAggregation: Reference-free ","arxiv_match":false},{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":"n open language models. arXiv preprint arXiv:2402.03300, 2024. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano. Learning to summarize from human feedback. In Advances in Neural Information Processing Systems, 2020. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 2018. Xuezhi Wang, Jason Wei, Dale Schuurmans, ","arxiv_match":false}],"id":"2009.01325","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":359},{"title":"Qwen2. 5-coder technical report","arxiv_id":"2409.12186","year":2024,"url":"https://arxiv.org/abs/2409.12186","citing_submissions":291,"explicit_arxiv_matches":277,"examples":[{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"rXiv preprint arXiv:2407.21783, 2024. Pengfei He, Zhenwei Dai, Bing He, Hui Liu, Xianfeng Tang, Hanqing Lu, Juanhui Li, Jiayuan Ding, Subhabrata Mukherjee, Suhang Wang, et al. Traject-bench: A trajectory-aware benchmark for evaluating agentic tool use. arXiv preprint arXiv:2510.04550, 2025. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, et al. Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186, 2024. Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma, Xu Wang, Qin Wang, and Guangsheng Yu. Sok: Age","arxiv_match":true},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"gavatula, and Yejin Choi. Cosmos qa: Machine reading comprehension with contextual commonsense reasoning, 2019. URL https://arxiv.org/ abs/1909.00277. Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. The platonic representation hypothesis. arXiv preprint arXiv:2405.07987, 2024. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, et al. Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186, 2024. Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, ","arxiv_match":true},{"id":"fkF1iei3Gs","title":"ACNA: Decentralized Active-Cell Routing for Sparse and Dynamically Expandable Neural Networks","forum_url":"https://openreview.net/forum?id=fkF1iei3Gs","excerpt":"189-prompt calibration set, a newly created untouched eight-domain blind set, synthetic adaptation datasets, checkpoint migration logic, random seeds, optimizer settings, and raw CPU/GPU/RTL logs. The accompanying internal package contains a completeness manifest and templates, but missing experimental source files are not fabricated; they must be copied from the experiment machine before submission. B. Hui et al. Qwen2.5-Coder Technical Report. arXiv:2409.12186, 2024. A Additional Experimental Details A.1 Eight-Cell Expansion Sequence The evaluated sequence i","arxiv_match":true}],"id":"2409.12186","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":360},{"title":"The ai scientist: Towards fully automated open-ended scientific discovery","arxiv_id":"2408.06292","year":2024,"url":"https://arxiv.org/abs/2408.06292","citing_submissions":290,"explicit_arxiv_matches":265,"examples":[{"id":"P9bJmoIx5X","title":"Controlling False Discoveries via Auditing Scientific Agents","forum_url":"https://openreview.net/forum?id=P9bJmoIx5X","excerpt":"al inspection of galaxies from the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society, 389(3):1179–1189, September 2008. ISSN 00358711, 13652966. doi: 10.1111/j.1365-2966.2008.13689.x. Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The AI Scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292, 2024. doi: 10.48550/arXiv.2408.06292. URL https://arxiv.org/abs/2408.06292. Chris Lu, Cong Lu, Robert Tjarko Lange, Yutaro Yamada, Shengran Hu, Jakob Foerster, David Ha, and ","arxiv_match":true},{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"o Wang, and Shengli Zhang. An LLM-agentbased framework for age of information optimization in heterogeneous random access networks. arXiv preprint arXiv:2601.18563, 2026. doi: 10.48550/arXiv.2601.18563. URL https:// arxiv.org/abs/2601.18563. Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The AI scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292, 2024. URL https://arxiv.org/abs/2408.06292. Hang Lu, Guochang Li, Qianyu Chen, Huiyan Gao, Shaogang Wang, Xuanyu He, Yiwei Liu, Gaopeng Chen, ","arxiv_match":true},{"id":"Xq1WyguERb","title":"Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery","forum_url":"https://openreview.net/forum?id=Xq1WyguERb","excerpt":"rXiv:2606.01139, 2026b. Sean Lovett, Miha Zgubic, Sofia Liguori, Sephora Madjiheurem, Hamish Tomlinson, Sophie Elster, Chris Apps, Sims Witherspoon, and Luis Piloto. Opfdata: Large-scale datasets for ac optimal power flow with topological perturbations. arXiv preprint arXiv:2406.07234, 2024. Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292, 2024. Enrico Marchesini, Benjamin Donnot, Constance Crozier, Ian Dytham, Christian Merz, Lars Sche","arxiv_match":true}],"id":"2408.06292","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":361},{"title":"SiT: Exploring flow and diffusion-based generative models with scalable interpolant transformers","arxiv_id":"2401.08740","year":2024,"url":"https://arxiv.org/abs/2401.08740","citing_submissions":289,"explicit_arxiv_matches":41,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"ll-frequency modeling for high-fidelity pixel diffusion. arXiv preprint arXiv:2605.17759, 2026a. Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In ECCV, 2024. Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li. Hpsv3: Towards wide-spectrum human preference score. In ICCV, 2025. Zehong Ma, Longhui Wei, Shuai Wang, Shiliang Zhang, and Qi Ti","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"th pixel diffusion. arXiv preprint arXiv:2605.23902, 2026. URL https://arxiv.org/abs/2605.23902. Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In ECCV, 2024. Zehong Ma, Longhui Wei, Shuai Wang, Shiliang Zhang, and Qi Tian. DeCo: Frequency-decoupled pixel diffusion for end-to-end image generation. In CVPR, 2026a. Yuhang Ma, Xiaoshi Wu, Keqi","arxiv_match":false},{"id":"o6IsUtKlgQ","title":"Learning Spatial-Pyramid Visual Representations for Autoregressive Image Generation","forum_url":"https://openreview.net/forum?id=o6IsUtKlgQ","excerpt":"angyu Yue. Amortized moment matching for visual generation. arXiv preprint arXiv:2607.26860, 2026. Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In European Conference on Computer Vision, pp. 23–40. Springer, 2024. Keita Miwa, Kento Sasaki, Hidehisa Arai, Tsubasa Takahashi, and Yu Yamaguchi. One-d-piece: Image tokenizer meets quality-control","arxiv_match":false}],"id":"2401.08740","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":362},{"title":"Kimi K2: Open Agentic Intelligence","arxiv_id":"2507.20534","year":2025,"url":"https://arxiv.org/abs/2507.20534","citing_submissions":288,"explicit_arxiv_matches":257,"examples":[{"id":"YoY3Gk7Syu","title":"Region-Level Policy Optimization for Fine-grained MLLM Perception","forum_url":"https://openreview.net/forum?id=YoY3Gk7Syu","excerpt":"ach. Towards vqa models that can read. In CVPR, 2019. Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, Ian Ballantyne, Cormac Brick, Victor Cărbune, Michelle Casbon, et al. Gemma 4 technical report. arXiv preprint arXiv:2607.02770, 2026. Kimi Team, Yifan Bai, Yiping Bao, Y Charles, Cheng Chen, Guanduo Chen, Haiting Chen, Huarong Chen, Jiahao Chen, Ningxin Chen, et al. Kimi k2: Open agentic intelligence. arXiv preprint arXiv:2507.20534, 2025. Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shu","arxiv_match":true},{"id":"CFVCzCNC1l","title":"Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=CFVCzCNC1l","excerpt":"nal Linguistics (Volume 1: Long Papers), pp. 5555–5579, Vienna, Austria, July 2025. Association for Computational Linguistics. ISBN 979-8-89176-251-0. doi: 10.18653/v1/2025.acl-long.277. Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, and Le Sun. ToolAlpaca: Generalized tool learning for language models with 3000 simulated cases. arXiv preprint arXiv:2306.05301, 2023. Kimi Team et al. Kimi K2: Open agentic intelligence. arXiv preprint arXiv:2507.20534, 2025. Xiaoyu Tian, Haotian Wang, Shuaiting Chen, Hao Zhou, Kaichi Yu, Yudian Zhang, Jade Ouyang, J","arxiv_match":true},{"id":"BOyTp7MiOV","title":"CAFE: Self-Improving Search Agents Need Co-Evolving Feedback","forum_url":"https://openreview.net/forum?id=BOyTp7MiOV","excerpt":"ang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning, 2025. URL https://arxiv.org/abs/2503.05592. Richard S Sutton, Andrew G Barto, and Andrew Barto. Reinforcement learning: An introduction, volume 1. MIT press Cambridge, 1998. Kimi Team, Yifan Bai, Yiping Bao, Y Charles, Cheng Chen, Guanduo Chen, Haiting Chen, Huarong Chen, Jiahao Chen, Ningxin Chen, et al. Kimi k2: Open agentic intelligence. arXiv preprint arXiv:2507.20534, 2025. Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, SH Cai, Yuan Cao, Y Charles, HS Che, Cheng C","arxiv_match":true}],"id":"2507.20534","metadata_source":"Cached arXiv API metadata","rank":363},{"title":"Stochastic Interpolants: A Unifying Framework for Flows and Diffusions","arxiv_id":"2303.08797","year":2023,"url":"https://arxiv.org/abs/2303.08797","citing_submissions":288,"explicit_arxiv_matches":84,"examples":[{"id":"z7tlMaVseU","title":"Prototype-Routed Flow Matching for Incomplete Multi-View Clustering","forum_url":"https://openreview.net/forum?id=z7tlMaVseU","excerpt":" Michael S. Albergo, Nicholas M. Boffi, and Eric Vanden-Eijnden. Stochastic interpolants: A unifying framework for flows and diffusions. Journal of Machine Learning Research, 26(209):1–80, 2025. David Arthur and Sergei Vassilvitskii. k-means++: The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Sympos","arxiv_match":false},{"id":"FumWWB5wLP","title":"Beyond the Estimate: Wiener-Informed Decoding for Easier Training of Pixel-Space Flow Models","forum_url":"https://openreview.net/forum?id=FumWWB5wLP","excerpt":"lip3-o: A family of fully open unified multimodal models-architecture, training and dataset, 2025a. URL https: //arxiv.org/abs/2505.09568. Michael Albergo, Nicholas M. Boffi, and Eric Vanden-Eijnden. Stochastic interpolants: A unifying framework for flows and diffusions. Journal of Machine Learning Research, 26(209):1–80, 2025. URL http://jmlr.org/papers/v26/23-1605.html. Hansheng Chen, Jan Ackermann, Minseo Kim, Gordon Wetzstein, and Leonidas Guibas. Asymmetric flo","arxiv_match":false},{"id":"WsLUWEDwZr","title":"Amortized Moment Matching for Visual Generation","forum_url":"https://openreview.net/forum?id=WsLUWEDwZr","excerpt":" Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden. Stochastic interpolants: A unifying framework for flows and diffusions. arXiv preprint arXiv:2303.08797, 2023. Black Forest Labs. FLUX.2 [klein]: Towards Interactive Visual Intelligence. https://bfl. ai/","arxiv_match":true}],"id":"2303.08797","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":364},{"title":"Why Do Multi-Agent LLM Systems Fail?","arxiv_id":"2503.13657","year":2025,"url":"https://arxiv.org/abs/2503.13657","citing_submissions":288,"explicit_arxiv_matches":96,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":" Mert Cemri, Melissa Z Pan, Shuyi Yang, Lakshya A Agrawal, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya Parameswaran, Dan Klein, Kannan Ramchandran, et al. Why do multi-agent llm systems fail? arXiv preprint arXiv:2503.13657, 2025. Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhi","arxiv_match":true},{"id":"aH5jpHsG9k","title":"Right Answers, Wrong States: Hidden Information Failures in Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=aH5jpHsG9k","excerpt":"ics: EMNLP 2024, pp. 6929–6948, 2024. Mert Cemri, Melissa Z Pan, Shuyi Yang, Lakshya A Agrawal, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya Parameswaran, Dan Klein, Kannan Ramchandran, et al. Why do multi-agent llm systems fail? Advances in Neural Information Processing Systems, 38, 2026. Nuo Chen, Yicheng Tong, Jiaying Wu, Minh Duc Duong, Qian Wang, Qingyun Zou, Bryan Hooi, and Bingsheng He. Beyond brainstorming: What driv","arxiv_match":false},{"id":"6UAUh3ggvZ","title":"Consistent Plan-Act for Long-Horizon Agentic Tasks","forum_url":"https://openreview.net/forum?id=6UAUh3ggvZ","excerpt":" Melissa Z. Pan, Shuyi Yang, Lakshya A. Agrawal, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya G. Parameswaran, Dan Klein, Kannan Ramchandran, Matei Zaharia, Joseph E. Gonzalez, and Ion Stoica. Why do multi-agent LLM systems fail? In NeurIPS, 2025. Datasets and Benchmarks Track. Hyungjoo Chae, Sunghwan Kim, Junhee Cho, Seungone Kim, Seungjun Moon, Gyeom Hwangbo, Dongha Lim, Minjin Kim, Yeonjun Hwang, Minju Gwak, Dongwook Choi","arxiv_match":false}],"id":"2503.13657","metadata_source":"Cached arXiv API metadata","rank":365},{"title":"2 OLMo 2 Furious","arxiv_id":"2501.00656","year":2024,"url":"https://arxiv.org/abs/2501.00656","citing_submissions":286,"explicit_arxiv_matches":255,"examples":[{"id":"RqXP83lQJI","title":"Local Splits, Global Shifts: An Interaction Decomposition of Retokenization in Language Models","forum_url":"https://openreview.net/forum?id=RqXP83lQJI","excerpt":"t B Owen. Estimating mean dimensionality of analysis of variance decompositions. Journal of the American Statistical Association, 101(474):712–721, 2006. Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843, 2016. Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Shane Arora, Akshita Bhagia, Yuling Gu, Shengyi Huang, Matt Jordan, et al. 2 olmo 2 furious. arXiv preprint arXiv:2501.00656, 2024. Art B Owen. Monte carlo theory, methods and examples, 2013. Buu Phan, Brandon Amos, Itai Gat","arxiv_match":true},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":", Nouha Dziri, Allyson Ettinger, Michal Guerquin, David Heineman, Hamish Ivison, Pang Wei Koh, Jiacheng Liu, Saumya Malik, William Merrill, Lester James V. Miranda, Jacob Morrison, Tyler Murray, Crystal Nam, Jake Poznanski, Valentina Pyatkin, Aman Rangapur, Michael Schmitz, Sam Skjonsberg, David Wadden, Christopher Wilhelm, Michael Wilson, Luke Zettlemoyer, Ali Farhadi, Noah A. Smith, and Hannaneh Hajishirzi. 2 olmo 2 furious, 2025. URL https: //arxiv.org/abs/2501.00656. Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learn","arxiv_match":true},{"id":"W1xeiKanFu","title":"Do Correct Answers Have Stable Latent Knowledge Support?","forum_url":"https://openreview.net/forum?id=W1xeiKanFu","excerpt":"Mistral AI. Mistral-large-instruct-2407. Hugging Face model card, 2024. URL https://hugg ingface.co/mistralai/Mistral-Large-Instruct-2407. Mistral AI Team. Mistral-Large-Instruct-2411 model card. Hugging Face model card, 2024. URL https://huggingface.co/mistralai/Mistral-Large-Instruct-2411. Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Shane Arora, Akshita Bhagia, Yuling Gu, Shengyi Huang, Matt Jordan, et al. 2 olmo 2 furious. arXiv preprint arXiv:2501.00656, 2024. OLMo Team, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, et al. 2 OLMo 2 Furious. In ","arxiv_match":true}],"id":"2501.00656","metadata_source":"Cached arXiv API metadata","rank":366},{"title":"Solving Math Word Problems with Process- and Outcome-Based Feedback","arxiv_id":"2211.14275","year":2022,"url":"https://arxiv.org/abs/2211.14275","citing_submissions":286,"explicit_arxiv_matches":271,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"rXiv:2607.13988, 2026. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. musique: Multihop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 10:539–554, 2022. Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process-and outcome-based feedback. arXiv preprint arXiv:2211.14275, 2022. Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, ","arxiv_match":true},{"id":"BTTuPGcoJL","title":"Learning from Correctness Trends: Sample-Wise Historical Advantage Shaping for Mathematical Reasoning","forum_url":"https://openreview.net/forum?id=BTTuPGcoJL","excerpt":"rasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, and Greg Durrett. To cot or not to cot? chain-of-thought helps mainly on math and symbolic reasoning. In International Conference on Learning Representations, volume 2025, pp. 94118–94162, 2025. Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process-and outcome-based feedback. arXiv preprint arXiv:2211.14275, 2022. Chao Wang, Hongtao Tian, Tao Yang, Yunsheng Shi, Ting Yao, and Wenbo Ding. Process advantage","arxiv_match":true},{"id":"BOyTp7MiOV","title":"CAFE: Self-Improving Search Agents Need Co-Evolving Feedback","forum_url":"https://openreview.net/forum?id=BOyTp7MiOV","excerpt":"wal. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. In Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers), pp. 10014–10037, 2023. Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process-and outcome-based feedback. arXiv preprint arXiv:2211.14275, 2022. Guoqing Wang, Sunhao Dai, Guangze Ye, Zeyu Gan, Wei Yao, Yong Deng, Xiaofeng Wu, and Zhenzhe","arxiv_match":true}],"id":"2211.14275","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":367},{"title":"From local to global: A graph RAG approach to query-focused summarization","arxiv_id":"2404.16130","year":2024,"url":"https://arxiv.org/abs/2404.16130","citing_submissions":284,"explicit_arxiv_matches":258,"examples":[{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"ry, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurelie Lozano, Georgios Kollias, Vijil Chenthamarakshan, Soham Dan, et al. Larimar: Large language models with episodic memory control. In International Conference on Machine Learning (ICML), 2024. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. From local to global: A graph RAG approach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024. Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi Y","arxiv_match":true},{"id":"1V3066nDJF","title":"Contract Memory Compiler: Resolve, Then Traverse","forum_url":"https://openreview.net/forum?id=1V3066nDJF","excerpt":"ng Cao. MemCompiler: Compile, don’t inject— state-conditioned memory for embodied agents. arXiv preprint arXiv:2605.07594, 2026. URL https://arxiv.org/abs/2605.07594. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. From local to global: A graph RAG approach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024. URL https://arxiv.org/abs/2404.16130. Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. HippoRAG: Neurobiolo","arxiv_match":true},{"id":"BcOYpbPnS1","title":"What Retrieval Buys Depends on Who Reads: Splitting Multi-Hop RAG Accuracy into Assembly and Reading","forum_url":"https://openreview.net/forum?id=BcOYpbPnS1","excerpt":" and Fabrizio Silvestri. The power of noise: Redefining retrieval for rag systems. In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp. 719–729, 2024. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024. Zirui Guo, Lianghao Xia, Yanhua Yu, Tian Ao, and Chao Huang. Lightrag: Simple and fast retri","arxiv_match":true}],"id":"2404.16130","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":368},{"title":"From slow bidirectional to fast autoregressive video diffusion models","arxiv_id":"2412.07772","year":2024,"url":"https://arxiv.org/abs/2412.07772","citing_submissions":284,"explicit_arxiv_matches":41,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"s of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6613–6623, 2024b. Tianwei Yin, Qiang Zhang, Richard Zhang, William T. Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025a. Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. F","arxiv_match":false},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"EE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6613–6623. IEEE, 2024. Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22963–22974. IEEE, 2025. Shenghai Yuan, Yuanyang Yin, Zongjian Li, Xinwei Huang, Xiao Yang, and Li Yuan. Helios: Re","arxiv_match":false},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":"tioned video generation via reflective planning agents. arXiv preprint arXiv:2603.08403, 2026d. Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22963–22974. IEEE, 2025. Yifei Yu, Xiaoshan Wu, Xinting Hu, Tao Hu, Yangtian Sun, Xiaoyang Lyu, Bo Wang, Lin Ma, Yu","arxiv_match":false}],"id":"2412.07772","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":369},{"title":"Qwen2.5-Math technical report: Toward mathematical expert model via self-improvement","arxiv_id":"2409.12122","year":2024,"url":"https://arxiv.org/abs/2409.12122","citing_submissions":284,"explicit_arxiv_matches":267,"examples":[{"id":"BTTuPGcoJL","title":"Learning from Correctness Trends: Sample-Wise Historical Advantage Shaping for Mathematical Reasoning","forum_url":"https://openreview.net/forum?id=BTTuPGcoJL","excerpt":"ozheng Luo, Xander Wu, Jie Ni, Yun Fu, Nuno Vasconcelos, and Yijiang Li. Learn what’s left, not what’s mastered: Saturation aware advantage reweighting for multi-reward policy optimization. arXiv preprint arXiv:2608.16072, 2026c. An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, et al. Qwen2. 5-math technical report: Toward mathematical expert model via self-improvement. arXiv preprint arXiv:2409.12122, 2024. Fengkai Yang, Zherui Chen, Xiaohan Wang, Xiaodong Lu, Jiajun Chai, Guojun Yin, Wei Lin, Shua","arxiv_match":true},{"id":"VjefWiXiJQ","title":"MechDS: Mechanistic RLVR Data Selection via Reasoning Component Dependence","forum_url":"https://openreview.net/forum?id=VjefWiXiJQ","excerpt":"cai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, Chenchen Ling, et al. Deepseek-v4: Towards highly efficient milliontoken context intelligence. arXiv preprint arXiv:2606.19348, 2026. An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, et al. Qwen2. 5-math technical report: Toward mathematical expert model via self-improvement. arXiv preprint arXiv:2409.12122, 2024. Ningyuan Yang, Weihua Du, Weiwei Sun, Sean Welleck, and Yiming Yang. Gradalign: Gradientalig","arxiv_match":true},{"id":"F6XC1gfjyT","title":"Rethinking Supervision Granularity: Segment-Level Learning for LLM-Based Theorem Proving","forum_url":"https://openreview.net/forum?id=F6XC1gfjyT","excerpt":"n Nie, Kun Yuan, and Xia Xiao. Scaling up multi-turn off-policy rl and multi-agent tree search for llm step-provers. arXiv preprint arXiv:2509.06493, 2025b. An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, Keming Lu, Mingfeng Xue, Runji Lin, Tianyu Liu, Xingzhang Ren, and Zhenru Zhang. Qwen2.5-math technical report: Toward mathematical expert model via self-improvement. arXiv preprint arXiv:2409.12122, 2024. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao,","arxiv_match":true}],"id":"2409.12122","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":370},{"title":"Xstest: A test suite for identifying exaggerated safety behaviours in large language models","arxiv_id":"2308.01263","year":2023,"url":"https://arxiv.org/abs/2308.01263","citing_submissions":284,"explicit_arxiv_matches":38,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"muel R Bowman. Gpqa: A graduate-level google-proof q&a benchmark. arXiv preprint arXiv:2311.12022, 2023. Paul Röttger, Hannah Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. Xstest: A test suite for identifying exaggerated safety behaviours in large language models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 5377–5400, 2024. John S","arxiv_match":false},{"id":"yECLVPwR3R","title":"Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation","forum_url":"https://openreview.net/forum?id=yECLVPwR3R","excerpt":"R2022/html/Rombach_ High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_ CVPR_2022_paper.html. Paul Röttger, Hannah Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 5377– 5400. Association","arxiv_match":false},{"id":"DkgHpC6dOc","title":"The Other Side of Answering: How Language Models Respond Beyond Direct Answers","forum_url":"https://openreview.net/forum?id=DkgHpC6dOc","excerpt":"tion for Computational Linguistics. doi: 10.18653/v1/P18-2124. URL https://aclanthology. org/P18-2124/. Paul Röttger, Hannah Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. XSTest: A test suite for identifying exaggerated safety behaviours in large language models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 5377– 5400, Mexico City","arxiv_match":false}],"id":"2308.01263","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":371},{"title":"Benchmarking neural network robustness to common corruptions and perturbations","arxiv_id":"1903.12261","year":2019,"url":"https://arxiv.org/abs/1903.12261","citing_submissions":283,"explicit_arxiv_matches":69,"examples":[{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":" spatial-temporal graph data for traffic forecasting. IEEE Transactions on Knowledge and Data Engineering, 34(11):5415–5428, 2022. doi: 10.1109/TKDE.2021.3056502. Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. In Proceedings of the International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=HJz6tiCqYm. Jiahao Ji, Wentao Zhang, Jingyuan Wang, and Chao Huang. Seeing the u","arxiv_match":false},{"id":"uqsXddQsDQ","title":"How Far Can Reliability Drift Without Changing the Confidence Distribution?","forum_url":"https://openreview.net/forum?id=uqsXddQsDQ","excerpt":"la Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum. Multicalibration: calibration for the (computationally-identifiable) masses. In ICML, 2018. Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. In ICLR, 2019. Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer. ","arxiv_match":false},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"s, 2020. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104, 2023. Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. In International Conference on Learning Representations, 2019. Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, ","arxiv_match":false}],"id":"1903.12261","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":372},{"title":"Instructpix2pix: Learning to follow image editing instructions","arxiv_id":"2211.09800","year":2022,"url":"https://arxiv.org/abs/2211.09800","citing_submissions":283,"explicit_arxiv_matches":30,"examples":[{"id":"goQTywWWdx","title":"VestEdit: Inversion-Free Video Editing via Spatio-Temporal Transport Stabilization","forum_url":"https://openreview.net/forum?id=goQTywWWdx","excerpt":" Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18392–18402, 2023. Lingling Cai, Kang Zhao, Hangjie Yuan, Xiang Wang, Yingya Zhang, and Kejie Huang. Dfvedit","arxiv_match":false},{"id":"IdwA2KX2G3","title":"StateWise: From Noise Awareness to State-Calibrated MLLM Conditioning for Unified Generation","forum_url":"https://openreview.net/forum?id=IdwA2KX2G3","excerpt":"Yufei Guo, et al. Improving image generation with better captions. Technical report, OpenAI, 2023. URL https://cdn.openai.com/papers/dall-e-3.pdf. Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18392–18402, 2023. Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, ","arxiv_match":false},{"id":"SIlCRKTkkb","title":"UniAVC: Unified Audio-Video Creation via Progressive Training and Asymmetric Scheduling","forum_url":"https://openreview.net/forum?id=SIlCRKTkkb","excerpt":"r latents: High-resolution video synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. Tim Brooks, Aleksander Holynski, and Alexei A. Efros. Instructpix2pix: Learning to follow image editing instructions. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. Anthony Chen, Naomi Ken Korem, Tavi Halperin, Matan Ben Yosef, Urska Jelercic, Ofir Bibi, Or Patashnik, and Daniel Cohen-Or. ","arxiv_match":false}],"id":"2211.09800","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":373},{"title":"Toy models of superposition","arxiv_id":"2209.10652","year":2022,"url":"https://arxiv.org/abs/2209.10652","citing_submissions":283,"explicit_arxiv_matches":142,"examples":[{"id":"4lG3d2QtGR","title":"Decomposing and Steering Functional Metacognition in Large Language Models","forum_url":"https://openreview.net/forum?id=4lG3d2QtGR","excerpt":"z Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. Yanai Elazar, Shauli Ravfogel, Alon Jacovi, and Yoav Goldberg. Amnesic probing: Behavioral explanation with amnesic counterfactuals. Transactions of the Association for Computational Linguistics, 9:160–175, 2021. Nelson Elhage et al. Toy models of superposition, 2022. URL https://arxiv.org/abs/2209. 10652. James Fodor. Line goes up? inherent limitations of benchmarks for evaluating large language models","arxiv_match":true},{"id":"VcCXvjmFCY","title":"Refraction: Beyond Axis-Aligned Interpretable Subspaces in Visual Representations","forum_url":"https://openreview.net/forum?id=VcCXvjmFCY","excerpt":". 73329–73352, 2025. Rudresh Dwivedi, Devam Dave, Het Naik, Smiti Singhal, Rana Omer, Pankesh Patel, Bin Qian, Zhenyu Wen, Tejal Shah, Graham Morgan, et al. Explainable ai (xai): Core ideas, techniques, and solutions. ACM computing surveys, 55(9):1–33, 2023. Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al. Toy models of superposition. arXiv preprint arXiv:2209.10652, 2022. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual ne","arxiv_match":true},{"id":"5LIfhMGIYd","title":"From Activation to Intervention: Interpreting Sparse Visual Features through Reconstruction","forum_url":"https://openreview.net/forum?id=5LIfhMGIYd","excerpt":"erer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. URL https://openreview. net/forum?id=YicbFdNTTy. Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al. Toy models of superposition. arXiv preprint arXiv:2209.10652, 2022. Thomas Fel, Agustin Picard, Louis Bethune, Thibaut Boissin, David Vigouroux, Julien Colin, R","arxiv_match":true}],"id":"2209.10652","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":374},{"title":"Estimating or propagating gradients through stochastic neurons for conditional computation","arxiv_id":"1308.3432","year":2013,"url":"https://arxiv.org/abs/1308.3432","citing_submissions":282,"explicit_arxiv_matches":254,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"on realistic long-context multitasks. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3639–3664, 2025. Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020. Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. Dimitri P. Bertsekas. Nonlinear Programming. Athena Scientific, 2 edition, 1999. Yonatan Bis","arxiv_match":true},{"id":"vSqsA99XH4","title":"Quantization-Aware Pre-Training with Constrained Empirical Weight Distribution","forum_url":"https://openreview.net/forum?id=vSqsA99XH4","excerpt":" Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation, 2013. URL https://arxiv.org/ abs/1308.3432. Roberto L. Castro, Andrei Panferov, Soroush Tabesh, Oliver Sieberling, Jiale Chen, Mahdi Nikdan, S","arxiv_match":true},{"id":"vSjw3bWmyF","title":"Who Contributes? Counterfactual Attribution in Sparse Mixture-of-Experts","forum_url":"https://openreview.net/forum?id=vSjw3bWmyF","excerpt":"Understanding and leveraging the expert specialization of context faithfulness in mixture-of-experts LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 21927–21942. Association for Computational Linguistics, 2025. doi: 10.18653/v1/2025.emnlp-main.1114. Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. Anzhe Cheng, Shukai Duan, Shixuan Li, Chenzhong Yin, Mingxi Cheng, Heng Ping, Tamoghna Chatt","arxiv_match":true}],"id":"1308.3432","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":375},{"title":"Measuring faithfulness in chain-of-thought reasoning","arxiv_id":"2307.13702","year":2023,"url":"https://arxiv.org/abs/2307.13702","citing_submissions":281,"explicit_arxiv_matches":251,"examples":[{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"Esin Durmus, Evan Hubinger, Jackson Kernion, Kamilė Lukošiūtė, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, Saurav Kadavath, Shannon Yang, Thomas Henighan, Timothy Maxwell, Timothy Telleen-Lawton, Tristan Hume, Zac Hatfield-Dodds, Jared Kaplan, Jan Brauner, Samuel R. Bowman, and Ethan Perez. Measuring faithfulness in chain-of-thought reasoning, 2023. URL https://arxiv.org/abs/2307.13702. Yijiang Li, Bingyang Wang, Yijun Liang, Yunjie Tian, Di Fu, and Nuno Vasconcelos. On-policy selfdi","arxiv_match":true},{"id":"JwUvLVBgt7","title":"Do Vision-Language Models Use the Regions They Cite? A Step-Specific Intervention Test","forum_url":"https://openreview.net/forum?id=JwUvLVBgt7","excerpt":"6, 2019. Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. ReferItGame: Referring to objects in photographs of natural scenes. In Proceedings of EMNLP, pp. 787–798, 2014. Ranjay Krishna, Yuke Zhu, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision, 2017. arXiv:1602.07332. Tamera Lanham et al. Measuring faithfulness in chain-of-thought reasoning. arXiv:2307.13702, 2023. arXiv preprint Xuchen Li, Xuzhao Li, Renjie Pi, Shiyu Hu, Jian Zhao, and Jiahui Gao. Beyond ","arxiv_match":true},{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"dang Lad, Jin Hwa Lee, Wes Gurnee, and Max Tegmark. Remarkable robustness of LLMs: Stages of inference? In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=Wxh5Xz7NpJ. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, et al. Measuring Faithfulness in Chain-of-Thought Reasoning. arXiv preprint arXiv:2307.13702, 2023. URL https://www.anthropic.com/research/ measuring-faithfulness-in-chain-of-thought-reasoning","arxiv_match":true}],"id":"2307.13702","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":376},{"title":"Code as Policies: Language Model Programs for Embodied Control","arxiv_id":"2209.07753","year":2022,"url":"https://arxiv.org/abs/2209.07753","citing_submissions":280,"explicit_arxiv_matches":83,"examples":[{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"ed workflow generation for large language model agents. arXiv preprint arXiv:2407.12821, 2024. Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng. Code as policies: Language model programs for embodied control. In Proceedings of 2023 IEEE International conference on robotics and automation (ICRA), pp. 9493–9500. IEEE, 2023. Grace Liu, Yuxiao Qu, Jeff Schneider, Aarti Singh, and Aviral Kumar. Cart: Teaching","arxiv_match":false},{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":" Chelsea Finn. Openvla: An open-source vision-language-action model. In Pulkit Agrawal, Oliver Kroemer, and Wolfram Burgard (eds.), Proceedings of The 8th Conference on Robot Learning, volume 270 of Proceedings of Machine Learning Research, pp. 2679–2713. PMLR, 06–09 Nov 2025. Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng. Code as policies: Language model programs for embodied control. In arXiv preprint arXiv:2209.07753, 2022. Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: ","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"ntic framework for scalable long-horizon robotic tasks. arXiv preprint arXiv:2603.11558, 2026. Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng. Code as policies: Language model programs for embodied control. In ICRA, 2023. Weixin Liang, Lili Yu, Liang Luo, Srinivasan Iyer, Ning Dong, Chunting Zhou, Gargi Ghosh, Mike Lewis, Wen tau Yih, Luke Zettlemoyer, and Xi Victoria Lin. Mixture-of-transformers: A sp","arxiv_match":false}],"id":"2209.07753","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":377},{"title":"Thinking in space: How multimodal large language models see, remember, and recall spaces","arxiv_id":"2412.14171","year":2024,"url":"https://arxiv.org/abs/2412.14171","citing_submissions":280,"explicit_arxiv_matches":46,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"en Zhou, Yves Liang, Zuyan Liu, et al. Hy-Embodied-VLM-1.0: Efficient Physical-World Agents. arXiv preprint arXiv:2607.12894, 2026. URL https://arxiv.org/abs/2607. 12894. Jihan Yang, Shusheng Yang, Anjali W. Gupta, Rilyn Han, Li Fei-Fei, and Saining Xie. Thinking in space: How multimodal large language models see, remember, and recall spaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. URL https: //arxiv.org/abs/2412.14171. Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen. A survey on multim","arxiv_match":true},{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"e. In Advances in Neural Information Processing Systems, volume 38, pp. 13569–13597. Curran Associates, Inc., 2025. Jihan Yang, Shusheng Yang, Anjali W. Gupta, Rilyn Han, Fei-Fei Li, and Saining Xie. Thinking in space: How multimodal large language models see, remember, and recall spaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10632–10643, 2025a. Rui Yang, Ziyu Zhu, Yanwei Li, Jingjia Huang, Shen Yan, Siyuan Zhou, Zhe Liu, Xia","arxiv_match":false},{"id":"VemSN4uC6Q","title":"Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs","forum_url":"https://openreview.net/forum?id=VemSN4uC6Q","excerpt":"ge language models to understand point clouds. In European Conference on Computer Vision, pp. 131–147. Springer, 2024. Jihan Yang, Shusheng Yang, Anjali Gupta, Rilyn Han, Fei-Fei Li, and Saining Xie. Thinking in space: How multimodal large language models see, remember, and recall spaces. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10632–10643, 2025a. Jihan Yang, Zifan Zhao, Xichen Pan, Shusheng Yang, Junyi Zhang, Bingyi Kang, Hu Xu, Shang-Wen Li, and S","arxiv_match":false}],"id":"2412.14171","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":378},{"title":"Scaling and evaluating sparse autoencoders","arxiv_id":"2406.04093","year":2024,"url":"https://arxiv.org/abs/2406.04093","citing_submissions":279,"explicit_arxiv_matches":98,"examples":[{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"quential activation patching, 2026. URL https://arxiv.org/abs/2608. 22332. Leo Gao, Tom Dupre la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/ forum?id=tcsZt9ZNKD. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abh","arxiv_match":false},{"id":"89cECgVi4S","title":"RealControlBench: A Comprehensive Benchmark for Fine-Grained LLM and LLM Agent steer in the Wild","forum_url":"https://openreview.net/forum?id=89cECgVi4S","excerpt":"rical Methods in Natural Language Processing (EMNLP), pp. 9004–9018, 2024. Leo Gao, Tom Dupre la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders. In International Conference on Learning Representations (ICLR), volume 2025, pp. 26721–26754, 2025. Daniil Gurgurov, Yusser Al Ghussin, Tanja Baeumel, Cheng-Ting Chou, Patrick Schramowski, Marius Mo","arxiv_match":false},{"id":"GXJlfZBXPl","title":"D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders","forum_url":"https://openreview.net/forum?id=GXJlfZBXPl","excerpt":"s. In European Conference on Computer Vision, pp. 172–188. Springer, 2024. Leo Gao, Tom Dupre la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/ forum?id=tcsZt9ZNKD. Difei Gu, Yunhe Gao, Gerasimos Chatzoudis, Zihan Dong, Guoning Zhang, B","arxiv_match":false}],"id":"2406.04093","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":379},{"title":"Gaussian Error Linear Units (GELUs)","arxiv_id":"1606.08415","year":2016,"url":"https://arxiv.org/abs/1606.08415","citing_submissions":277,"explicit_arxiv_matches":257,"examples":[{"id":"hjPoL9WGRc","title":"AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents","forum_url":"https://openreview.net/forum?id=hjPoL9WGRc","excerpt":"ions (ICLR), 2020. arXiv:1912.01603. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse control tasks through world models. Nature, 640(8059):647–653, 2025. doi: 10.1038/s41586-025-08744-2. Nicklas Hansen, Hao Su, and Xiaolong Wang. TD-MPC2: Scalable, robust world models for continuous control. In International Conference on Learning Representations (ICLR), 2024. arXiv:2310.16828. Dan Hendrycks and Kevin Gimpel. arXiv:1606.08415, 2016. Gaussian error linear units (GELUs). arXiv preprint Shahab Heshmati-Alamdari, George C. Karr","arxiv_match":true},{"id":"qZy9kImfTu","title":"Deep Transported Gaussian Models for the Correlation Matrices","forum_url":"https://openreview.net/forum?id=qZy9kImfTu","excerpt":"rial and Applied Mathematics, Philadelphia, PA, 2008. ISBN 978-0-89871-646-7. Chen Hu, Ziheng Chen, Rui Wang, Yefeng Zheng, and Nicu Sebe. Riemannian high-order pooling for brain foundation models. In The Fourteenth International Conference on Learning Representations, 2026. Vinay Jayaram and Alexandre Barachant. MOABB: Trustworthy algorithm benchmarking for BCIs. Journal of Neural Engineering, 15(6):066011, 2018. Dan Hendrycks and Kevin Gimpel. arXiv:1606.08415, 2016. Gaussian error linear units (GELUs). arXiv preprint Sture Holm. A simple sequentially reject","arxiv_match":true},{"id":"pfajkeag8r","title":"Making Darkness Contagious: Prior-Guided Synthesis for Low-Light Object Detection","forum_url":"https://openreview.net/forum?id=pfajkeag8r","excerpt":"ical features for object detection and beyond under low-light vision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6725– 6735, 2023. Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (GELUs), 2016. Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu. Crafting object detection in very low light. In Proceedings of the British Machine Vision Conference, 2021. doi: 10.5244/C.35.21. Mingbo Hong","arxiv_match":false}],"id":"1606.08415","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":380},{"title":"Language agent tree search unifies reasoning acting and planning in language models","arxiv_id":"2310.04406","year":2023,"url":"https://arxiv.org/abs/2310.04406","citing_submissions":276,"explicit_arxiv_matches":99,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"v:2507.18071, 2025b. Xuhui Zheng, Kang An, Ziliang Wang, Yuhang Wang, and Yichao Wu. Stepsearch: Igniting llms search ability via step-wise proximal policy optimization. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 21816–21841, 2025c. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. Language agent tree search unifies reasoning acting and planning in language models. arXiv preprint arXiv:2310.04406, 2023. Zefang Zong, Dingwei Chen, Yang Li, Qi Yi, Bo Zhou, Chengming Li, Bo Qian, Peng Chen, and Ji","arxiv_match":true},{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":" Learning Representations, 2023. Murong Yue, Jie Zhao, Min Zhang, Liang Du, and Ziyu Yao. Large language model cascades with mixture of thought representations for cost-efficient reasoning. In International Conference on Learning Representations, volume 2024, pp. 21691–21728, 2024. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. Language agent tree search unifies reasoning, acting, and planning in language models. arXiv preprint arXiv:2310.04406, 2024. A The uncertainty score ut in the deferral rule (Sec. 3) can be any information-theoretic qu","arxiv_match":true},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"g Representations, volume 2024, pp. 19036–19066, 2024. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pp. 19724–19731, 2024. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. Language agent tree search unifies reasoning acting and planning in language models. arXiv preprint arXiv:2310.04406, 2023. Jiawei Zhou, Chi Zhang, Xiang Feng, Qiming Zhang, Haibo Qiu, Lihuo He, Dengpan Ye, Xinbo Gao","arxiv_match":true}],"id":"2310.04406","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":381},{"title":"Unified training of universal time series forecasting transformers","arxiv_id":"2402.02592","year":2024,"url":"https://arxiv.org/abs/2402.02592","citing_submissions":275,"explicit_arxiv_matches":41,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":" Interventions in Time Series Foundation Models. In International Conference on Machine Learning (ICML), 2025. Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. Unified Training of Universal Time Series Forecasting Transformers. In International Conference on Machine Learning (ICML), 2024. Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. An Explanation of in-Context Learning as Implicit Bayesian Inference. I","arxiv_match":false},{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"mmad Javad Darvishi Bayazi, George Adamopoulos, Roland Riachi, Nadhir Hassen, et al. Lag-llama: Towards foundation models for probabilistic time series forecasting. arXiv preprint arXiv:2310.08278, 2023. URL https://arxiv.org/abs/2310.08278. Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. Unified training of universal time series forecasting transformers. arXiv preprint arXiv:2402.02592, 2024. URL https://arxiv.org/abs/2402.02592. Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition transformers with","arxiv_match":true},{"id":"LJ3tnmkITu","title":"Recurrent Domain Incremental Adaptation of Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=LJ3tnmkITu","excerpt":"dels: A comprehensive survey and benchmark. IEEE Trans. Pattern Anal. Mach. Intell., 48(10):11642–11662, 2026. Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. Unified training of universal time series forecasting transformers. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, volume 235 of Proceedings of Machine Learning Research, pp. 53140–53164. PMLR / OpenReview.","arxiv_match":false}],"id":"2402.02592","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":382},{"title":"Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis","arxiv_id":"2306.09341","year":2023,"url":"https://arxiv.org/abs/2306.09341","citing_submissions":273,"explicit_arxiv_matches":259,"examples":[{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"sen Feng, Xiaoxiao Xu, Yi Wang, Yichang Zhang, Yongqiang Zhu, Yujia Wu, Yuxuan Cai, and Zenan Liu. Qwen-image technical report. arXiv preprint arXiv:2508.02324, 2025. URL https://arxiv.org/abs/2508.02324. Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-toimage synthesis. arXiv preprint arXiv:2306.09341, 2023. URL https://arxiv.org/abs/ 2306.09341. Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, and Difa","arxiv_match":true},{"id":"41Axobxx7a","title":"Scaling Muon for Diffusion Transformers","forum_url":"https://openreview.net/forum?id=41Axobxx7a","excerpt":" Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, and Sham Kakade. SOAP: Improving and stabilizing shampoo using adam for language modeling. In International Conference on Learning Representations, volume 2025, pp. 93423–93444, 2025. Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-toimage synthesis. arXiv preprint arXiv:2306.09341, 2023. Yuanyang Yin, Yaqi Zhao, Mingwu Zheng, Ke Lin, Jiarong Ou, Rui Chen, Victor Shea-Jay Huang, ","arxiv_match":true},{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":", Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8228–8238, 2024. Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341, 2023. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Do","arxiv_match":true}],"id":"2306.09341","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":383},{"title":"MLE-bench: Evaluating machine learning agents on machine learning engineering","arxiv_id":"2410.07095","year":2024,"url":"https://arxiv.org/abs/2410.07095","citing_submissions":273,"explicit_arxiv_matches":92,"examples":[{"id":"P9bJmoIx5X","title":"Controlling False Discoveries via Auditing Scientific Agents","forum_url":"https://openreview.net/forum?id=P9bJmoIx5X","excerpt":"01621459. 2014.999157. Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Aleksander Madry, and Lilian Weng. MLEbench: Evaluating machine learning agents on machine learning engineering. In International Conference on Learning Representations (ICLR), 2025. URL https://openreview.net/ forum?id=6s5uXNWGIh. Tingting Chen, Beibei Lin, Zifeng Yuan, Qiran Zou, Hongyu He, Anirudh Goyal, Ye","arxiv_match":false},{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"38/s42256-024-00832-8. Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Aleksander Madry, and Lilian Weng. MLEbench: Evaluating machine learning agents on machine learning engineering. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview. net/forum?id=6s5uXNWGIh. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oli","arxiv_match":false},{"id":"6EWMGy11eE","title":"CreditAgentBench: Measuring Agentic Credit-Model Repair under Production Constraints","forum_url":"https://openreview.net/forum?id=6EWMGy11eE","excerpt":"07/s10614-020-10042-0. Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Aleksander Madry, and Lilian Weng. MLEbench: Evaluating machine learning agents on machine learning engineering. In The Thirteenth International Conference on Learning Representations, 2025. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri B","arxiv_match":false}],"id":"2410.07095","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":384},{"title":"Dora: Weight-decomposed low-rank adaptation","arxiv_id":"2402.09353","year":2024,"url":"https://arxiv.org/abs/2402.09353","citing_submissions":272,"explicit_arxiv_matches":52,"examples":[{"id":"vFk3Vx7BZt","title":"TriBLoRA: Tri-Factor Blockwise Low-Rank Adaptation","forum_url":"https://openreview.net/forum?id=vFk3Vx7BZt","excerpt":"tion for Computational Linguistics (Volume 1: Long Papers), pp. 29252–29269, 2026. Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, KwangTing Cheng, and Min-Hung Chen. DoRA: Weight-decomposed low-rank adaptation. In Proceedings of the 41st International Conference on Machine Learning, pp. 32100–32121, 2024. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Z","arxiv_match":false},{"id":"OFVWcaGfdi","title":"The Delta Is Not the Skill: Zero-Prior Elicitation Transfers LoRA Behavior Across Model Families","forum_url":"https://openreview.net/forum?id=OFVWcaGfdi","excerpt":"uning transfer. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 2617–2636, 2025. Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, and Noah A Smith. Tuning language models by proxy. arXiv preprint arXiv:2401.08565, 2024a. Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, KwangTing Cheng, and Min-Hung Chen. Dora: Weight-decomposed low-rank adaptation. arXiv preprint arXiv:2402.09353, 2024b. Julian Minder, Clément Dumas, Caden Juang, Bilal Chughtai, and Neel Nanda. Overcoming spar","arxiv_match":true},{"id":"J6s4pH2a2M","title":"LLM2llm: Parameter-Efficient Adaptation via Transferred Experts from MoE LLMs","forum_url":"https://openreview.net/forum?id=J6s4pH2a2M","excerpt":"for effective training of language models. arXiv preprint arXiv:2605.08842, 2026b. Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, KwangTing Cheng, and Min-Hung Chen. Dora: Weight-decomposed low-rank adaptation. In Forty-first International Conference on Machine Learning, 2024b. Fanxu Meng, Zhaohui Wang, and Muhan Zhang. Pissa: Principal singular values and singular vectors adaptation of large language mode","arxiv_match":false}],"id":"2402.09353","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":385},{"title":"Gorilla: Large language model connected with massive APIs","arxiv_id":"2305.15334","year":2023,"url":"https://arxiv.org/abs/2305.15334","citing_submissions":272,"explicit_arxiv_matches":89,"examples":[{"id":"o7ODDwTMgU","title":"Valid but Wrong: Decomposing Language-Model Errors on Closed Vocabularies","forum_url":"https://openreview.net/forum?id=o7ODDwTMgU","excerpt":", Carlos F. Enguix, and Kusum Lata. Text2kgbench: A benchmark for ontology-driven knowledge graph generation from text, 2023. URL https://arxiv.org/abs/2308.02357. Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick, Nadia Polikarpova, and Loris D’Antoni. Grammar-aligned decoding, 2025. URL https://arxiv.org/abs/2405.21047. Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez. Gorilla: Large language model connected with massive apis. arXiv preprint arXiv:2305.15334, 2023. Shishir G. Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, ","arxiv_match":true},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"imulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pp. 1–22, 2023. Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. Gorilla: Large language model connected with massive apis. Proceedings of Advances in Neural Information Processing Systems, pp. 126544–126565, 2024. Cheng Qian, Emre Can Acikgoz, Qi He, Hongru Wang, Xiusi Chen, Dilek Hakkani-Tür, Gokhan Tur, and Heng Ji. ","arxiv_match":false},{"id":"CFVCzCNC1l","title":"Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=CFVCzCNC1l","excerpt":"dings-acl.326. Aaron Parisi, Yao Zhao, and Noah Fiedel. TALM: Tool augmented language models. arXiv preprint arXiv:2205.12255, 2022. Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez. Gorilla: Large language model connected with massive APIs. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 126544–126565. Curran Associates, Inc.,","arxiv_match":false}],"id":"2305.15334","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":386},{"title":"Roberta: A robustly optimized bert pretraining approach","arxiv_id":"1907.11692","year":2019,"url":"https://arxiv.org/abs/1907.11692","citing_submissions":271,"explicit_arxiv_matches":249,"examples":[{"id":"wslxAq80Kb","title":"Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models","forum_url":"https://openreview.net/forum?id=wslxAq80Kb","excerpt":" pp. 273–294. Springer, 2018. Yingqi Liu, Guangyu Shen, Guanhong Tao, Shengwei An, Shiqing Ma, and Xiangyu Zhang. Piccolo: Exposing complex backdoors in NLP transformer models. In 2022 IEEE Symposium on Security and Privacy (SP), pp. 2025–2042. IEEE, 2022. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692, 2019. Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. Le","arxiv_match":true},{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"dreas Krause (eds.), Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pp. 3122–3130. PMLR, 10–15 Jul 2018. URL https://proceedings.mlr. press/v80/lipton18a.html. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. 2019a. URL https://arxiv.org/abs/1907.11692. Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A c","arxiv_match":true},{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":". Su, X. Yao, Z. Jiang, G. Lai, Y. Du, Y. Qin, W. Xu, E. Lu, J. Yan, Y. Chen, H. Zheng, Y. Liu, S. Liu, B. Yin, W. He, H. Zhu, Y. Wang, J. Wang, M. Dong, Z. Zhang, Y. Kang, H. Zhang, X. Xu, Y. Zhang, Y. Wu, X. Zhou, and Z. Yang. Muon is scalable for llm training, 2025. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. Roberta: A robustly optimized bert pretraining approach, 2019. URL https: //arxiv.org/abs/1907.11692. A. Muhamed, O. Li, D. P. Woodruff, M. Diab, and V. Smith. Grass: Compute efficient lowmemory llm t","arxiv_match":true}],"id":"1907.11692","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":387},{"title":"LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics","arxiv_id":"2511.08544","year":2025,"url":"https://arxiv.org/abs/2511.08544","citing_submissions":269,"explicit_arxiv_matches":254,"examples":[{"id":"P6EnVJlhxB","title":"What Can Latent World Models Know? Physical Information in Multimodal Predictive Representations","forum_url":"https://openreview.net/forum?id=P6EnVJlhxB","excerpt":"a Meier, Yann LeCun, Michael Rabbat, and Nicolas Ballas. V-JEPA 2: Self-supervised video models enable understanding, prediction and planning. arXiv preprint arXiv:2506.09985, 2025. doi: 10.48550/arXiv.2506.09985. URL https://arxiv.org/abs/2506.09985. Randall Balestriero and Yann LeCun. LeJEPA: Provable and scalable self-supervised learning without the heuristics. arXiv preprint arXiv:2511.08544, 2025. doi: 10.48550/arXiv.2511.08544. URL https://arxiv.org/abs/2511.08544. Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances. Computational Linguis","arxiv_match":true},{"id":"itoxkUzY2n","title":"MEND: Measuring and Constraining Latent Dynamics in World Models","forum_url":"https://openreview.net/forum?id=itoxkUzY2n","excerpt":"2: Self-supervised video models enable understanding, prediction and planning. arXiv preprint arXiv:2506.09985, 2025. Omri Azencot, N Benjamin Erichson, Vanessa Lin, and Michael Mahoney. Forecasting sequential data using consistent koopman autoencoders. In International Conference on Machine Learning (ICML), pp. 475–485. PMLR, 2020. Randall Balestriero and Yann LeCun. Lejepa: Provable and scalable self-supervised learning without the heuristics. arXiv preprint arXiv:2511.08544, 2025. Adrien Bardes, Jean Ponce, and Yann LeCun. VICReg: Variance-invariance-covariance regulariza","arxiv_match":true},{"id":"RIpEtbedmF","title":"LeWM++: Repairing the Planning Interface of Latent World Models","forum_url":"https://openreview.net/forum?id=RIpEtbedmF","excerpt":" Randall Balestriero and Yann LeCun. Lejepa: Provable and scalable self-supervised learning without the heuristics. arXiv preprint arXiv:2511.08544, 2025. Niccolò Caselli, Salvatore Lo Sardo, Francesco Massafra, Ippokratis Pantelidis, Samuele Pun","arxiv_match":true}],"id":"2511.08544","metadata_source":"Cached arXiv API metadata","rank":388},{"title":"Self-Distillation Enables Continual Learning","arxiv_id":"2601.19897","year":2026,"url":"https://arxiv.org/abs/2601.19897","citing_submissions":268,"explicit_arxiv_matches":199,"examples":[{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":" Learning Research, pp. 627–635, Fort Lauderdale, FL, USA, 11–13 Apr 2011. PMLR. URL https://proceedings.mlr.press/ v15/ross11a.html. Idan Shenfeld, Mehul Damani, Jonas Hübotter, and Pulkit Agrawal. Self-distillation enables continual learning. In Forty-third International Conference on Machine Learning, 2026a. URL https://openreview.net/forum?id=qA6FgH0nnZ. Idan Shenfeld, Jyothish Pari, and Pulkit Agrawal. RL’s razor: Why online reinforce","arxiv_match":false},{"id":"fG6gMiEzqt","title":"From Measured Biology to Analysis Decisions: How Far Does Post-Trained Knowledge Travel?","forum_url":"https://openreview.net/forum?id=fG6gMiEzqt","excerpt":"5: Towards native multimodal agents, February 2026. URL https://qwen. ai/blog?id=qwen3.5. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Idan Shenfeld, Mehul Damani, Jonas Hübotter, and Pulkit Agrawal. Self-distillation enables continual learning. arXiv preprint arXiv:2601.19897, 2026. Elana Simon, Kyle Swanson, and James Zou. Language models for biological research: a primer.","arxiv_match":true},{"id":"50kJuTajGS","title":"ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation","forum_url":"https://openreview.net/forum?id=50kJuTajGS","excerpt":"/v1/2024.findings-naacl.264. URL https://aclanthology.org/2024.findings-naacl.264/. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. URL https://arxiv.org/abs/2402.03300. Idan Shenfeld, Mehul Damani, Jonas Hübotter, and Pulkit Agrawal. Self-distillation enables continual learning. arXiv preprint arXiv:2601.19897, 2026. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion","arxiv_match":true}],"id":"2601.19897","metadata_source":"Cached arXiv API metadata","rank":389},{"title":"GShard: Scaling giant models with conditional computation and automatic sharding","arxiv_id":"2006.16668","year":2020,"url":"https://arxiv.org/abs/2006.16668","citing_submissions":267,"explicit_arxiv_matches":116,"examples":[{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"024. URL https: //proceedings.mlr.press/v235/ludziejewski24a.html. Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen. Gshard: Scaling giant models with conditional computation and automatic sharding. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=qrwe7XHTmYb. Xi Victoria Lin, Akshat Shrivastava, Liang Luo, Srinivasan Iyer, Mike Lewis, Gargi Gho","arxiv_match":false},{"id":"vSjw3bWmyF","title":"Who Contributes? Counterfactual Attribution in Sparse Mixture-of-Experts","forum_url":"https://openreview.net/forum?id=vSjw3bWmyF","excerpt":"amage. In Advances in Neural Information Processing Systems, 1989. Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen. GShard: Scaling giant models with conditional computation and automatic sharding. In International Conference on Learning Representations, 2021. Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, and Luke Zettlemoyer. BASE layers: Simplifying training of large, sparse models.","arxiv_match":false},{"id":"fkF1iei3Gs","title":"ACNA: Decentralized Active-Cell Routing for Sparse and Dynamically Expandable Neural Networks","forum_url":"https://openreview.net/forum?id=fkF1iei3Gs","excerpt":"control signal and avoids heavy computation, while future cell-aware hardware must translate that signal into efficient dispatch, memory locality, clock gating, and, where practical, power gating. The present work therefore establishes feasibility and an experimentally grounded hardware path without claiming a measured FPGA/ASIC energy reduction. D. Lepikhin et al. GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. arXiv:2006.16668, 2020. W. Fedus, B. Zoph, and N. Shazeer. Switch Transformers: Scaling to Trillion Parameter Models","arxiv_match":true}],"id":"2006.16668","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":390},{"title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","arxiv_id":"1708.07747","year":2017,"url":"https://arxiv.org/abs/1708.07747","citing_submissions":266,"explicit_arxiv_matches":242,"examples":[{"id":"oq0BXtA19v","title":"Test-Time Compositional Generalization in Diffusion Models via Prototype Discovery","forum_url":"https://openreview.net/forum?id=oq0BXtA19v","excerpt":". In Advances in Neural Information Processing Systems, 2025. Zekun Wang, Karthik Singaravadivelan, and Christopher J. MacLellan. Diffusion models and concept formation, 2026. URL https://arxiv.org/abs/2609.13047. Andrew P Witkin. Scale-space filtering. In International Joint Conference on Artificial Intelligence, 1983. Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam","arxiv_match":true},{"id":"RxwKuiYnHn","title":"BestSHAP: Best-of-Many Adaptive Approximations for the Shapley Value","forum_url":"https://openreview.net/forum?id=RxwKuiYnHn","excerpt":"view.net/forum?id=HD6bWcj87Y. R. Teal Witter, Yurong Liu, and Christopher Musco. Regression-adjusted monte carlo estimators for Shapley values and probabilistic values. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id= Qabko39AS5. Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms, 2017. URL https://arxiv.org/abs/1708. 07747. Jiayao Zhang, Qiheng Sun, Jinfei Liu, Li Xiong, Jian Pei, and Kui Ren. Efficient sampling approach","arxiv_match":true},{"id":"3bA5dzjq5O","title":"Anchor in Evidence: Distribution-Preserving Backdoor Attacks in Federated Learning","forum_url":"https://openreview.net/forum?id=3bA5dzjq5O","excerpt":"Neural Information Processing Systems, 38:154985–155017, 2026. Dawood Wasif, Terrence Moore, and Jin-Hee Cho. Resfl: An uncertainty-aware framework for responsible federated learning by balancing privacy, fairness and utility. In International Conference on Learning Representations, volume 2026, pp. 142238–142275, 2026. Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li. Dba: Distributed backdoor attacks against fe","arxiv_match":true}],"id":"1708.07747","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":391},{"title":"The geometry of truth: Emergent linear structure in large language model representations of true/false datasets","arxiv_id":"2310.06824","year":2023,"url":"https://arxiv.org/abs/2310.06824","citing_submissions":266,"explicit_arxiv_matches":125,"examples":[{"id":"FrxiIrkLSN","title":"The Anatomy of Conversational Consistency: Linear Readouts and Control Under Pressure","forum_url":"https://openreview.net/forum?id=FrxiIrkLSN","excerpt":"ions with large language models. Transactions on Machine Learning Research, 2026b. ISSN 2835-8856. URL https: //openreview.net/forum?id=UYNQXPevpF. Survey Certification. Samuel Marks and Max Tegmark. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=aajyHYjjsk. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, San","arxiv_match":false},{"id":"fXt5rH6mod","title":"Position: The Linear Representation Hypothesis Needs a Group Action","forum_url":"https://openreview.net/forum?id=fXt5rH6mod","excerpt":"e of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751, 2013. URL https://aclanthology.org/N13-1090/. Samuel Marks and Max Tegmark. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=aajyHYjjsk. Chris Olah. What is a linear representation? what is a multidimensional feature? Transformer Circuits ","arxiv_match":false},{"id":"W5LtRG0F8Z","title":"Locating, Removing, and Restoring: Auditing the Modular Account of Safety in Instruction-Tuned LLMs","forum_url":"https://openreview.net/forum?id=W5LtRG0F8Z","excerpt":"9962–19978, 2024. Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li. Trustworthy llms: a survey and guideline for evaluating large language models’ alignment, 2024. URL https://arxiv.org/abs/2308. 05374. Samuel Marks and Max Tegmark. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets, 2024. URL https://arxiv.org/abs/2310. 06824. Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel L","arxiv_match":true}],"id":"2310.06824","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":392},{"title":"Cosmos World Foundation Model Platform for Physical AI","arxiv_id":"2501.03575","year":2025,"url":"https://arxiv.org/abs/2501.03575","citing_submissions":265,"explicit_arxiv_matches":247,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"n Machine Learning Research, 2024. Jack Parker-Holder and Shlomi Fruchter. Genie 3: A new frontier for world models. Google DeepMind Blog, 2025. URL https://deepmind.google/blog/ genie-3-a-new-frontier-for-world-models/. https://docs.nvidia.com/cosmos/latest/cosmos3/model NVIDIA, Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, et al. Cosmos World Foundation Model Platform for Physical AI. arXiv preprint arXiv:2501.03575, 2025. URL https://arxiv.org/ abs/2501.03575. Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, et al. Movie Gen: A Cast of Media Foundatio","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":" enhances robot learning, 2024. URL https://arxiv.org/abs/2406.11815. Dantong Niu, Yuvan Sharma, Haoru Xue, Giscard Biamby, Junyi Zhang, Ziteng Ji, Trevor Darrell, and Roei Herzig. Pre-training auto-regressive robotic models with 4d representations. In Fortysecond International Conference on Machine Learning, 2025. URL https://openreview. net/forum?id=2FDsh5D2Th. NVIDIA. Cosmos world foundation model platform for physical ai, 2025a. URL https:// arxiv.org/abs/2501.03575. NVIDIA. World simulation with video foundation models for physical ai, 2025b. URL https: //arxiv.o","arxiv_match":true},{"id":"GM9LePOwrX","title":"Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=GM9LePOwrX","excerpt":"ang, Ting-Chun Wang, Fangyin Wei, Xinyue Wei, Jay Zhangjie Wu, Jiashu Xu, Wei Yang, Yen-Chen Lin, Xiaohui Zeng, Yu Zeng, Jing Zhang, Qinsheng Zhang, Yuxuan Zhang, Qingqing Zhao, and Artur Zólkowski. Cosmos world foundation model platform for physical AI. CoRR, abs/2501.03575, 2025. Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, and R","arxiv_match":false}],"id":"2501.03575","metadata_source":"Cached arXiv API metadata","rank":393},{"title":"MVBench: A comprehensive multi-modal video understanding benchmark","arxiv_id":"2311.17005","year":2023,"url":"https://arxiv.org/abs/2311.17005","citing_submissions":265,"explicit_arxiv_matches":32,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"arxiv.org/abs/2210.03929. Kimi Team. Kimi K3: Open Frontier Intelligence. arXiv preprint arXiv:2607.24653, 2026. URL https://arxiv.org/abs/2607.24653. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, Limin Wang, and Yu Qiao. MVBench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. URL https://arxiv.org/abs/2311.17005. Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian, Victor Jouault, Abhinav Rastogi, Adrien ","arxiv_match":true},{"id":"w8aZY88Jbh","title":"ReVA: A New Scene-Centric Dataset for Remote Sensing Video Question Answering","forum_url":"https://openreview.net/forum?id=w8aZY88Jbh","excerpt":"tion aerial images. ISPRS Journal of Photogrammetry and Remote Sensing, 214: 65–81, 2024b. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22195–22206, 2024c. Xinhao Li, Yi Wang, Jiashuo Yu, Xiangyu Zeng, Yuhan Zhu, Haian Huang, Jianfei Gao, Kunch","arxiv_match":false},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"uage models. In International conference on machine learning, pp. 19730–19742. PMLR, 2023. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22195–22206, 2024b. KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang,","arxiv_match":false}],"id":"2311.17005","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":394},{"title":"Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers","arxiv_id":"2002.10957","year":2020,"url":"https://arxiv.org/abs/2002.10957","citing_submissions":265,"explicit_arxiv_matches":31,"examples":[{"id":"Y15G7fdvPv","title":"S$^2$Aligner: Pair-Efficient Transferable Pre-Training for Sparse Text-Attributed Graphs","forum_url":"https://openreview.net/forum?id=Y15G7fdvPv","excerpt":"ngan Majumder, and Furu Wei. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in neural information processing systems, 33:5776–5788, 2020. Zhihao Wen and Yuan Fang. Augmenting low-resource text classification with graphgrounded pre-training and prompting. In Proceed","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"nid Hurwicz, and Hirofumi Uzawa (eds.), Studies in Linear and Nonlinear Programming, pp. 154–165. Stanford University Press, 1958. Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. In Advances in Neural Information Processing Systems, volume 33, 2020. Chaojun Xiao, Pengle Zhang, Xu Han, Guangxuan Xiao, Yankai Lin, Zhengyan Zhang, Zhiyuan Liu, and Maosong Sun. InfLLM: Training-","arxiv_match":false},{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"Yu-Gang Jiang, and Yinghao Xu. RepWAM: World action modeling with representation visual-action tokenizers. arXiv:2606.13674, 2026. Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. MiniLM: Deep selfattention distillation for task-agnostic compression of pre-trained transformers. In NeurIPS, 2020. Chuan Wen, Xingyu Lin, John So, Kai Chen, Qi Dou, Yang Gao, and Pieter Abbeel. Any-point trajectory modeling for policy learning. In RSS, 2024. Yuxin Wu and Kaiming He. Group norma","arxiv_match":false}],"id":"2002.10957","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":395},{"title":"Simplified and generalized masked diffusion for discrete data","arxiv_id":"2406.04329","year":2024,"url":"https://arxiv.org/abs/2406.04329","citing_submissions":265,"explicit_arxiv_matches":43,"examples":[{"id":"6HZCBDqoCa","title":"DisCoMesh: Efficient and Flexible Artist-Mesh Generation via Sparse Computation powered Discrete Diffusion","forum_url":"https://openreview.net/forum?id=6HZCBDqoCa","excerpt":" Kuleshov. Simple and effective masked diffusion language models. In Advances in Neural Information Processing Systems, volume 37, 2024. doi: 10.52202/ 079017-4135. URL https://arxiv.org/abs/2406.07524. Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K. Titsias. Simplified and generalized masked diffusion for discrete data. In Advances in Neural Information Processing Systems, volume 37, 2024. doi: 10.52202/079017-3277. URL https://arxiv.org/abs/2406. 04329. Yawar Siddiqui, Antonio Alliegro, Alexey Artemov, Tatiana Tommasi, Daniele Sirigatti, Vladislav Ro","arxiv_match":true},{"id":"bkrXDY9uXm","title":"Ordered-DLM: Ordering Token Generation through Syntactic Hierarchy","forum_url":"https://openreview.net/forum?id=bkrXDY9uXm","excerpt":"Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long. 559. URL https://aclanthology.org/2021.acl-long.559/. Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis Titsias. Simplified and generalized masked diffusion for discrete data. Advances in neural information processing systems, 37: 103131–103167, 2024. Jean-Francois Ton, Muhammad Faaiz Taufiq, and Yang Liu. Understanding chain-of-thought in LLMs through information theory.","arxiv_match":false},{"id":"xs3B5JItLk","title":"PACE: Prune Aggressively and Commit Early for Diffusion VLM Inference Acceleration","forum_url":"https://openreview.net/forum?id=xs3B5JItLk","excerpt":"cient large multimodal models. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 22857–22867. IEEE, 2025. Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis Titsias. Simplified and generalized masked diffusion for discrete data. Advances in neural information processing systems, 37: 103131–103167, 2024. Donald M Topkis. Minimizing a submodular function on a lattice. Operations research, 26(2): 305–321, 1978. Ashish Vaswani,","arxiv_match":false}],"id":"2406.04329","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":396},{"title":"ChatDev: Communicative agents for software development","arxiv_id":"2307.07924","year":2023,"url":"https://arxiv.org/abs/2307.07924","citing_submissions":264,"explicit_arxiv_matches":39,"examples":[{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"or evaluating image-to-code generation. arXiv preprint arXiv:2605.11307, 2026. Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, et al. Chatdev: Communicative agents for software development. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 15174–15186, 2024. Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Ch","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"https://openai.com/index/ gpt-6-astra/, September 2026b. Accessed: 2026-09-25. Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, et al. Chatdev: Communicative agents for software development. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 15174–15186, 2024. Jianhao Ruan, Zhihao Xu, Yiran Peng, Fashen Ren, Zhaoyang Y","arxiv_match":false},{"id":"x9qK9ehqNi","title":"Coordinated Mixed-Precision KV Cache Quantization for Efficient LLM-based Multi-agent Systems","forum_url":"https://openreview.net/forum?id=x9qK9ehqNi","excerpt":"23.findings-emnlp.378. URL https://aclanthology.org/2023. findings-emnlp.378/. Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, et al. Chatdev: Communicative agents for software development. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 15174–15186, 2024. Qwen Team. Qwen3.8-27B model card. Hugging Face, 2026. URL ","arxiv_match":false}],"id":"2307.07924","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":397},{"title":"Identifying the risks of LM agents with an LM-emulated sandbox","arxiv_id":"2309.15817","year":2023,"url":"https://arxiv.org/abs/2309.15817","citing_submissions":264,"explicit_arxiv_matches":61,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":" interpretability, 2026. URL https://arxiv.org/abs/2604.01151. Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, and Tatsunori Hashimoto. Identifying the risks of LM agents with an LM-emulated sandbox. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=GEcwtMk1uA. Yiling Tao, Xinran Zheng, Shuo Yang, Meiling Tao, and Xingjun Wang. GroupGu","arxiv_match":false},{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":"ttps://developers.openai.com/api/docs/models/ gpt-5.6-sol, 2026a. Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris Maddison, and Tatsunori Hashimoto. Identifying the risks of lm agents with an lm-emulated sandbox. In International Conference on Learning Representations, volume 2024, pp. 27031–27098, 2024. Ravi S Sandhu, Edward J Coyne, Hal L Feinstein, and Charles E Youman. Role-based access control models. C","arxiv_match":false},{"id":"Uowq0ts0EJ","title":"AdaGuard: An Adaptive Guard Model with User-defined Policies","forum_url":"https://openreview.net/forum?id=Uowq0ts0EJ","excerpt":"n for Computational Linguistics: EMNLP 2023, pp. 4694–4702, 2023. Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris Maddison, and Tatsunori Hashimoto. Identifying the risks of lm agents with an lm-emulated sandbox. 2024:27031–27098, 2024. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can te","arxiv_match":false}],"id":"2309.15817","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":398},{"title":"Vision transformers need registers","arxiv_id":"2309.16588","year":2023,"url":"https://arxiv.org/abs/2309.16588","citing_submissions":264,"explicit_arxiv_matches":43,"examples":[{"id":"ubiZuUXpr6","title":"Copper-Policy: Focus on the Representation for Robust Robot Manipulation","forum_url":"https://openreview.net/forum?id=ubiZuUXpr6","excerpt":" Diffusion. In Proceedings of Robotics: Science and Systems, Daegu, Republic of Korea, July 2023. doi: 10.15607/RSS. 2023.XIX.026. Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. In B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (eds.), International Conference on Learning Representations, volume 2024, pp. 2632–2652, 2024. URL https://proceedings.iclr.cc/p","arxiv_match":false},{"id":"huYd6eviSI","title":"Twinsight: Guiding Human Teaching and Policy Adaptation with Learner Forecasts","forum_url":"https://openreview.net/forum?id=huYd6eviSI","excerpt":"cs and Automation, ICRA 2025, Atlanta, GA, USA, May 19-23, 2025, pp. 8291–8298. IEEE, 2025c. doi: 10.1109/ICRA55743.2025.11128717. Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. Shivin Dass, Karl Pertsch, Hejia Zhang, Youngwoon Lee, Joseph J","arxiv_match":false},{"id":"8p5UQCDo47","title":"Align2Refine: Diagnosing and Refining Video Representations through Spatial Latent Alignment","forum_url":"https://openreview.net/forum?id=8p5UQCDo47","excerpt":" urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213–3223, 2016. Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers. In International conference on learning representations, volume 2024, pp. 2632–2652, 2024. Henghui Ding, Chang Liu, Shuting He, Xudong Jiang, Philip HS Torr, and Song Bai. Mose: A new dataset for vi","arxiv_match":false}],"id":"2309.16588","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":399},{"title":"The LAMBADA dataset: Word prediction requiring a broad discourse context","arxiv_id":"1606.06031","year":2016,"url":"https://arxiv.org/abs/1606.06031","citing_submissions":260,"explicit_arxiv_matches":33,"examples":[{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"tps://openreview.net/forum? id=q44uq3tc2D. Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc-Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. The lambada dataset: Word prediction requiring a broad discourse context. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 1525–1534, 2016. Piotr Pi˛ekos, Róbert Csordás, and Jürgen Schmidhuber. Mixtur","arxiv_match":false},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":". arXiv preprint arXiv:2607.15456, 2026. Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc-Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. The LAMBADA dataset: Word prediction requiring a broad discourse context. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 1525–1534, 2016. Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick, and ","arxiv_match":false},{"id":"k9j0YWUVI2","title":"AlignQuant: Tile-Aligned Mixed-Precision Quantization for Efficient LLM Generation","forum_url":"https://openreview.net/forum?id=k9j0YWUVI2","excerpt":". arXiv preprint arXiv:2602.01027, 2026. Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc-Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. The lambada dataset: Word prediction requiring a broad discourse context. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 1525–1534, 2016. Gunho Park, Jeongin Bae, Beomseok Kwon, Byeongwook Kim, Se Ju","arxiv_match":false}],"id":"1606.06031","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":400},{"title":"Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning","arxiv_id":"2601.16163","year":2026,"url":"https://arxiv.org/abs/2601.16163","citing_submissions":258,"explicit_arxiv_matches":167,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"rth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn. Openvla: An open-source vision-language-action model, 2024. URL https://arxiv.org/ abs/2406.09246. Moo Jin Kim et al. Cosmos policy: Fine-tuning video models for visuomotor control and planning, 2026. URL https://arxiv.org/abs/2601.16163. Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. End-to-end training of deep visuom","arxiv_match":true},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success. In Proceedings of Robotics: Science and Systems, 2025. URL https://arxiv.org/abs/2502.19645. Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin, Yen-Chen Lin, Yunhao Ge, Grace Lam, Percy Liang, Shuran Song, Ming-Yu Liu, Chelsea Finn, and Jinwei Gu. Cosmos policy: Fine-tuning video models for visuomotor control and planning. arXiv preprint arXiv:2601.16163, 2026. URL https://arxiv.org/abs/2601.16163. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue","arxiv_match":true},{"id":"FZ7Aq4uPre","title":"UniWAM: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision","forum_url":"https://openreview.net/forum?id=FZ7Aq4uPre","excerpt":"EEE Transactions on Systems Science and Cybernetics, 4(2):100–107, 1968. InternNav Team. InternVLA-N1: An open dual-system navigation foundation model with learned latent plans. Technical report, 2025. URL https://internrobotics.github.io/ internvla-n1.github.io/. Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin, Yen-Chen Lin, Yunhao Ge, Grace Lam, Percy Liang, Shuran Song, et al. Cosmos policy: Fine-tuning video models for visuomotor control and planning. arXiv preprint arXiv:2601.16163, 2026. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, e","arxiv_match":true}],"id":"2601.16163","metadata_source":"Cached arXiv API metadata","rank":401},{"title":"Back to Basics: Let Denoising Generative Models Denoise","arxiv_id":"2511.13720","year":2025,"url":"https://arxiv.org/abs/2511.13720","citing_submissions":257,"explicit_arxiv_matches":108,"examples":[{"id":"D2lvweC7MC","title":"When Are Diffusion Recovery Clocks Identifiable? A Spectral Measurement Protocol for Teacher-Forced Denoising","forum_url":"https://openreview.net/forum?id=D2lvweC7MC","excerpt":"on strategy for diffusion models using stepwise spectral analysis. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4215–4224. IEEE, 2025. Tianhong Li and Kaiming He. Back to basics: Let denoising generative models denoise. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 36115– 36125, 2026. Sangyun Lee, Hyungjin Chung, Jaehyeon Kim, and Jong Chul Ye. Progressive deblurring of d","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"retraining. In ICLR, 2026. Shanchuan Lin, Anran Wang, and Xiao Yang. Sdxl-lightning: Progressive adversarial diffusion distillation. arXiv preprint arXiv:2402.13929, 2024. Tianhong Li and Kaiming He. Back to basics: Let denoising generative models denoise. In CVPR, 2026. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, pp. 7","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":" Jaskirat Singh, Yunzhong Hou, Zhenchang Xing, Saining Xie, and Liang Zheng. Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers. In ICCV, 2025. Tianhong Li and Kaiming He. Back to basics: Let denoising generative models denoise. In CVPR, 2026. Shanchuan Lin and Xiao Yang. Diffusion model with perceptual loss. arXiv preprint arXiv:2401.00110, 2023. URL https://arxiv.org/abs/2401.00110. Shanchuan Lin, Anran Wang, and Xiao Yan","arxiv_match":false}],"id":"2511.13720","metadata_source":"Cached arXiv API metadata","rank":402},{"title":"DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression","arxiv_id":"2609.19969","year":2026,"url":"https://arxiv.org/abs/2609.19969","citing_submissions":257,"explicit_arxiv_matches":166,"examples":[{"id":"aAcE9xwrev","title":"A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization","forum_url":"https://openreview.net/forum?id=aAcE9xwrev","excerpt":"erger, Anastasia Makarova, Matteo Turchetta, and Andreas Krause. Mixed-variable bayesian optimization. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI’20), pp. 2633–2639, 2020. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026a. URL https://arxiv.org/abs/2606.19348. DeepSeek-AI. Deepseek-v4.1-flash: Pushing the limits of kv cache compression, 2026b. URL https://arxiv.org/abs/2609.19969. A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Springer Berlin, Heidelberg, ","arxiv_match":true},{"id":"cMoqGnXtdv","title":"AIRIS: Benchmarking Social Embodied Intelligence in Egocentric Streaming Videos","forum_url":"https://openreview.net/forum?id=cMoqGnXtdv","excerpt":"visual social interactivity in omni models. arXiv preprint arXiv:2603.16859, 2026. Anyi Xu, B Li, Bangcai Lin, Bing Xue, BingCheng Xian, Bingzheng Xu, Bochao Wu, Bowei Zhang, Boyi Deng, CC Yu, et al. Deepseek-v4. 1-flash: Pushing the limits of kv cache compression, 2026a. Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, and Junyang Lin. Qwen2.5-omni technical repor","arxiv_match":false},{"id":"2LN3rZeePs","title":"Latent Bridge: Asking for Help Without Words","forum_url":"https://openreview.net/forum?id=2LN3rZeePs","excerpt":" Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018. URL https://arxiv.org/abs/1803.05457. DeepSeek-AI. DeepSeek-V3.2: Pushing the frontier of open large language models, 2025. URL https://arxiv.org/abs/2512.02556. DeepSeek-AI. DeepSeek-V4.1-Flash: Pushing the limits of KV cache compression, 2026. URL https://arxiv.org/abs/2609.19969. DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. DeepS","arxiv_match":true}],"id":"2609.19969","metadata_source":"Cached arXiv API metadata","rank":403},{"title":"Diffusion model alignment using direct preference optimization","arxiv_id":"2311.12908","year":2023,"url":"https://arxiv.org/abs/2311.12908","citing_submissions":257,"explicit_arxiv_matches":33,"examples":[{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"dgment. Psychological Review, 34(4):273–286, 1927. Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8228–8238, 2024. Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Hu","arxiv_match":false},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":" Synthesis. arXiv preprint arXiv:2405.14224, 2024. Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion Model Alignment Using Direct Preference Optimization. In CVPR, 2024. Wan Team. Wan2.2: Open and Advanced Large-Scale Video Generative Models. Official code and model release, https://github.com/Wan-Video/Wan2.2, 2025. Wan Team, Ang Wang, Baole Ai, Bin ","arxiv_match":false},{"id":"AivYEuElOk","title":"sCM-RNA: Preference-Aligned Consistency Models for RNA Inverse Folding","forum_url":"https://openreview.net/forum?id=AivYEuElOk","excerpt":"and biomolecular engineering, 12(1):263–286, 2021. Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8228–8238, 2024. Ziwen Wang, Jiajun Fan, Ruihan Guo, Thao Nguyen, Heng Ji, and Ge Liu. Proteinzero: Selfimpr","arxiv_match":false}],"id":"2311.12908","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":404},{"title":"Large language models are not fair evaluators","arxiv_id":"2305.17926","year":2023,"url":"https://arxiv.org/abs/2305.17926","citing_submissions":257,"explicit_arxiv_matches":50,"examples":[{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":"oceedings of Machine Learning Research, pp. 59355–59376. PMLR, 2025. Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, and Zhifang Sui. Large language models are not fair evaluators. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9440–9450. Association for Computational Linguistics, 2024. Xueru Wen, Jie Lou","arxiv_match":false},{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":"eprint arXiv:2303.11366, 2023. URL https://arxiv.org/abs/2303.11366. Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, and Zhifang Sui. Large language models are not fair evaluators. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 2024a. Qian Wang, Zhanzhi Lou, Zhenheng Tang, Nuo Chen, Xuandong Zhao, Wenxuan Zhang, Dawn Song, and Bings","arxiv_match":false},{"id":"w88HAucqab","title":"Evolving Procedural Memory from User Traffic for Agentic Graphic Design","forum_url":"https://openreview.net/forum?id=w88HAucqab","excerpt":".emnlp-main.214. URL https: //aclanthology.org/2025.emnlp-main.214/. Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, and Zhifang Sui. Large language models are not fair evaluators. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9440–9450. Association for Computational Linguistics, 2024b. doi: 10.18653/ v1","arxiv_match":false}],"id":"2305.17926","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":405},{"title":"Diffusion Transformers with Representation Autoencoders","arxiv_id":"2510.11690","year":2025,"url":"https://arxiv.org/abs/2510.11690","citing_submissions":256,"explicit_arxiv_matches":94,"examples":[{"id":"D2lvweC7MC","title":"When Are Diffusion Recovery Clocks Identifiable? A Spectral Measurement Protocol for Teacher-Forced Denoising","forum_url":"https://openreview.net/forum?id=D2lvweC7MC","excerpt":"is easier than you think. In International Conference on Learning Representations, 2025. Yongsheng Yu, Wei Xiong, Weili Nie, Yichen Sheng, Shiqiu Liu, and Jiebo Luo. Pixeldit: Pixel diffusion transformers for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14273–14282, 2026. Boyang Zheng, Nanye Ma, Shengbang Tong, and Saining Xie. Diffusion transformers with representation autoencoders. arXiv preprint arXiv:2510.11690, 2025. Amir Ziashahabi, Baturalp Buyukates, Artan Sheshmani, Yi-Zhuang You, and Salman Avestimehr. ","arxiv_match":true},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"ihao Liu, Zikai Zhou, Yiliang Gu, Yi Wang, Xiaoxiao Xu, and Lin Qu. Qwenimage-vae-2.0 technical report. arXiv preprint arXiv:2605.13565, 2026. Boyang Zheng, Nanye Ma, Shengbang Tong, and Saining Xie. Diffusion transformers with representation autoencoders. In ICLR, 2026a. URL https://openreview.net/forum?id= 0u1LigJaab. Kaiwen Zheng, Yuji Wang, Qianli Ma, Huayu Chen, Jintao Zhang, Yogesh Balaji, Jianfei Chen, Ming-Yu Liu, Jun Zhu, and Qinsheng Zhang. ","arxiv_match":false},{"id":"MpNPoDTf3B","title":"Semantic Fields Are Interfaces: Consumer-Typed Functional Migration for Representation Autoencoders","forum_url":"https://openreview.net/forum?id=MpNPoDTf3B","excerpt":"g Yu, Xiangyu Zhang, and Xiaojuan Qi. Vision foundation models as effective visual tokenizers for autoregressive generation. In NeurIPS, 2025. Boyang Zheng, Nanye Ma, Shengbang Tong, and Saining Xie. Diffusion transformers with representation autoencoders. In ICLR, 2026. Yifei Zhou, Zilu Li, Abhinav Shrivastava, Hengshuang Zhao, Antonio Torralba, Taipeng Tian, and Ser-Nam Lim. BT2 : Backward-compatible training with basis transformation. In ICCV, 2023","arxiv_match":false}],"id":"2510.11690","metadata_source":"Cached arXiv API metadata","rank":406},{"title":"Llava-video: Video instruction tuning with synthetic data","arxiv_id":"2410.02713","year":2024,"url":"https://arxiv.org/abs/2410.02713","citing_submissions":256,"explicit_arxiv_matches":169,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":" Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, and Ziwei Liu. Long context transfer from language to vision. arXiv:2406.16852, 2024a. Shaojie Zhang, Jiahui Yang, Jianqin Yin, Zhenbo Luo, and Jian Luan. Q-frame: Query-aware frame selection and multi-resolution adaptation for video-llms. In ICCV, 2025b. Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv:2410.02713, 2024b. Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar, Zhenheng Yang, and Yang You. Focu","arxiv_match":true},{"id":"w8aZY88Jbh","title":"ReVA: A New Scene-Centric Dataset for Remote Sensing Video Question Answering","forum_url":"https://openreview.net/forum?id=w8aZY88Jbh","excerpt":"hinking with videos: Multimodal tool-augmented reinforcement learning for long video reasoning. arXiv preprint arXiv:2508.04416, 2025. Meimei Zhang, Fang Chen, and Bin Li. Multistep question-driven visual question answering for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 61:1–12, 2023. Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Llava-video: Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024c. Yuanhan Zhang, Bo Li, haotian Liu, Yong jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu","arxiv_match":true},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"ificial intelligence, volume 33, pp. 9127–9134, 2019. Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. In Proceedings of the 2023 conference on empirical methods in natural language processing: system demonstrations, pp. 543–553, 2023. Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Llava-video: Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024. Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning i","arxiv_match":true}],"id":"2410.02713","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":407},{"title":"OGBench: Benchmarking offline goal-conditioned RL","arxiv_id":"2410.20092","year":2024,"url":"https://arxiv.org/abs/2410.20092","citing_submissions":254,"explicit_arxiv_matches":42,"examples":[{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"5713-0661. URL https://proceedings.neurips.cc/ paper files/paper/2025/hash/1c5956164472d6d8123d574aa75cd063-Abstract-Conference.html. Seohong Park, Kevin Frans, Benjamin Eysenbach, and Sergey Levine. OGBench: Benchmarking offline goal-conditioned RL. In International Conference on Learning Representations, 2025. URL https://proceedings.iclr.cc/paper files/paper/2025/hash/ ecd92623ac899357312aaa8915853699-Abstract-Conference.html. Arjun Parthasar","arxiv_match":false},{"id":"RIpEtbedmF","title":"LeWM++: Repairing the Planning Interface of Latent World Models","forum_url":"https://openreview.net/forum?id=RIpEtbedmF","excerpt":"bs/1909.05829. Hoang Nguyen, Xiaohao Xu, and Xiaonan Huang. Latent geometry beyond search: Amortizing planning in world models, 2026. Seohong Park, Kevin Frans, Benjamin Eysenbach, and Sergey Levine. OGBench: Benchmarking offline goal-conditioned RL. In International Conference on Learning Representations, 2025. Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine. Advantage-weighted regression: Simple and scalable off-policy reinforcement","arxiv_match":false},{"id":"vBuP2bGyw9","title":"Post-Training Multimodal Embedding Models towards Physical Dynamics Awareness","forum_url":"https://openreview.net/forum?id=vBuP2bGyw9","excerpt":"n Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Fu, and Zipeng Lin. Open X-Embodiment: Robotic Learning Datasets and RT-X Models. arXiv preprint arXiv:2310.08864, 2023. URL https://arxiv.org/abs/2310.08864. Seohong Park, Kevin Frans, Benjamin Eysenbach, and Sergey Levine. OGBench: Benchmarking Offline Goal-Conditioned RL. arXiv preprint arXiv:2410.20092, 2024. URL https: //arxiv.org/abs/2410.20092. William Peebles and Saining Xie. Scalable Diffusion Models with Transformers. arXiv preprint arXiv","arxiv_match":true}],"id":"2410.20092","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":408},{"title":"V*: Guided visual search as a core mechanism in multimodal LLMs","arxiv_id":"2312.14135","year":2023,"url":"https://arxiv.org/abs/2312.14135","citing_submissions":254,"explicit_arxiv_matches":42,"examples":[{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"ran Liu, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Jiaqi Wang, Xipeng Qiu, and Dahua Lin. Sim-cot: Supervised implicit chain-of-thought. arXiv preprint arXiv:2509.20317, 2025. Penghao Wu and Saining Xie. V?: Guided visual search as a core mechanism in multimodal llms. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13084–13094, 2024. Tianrun Xu, Guanyu Chen, Ye Li, Yuxin Xi, Zeyu Mu, Ruichen Wang, Tianren Zhang, Haichuan ","arxiv_match":false},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"ran Liu, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Jiaqi Wang, Xipeng Qiu, and Dahua Lin. Sim-cot: Supervised implicit chain-of-thought. arXiv preprint arXiv:2509.20317, 2025. Penghao Wu and Saining Xie. V?: Guided visual search as a core mechanism in multimodal llms. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13084–13094, 2024. xAI. Grok-1.5 vision preview. xAI Blog, 2024. Tong Xiao, Xin Xu, Zhenya Huang, Hongyu Gao","arxiv_match":false},{"id":"sBvEgtiF5l","title":"Counterfactual Visual On-Policy Distillation","forum_url":"https://openreview.net/forum?id=sBvEgtiF5l","excerpt":"for high-resolution image perception in multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 7907–7915, 2025. Penghao Wu and Saining Xie. V?: Guided visual search as a core mechanism in multimodal llms. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13084–13094, 2024. Shizhe Xiang, Ke An, Wenlong Yu, Yue Liu, Jian Luan, Pei Fu, and Qilong Wang. Teaching th","arxiv_match":false}],"id":"2312.14135","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":409},{"title":"Docvqa: A dataset for vqa on document images","arxiv_id":"2007.00398","year":2020,"url":"https://arxiv.org/abs/2007.00398","citing_submissions":252,"explicit_arxiv_matches":37,"examples":[{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"7(12), December 2024c. ISSN 1869-1919. doi: 10.1007/s11432-024-4235-6. URL http://dx.doi.org/10.1007/ s11432-024-4235-6. Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning, 2022. URL https: //arxiv.org/abs/2203.10244. Minesh Mathew, Dimosthenis Karatzas, and C. V. Jawahar. DocVQA: A dataset for VQA on document images, 2020. URL https://arxiv.org/abs/2007.00398. Lingchen Meng, Jianwei Yang, Rui Tian, Xiyang Dai, Zuxuan Wu, Jianfeng Gao, and Yu-Gang Jiang. Dee","arxiv_match":true},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"about charts with visual and logical reasoning. In Findings of the Association for Computational Linguistics: ACL 2022, pp. 2263–2279, May 2022. Minesh Mathew, Dimosthenis Karatzas, and C.V. Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2200–2209, January 2021. Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny,","arxiv_match":false},{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"ering about charts with visual and logical reasoning. In Findings of the association for computational linguistics: ACL 2022, pp. 2263–2279, 2022. Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2199– 2208. IEEE, 2021. Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawahar. I","arxiv_match":false}],"id":"2007.00398","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":410},{"title":"Eliciting latent predictions from transformers with the tuned lens","arxiv_id":"2303.08112","year":2023,"url":"https://arxiv.org/abs/2303.08112","citing_submissions":251,"explicit_arxiv_matches":228,"examples":[{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":"xiv.org/ abs/2605.19376. Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. Deep equilibrium models. In Advances in Neural Information Processing Systems (NeurIPS), 2019. URL https://arxiv.org/abs/1909. 01377. Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. Eliciting latent predictions from transformers with the tuned lens. arXiv preprint arXiv:2303.08112, 2023. URL https://arxiv.org/abs/2303.08112. Hugh Blayney, Álvaro Arroyo, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Michael M.","arxiv_match":true},{"id":"fNrQjgb4Ch","title":"DV-Lens: Revealing the Functional Organization of Language Model Parameters","forum_url":"https://openreview.net/forum?id=fNrQjgb4Ch","excerpt":", Jonathan Tow, Dakota Mahan, Duy Phung, Maksym Zhuravinskyi, Reshinth Adithyan, James Baicoianu, Ben Brooks, Nathan Cooper, Ashish Datta, et al. Stable LM 2 1.6B technical report. arXiv preprint arXiv:2402.17834, 2024. URL https://arxiv.org/abs/ 2402.17834. Nora Belrose, Igor Ostrovsky, Lev McKinney, Zach Furman, Logan Smith, Danny Halawi, Stella Biderman, and Jacob Steinhardt. Eliciting latent predictions from transformers with the tuned lens. arXiv preprint arXiv:2303.08112, 2023. DeepSeek-AI. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learn","arxiv_match":true},{"id":"QLMHtA7hoi","title":"Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection","forum_url":"https://openreview.net/forum?id=QLMHtA7hoi","excerpt":"maly detection. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 20111–20121. IEEE, 2022. [3] Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics, 48(1):207–219, 2022. [4] Nora Belrose, Igor Ostrovsky, Lev McKinney, Zach Furman, Logan Smith, Danny Halawi, Stella Biderman, and Jacob Steinhardt. Eliciting latent predictions from transformers with the tuned lens. arXiv preprint arXiv:2303.08112, 2023. [5] Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge ","arxiv_match":true}],"id":"2303.08112","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":411},{"title":"Jailbreaking black box large language models in twenty queries","arxiv_id":"2310.08419","year":2023,"url":"https://arxiv.org/abs/2310.08419","citing_submissions":251,"explicit_arxiv_matches":84,"examples":[{"id":"mVJzfFoRun","title":"Checking Leakage Witnesses versus Certifying Bounded Non-Leakage","forum_url":"https://openreview.net/forum?id=mVJzfFoRun","excerpt":"e complexity of quantitative information flow problems. In IEEE Computer Security Foundations Symposium, pp. 205–217, 2011. doi: 10.1109/CSF.2011.21. Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong. Jailbreaking black box large language models in twenty queries. In 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), pp. 23–42, 2025. doi: 10.1109/SaTML64287.2025.00010. URL https://arxiv.org/abs/2310.08419. William Cook, Collette R. Coullard, and György Turán. On the complexity of cutting-plane proofs.","arxiv_match":true},{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":"al Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=aJUuere4fM. Blake Bullwinkel, Eugenia Kim, Amanda Minnich, and Mark Russinovich. Learning to attack and defend: Adaptive red teaming of language models via grpo, 2026. URL https://arxiv.org/ abs/2606.09701. Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. Jailbreaking black box large language models in twenty queries. arXiv preprint arXiv:2310.08419, 2023. Google DeepMind. Gemini 3 flash - model card, 2026. URL https://deepmind.google/ models/mode","arxiv_match":true},{"id":"IKUjfAknOl","title":"Agent Hacks Agents: Autoresearch Discovers Vulnerabilities in Production Agents","forum_url":"https://openreview.net/forum?id=IKUjfAknOl","excerpt":"usal attribution and counterfactual repair for llm agent failures. arXiv preprint arXiv:2605.25338, 2026. Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. Jailbreaking black box large language models in twenty queries. In 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), pp. 23–42. IEEE, 2025. Zhaorun Chen, Xun Liu, Haibo Tong, Chengquan Guo, Yuzhou Nie, Jiawei Zhang, Mintong Kang, Chejian X","arxiv_match":false}],"id":"2310.08419","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":412},{"title":"Motus: A Unified Latent Action World Model","arxiv_id":"2512.13030","year":2025,"url":"https://arxiv.org/abs/2512.13030","citing_submissions":251,"explicit_arxiv_matches":94,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"wen Yu, Fei Zhang, Hang Zhang, Xi Zhang, Bo Zheng, Humen Zhong, Jingren Zhou, Fan Zhou, Jing Zhou, Yuanzhi Zhu, and Ke Zhu. Qwen3-vl technical report. arXiv preprint arXiv:2511.21631, 2025. Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, Chendong Xiang, Yinze Rong, Hongyan Zhao, Hanyu Liu, Zhizhong Su, Lei Ma, Hang Su, and Jun Zhu. Motus: A unified latent action world model, 2025. URL https: //arxiv.org/abs/2512.13030. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, La","arxiv_match":true},{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"d Jeff Clune. Video PreTraining (VPT): Learning to act by watching unlabeled online videos. In NeurIPS, 2022. Homanga Bharadhwaj, Roozbeh Mottaghi, Abhinav Gupta, and Shubham Tulsiani. Track2Act: Predicting point tracks from internet videos enables generalizable robot manipulation. In ECCV, 2024. Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, et al. Motus: A unified latent action world model. arXiv:2512.13030, 2025. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fus","arxiv_match":true},{"id":"FZ7Aq4uPre","title":"UniWAM: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision","forum_url":"https://openreview.net/forum?id=FZ7Aq4uPre","excerpt":" Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, et al. Motus: A unified latent action world model. arXiv preprint arXiv:2512.13030, 2025. Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu F","arxiv_match":true}],"id":"2512.13030","metadata_source":"Cached arXiv API metadata","rank":413},{"title":"Building normalizing flows with stochastic interpolants","arxiv_id":"2209.15571","year":2022,"url":"https://arxiv.org/abs/2209.15571","citing_submissions":250,"explicit_arxiv_matches":80,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"al, Md Shahriar Rahim Siddiqui, Simon Ghyselincks, Moshe Eliasof, and Eldad Haber. Preconditioned flow matching. arXiv preprint arXiv:2603.02337, 2026. Michael Samuel Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. In ICLR, 2023. Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. QuaRot: Outlier-free 4-bit inferen","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"cht. MMDiff: Extending diffusion transformers for multi-modal generation. arXiv preprint arXiv:2606.16673, 2026. URL https: //arxiv.org/abs/2606.16673. Michael Samuel Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. In ICLR, 2023. Anonymous. Learning anisotropic prediction targets for flow models. Under review at ICLR 2027, 2026. Mustafa Ayazoglu. Extremely lightweight quantization robust real-time single-image","arxiv_match":false},{"id":"tlahLcI2zN","title":"Beyond Category Assignments: Learning Sample-Prototype Dynamics for Generalized Category Discovery","forum_url":"https://openreview.net/forum?id=tlahLcI2zN","excerpt":" Michael Samuel Albergo and Eric Vanden-Eijnden. Building normalizing flows with stochastic interpolants. In ICLR, 2023. Yuwei Bian, Shidong Wang, and Haofeng Zhang. CURE: Consistency-under-unified semantic regularization for generalized category discovery. In ICML, 2026. Lea Bogensperger, Dominik Narnh","arxiv_match":false}],"id":"2209.15571","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":414},{"title":"Quest: Query-aware sparsity for efficient long-context llm inference","arxiv_id":"2406.10774","year":2024,"url":"https://arxiv.org/abs/2406.10774","citing_submissions":250,"explicit_arxiv_matches":83,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"ffusion transformers. arXiv preprint arXiv:2505.14687, 2025. Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, and Tri Dao. Flashattention-3: Fast and accurate attention with asynchrony and low-precision. Advances in Neural Information Processing Systems, 37:68658–68685, 2024. Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, and Song Han. Quest: Query-aware sparsity for efficient long-context llm inference. arXiv preprint arXiv:2406.10774, 2024. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łuka","arxiv_match":true},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":". Tensor cache: Eviction-conditioned associative memory for transformers, 2026. URL https://arxiv.org/ abs/2605.22884. Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, and Song Han. QUEST: Queryaware sparsity for efficient long-context LLM inference. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=KzACYw0MTV. Haolin Tian, Yuzhe Liu, and Tonghan Wang. Every cache entry earns its place: Global","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"h American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4149–4158, 2019. Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, and Song Han. Quest: Queryaware sparsity for efficient long-context LLM inference. In Forty-first International Conference on Machine Learning, 2024. Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan. Quadtree attention for vision transformers. In The Tenth International Conferenc","arxiv_match":false}],"id":"2406.10774","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":415},{"title":"Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks","arxiv_id":"1506.03099","year":2015,"url":"https://arxiv.org/abs/1506.03099","citing_submissions":250,"explicit_arxiv_matches":36,"examples":[{"id":"bI2MFCxHdB","title":"Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion","forum_url":"https://openreview.net/forum?id=bI2MFCxHdB","excerpt":" Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. Advances in neural information processing systems, 28, 2015. Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti,","arxiv_match":false},{"id":"BBn3Q6dIzT","title":"History Dropout for Long-Horizon Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=BBn3Q6dIzT","excerpt":" Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems, volume 28, 2015. Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents:","arxiv_match":false},{"id":"0AaiHNGPSg","title":"TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction","forum_url":"https://openreview.net/forum?id=0AaiHNGPSg","excerpt":" Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. Advances in Neural Information Processing Systems, 28, 2015. Yohann Cabon, Lucas Stoffl, Leonid Antsfeld, Gabriela Csurka, Boris Chidlovskii, Jerome Revaud, and Vincent Leroy. Must3r: Multi-view net","arxiv_match":false}],"id":"1506.03099","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":416},{"title":"Self-rewarding language models","arxiv_id":"2401.10020","year":2024,"url":"https://arxiv.org/abs/2401.10020","citing_submissions":250,"explicit_arxiv_matches":111,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"descriptions. Transactions of the Association for Computational Linguistics, 2:67–78, 2014. Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. Self-rewarding language models. In Proceedings of the 41st International Conference on Machine Learning, pp. 57905–57923, 2024. Xingshan Zeng, Zishan Xu, Boju Zhang, Yuzhou Wu, Lingzhi Wang, Jianghao Lin, Liangyou Li, Yasheng Wang","arxiv_match":false},{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"/arxiv.org/abs/2505.09388. Ori Yoran, Samuel Joseph Amouyal, Chaitanya Malaviya, Ben Bogin, Ofir Press, and Jonathan Berant. Assistantbench: Can web agents solve realistic and time-consuming tasks? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 8938–8968, 2024. Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. Self-rewarding language models. arXiv preprint arXiv:2401.10020, 2024. Daoguang Zan, Zhirong Huang, Wei Liu, Hanwu Chen, Shulin Xin, Linhao Zhang, Qi Liu, Li Aoyan","arxiv_match":true},{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":" for contentrich text-to-image generation. arXiv preprint arXiv:2206.10789, 2(3):5, 2022. Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason E Weston. Self-rewarding language models. In Forty-first International Conference on Machine Learning, 2024. Huaisheng Zhu, Teng Xiao, and Vasant G Honavar. Dspo: Direct score preference optimization for diffusion model alignment. In The Th","arxiv_match":false}],"id":"2401.10020","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":417},{"title":"Sgdr: Stochastic gradient descent with warm restarts","arxiv_id":"1608.03983","year":2016,"url":"https://arxiv.org/abs/1608.03983","citing_submissions":250,"explicit_arxiv_matches":87,"examples":[{"id":"uwSMvBYqGq","title":"Auditing Optimization and Probabilistic Reliability in Frozen Vision-Language Representation Intervention","forum_url":"https://openreview.net/forum?id=uwSMvBYqGq","excerpt":"e/peft, 2022. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In ACL, pp. 311–318, 2002. Ilya Loshchilov and Frank Hutter. SGDR: stochastic gradient descent with warm restarts. In ICLR, 2017. André F. T. Martins and Ramón Fernandez Astudillo. From softmax to sparsemax: A sparse model of attention and multi-label classification. In ICML, pp. 1614–1623, 2016. Vinay Venkate","arxiv_match":false},{"id":"U9lsoJxNae","title":"Beyond Confidence? Auditing Routing Entropy in Attention-Residual Transformers","forum_url":"https://openreview.net/forum?id=U9lsoJxNae","excerpt":"ransformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022, 2021. Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. In International Conference on Learning Representations, 2017. Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii K","arxiv_match":false},{"id":"ovv7oYCkNk","title":"Real-Time Action Chunking for Relative-Action Robot Policies","forum_url":"https://openreview.net/forum?id=ovv7oYCkNk","excerpt":"tumi-100k: Advancing data-driven robotic manipulation with a large-scale umi-style dataset. arXiv preprint arXiv:2510.08022, 2025. Songming Liu, Bangguo Li, Kai Ma, Lingxuan Wu, Hengkai Tan, Xiao Ouyang, Hang Su, and Jun Zhu. Rdt2: Exploring the scaling limit of umi data towards zero-shot cross-embodiment generalization. arXiv preprint arXiv:2602.03310, 2026. Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. Ilya Loshchilov and Frank Hutter. arXiv:1711.05101, 2017. Decoupled weight decay regularizat","arxiv_match":true}],"id":"1608.03983","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":418},{"title":"Visionzip: Longer is better but not necessary in vision language models","arxiv_id":"2412.04467","year":2024,"url":"https://arxiv.org/abs/2412.04467","citing_submissions":250,"explicit_arxiv_matches":30,"examples":[{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":"In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 24939–24949. IEEE, 2025b. Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer is better but not necessary in vision language models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19792–19802. IEEE, 2025c. Mingfeng Yuan, Hao Zhang, Mahan Mohammadi, Runhao Li, Jinjun Shan, and Steven L Waslander","arxiv_match":false},{"id":"Xdik5HW8VD","title":"WaLKPruner: Cognitively Inspired Visual Token Pruning for Spatial Understanding","forum_url":"https://openreview.net/forum?id=Xdik5HW8VD","excerpt":"ll spaces. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 10632–10643, 2025a. Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer is better but not necessary in vision language models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19792–19802. IEEE, 2025b. Shusheng Yang, Jihan Yang, Pinzhi Huang, Ellis Brown, Zihao Yang, Yue Yu, Shengbang Tong,","arxiv_match":false},{"id":"iBCBaGASs6","title":"HEAT: Hierarchical Elastic Adaptive Token Sparsification via Depth Scaling","forum_url":"https://openreview.net/forum?id=iBCBaGASs6","excerpt":"large vision-language models via pyramid visual redundancy reduction. arXiv preprint arXiv:2410.17247, 2024. Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer is better but not necessary in vision language models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19792–19802, 2025. Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou. Fit and prune: Fast and training-free visual token pruning f","arxiv_match":false}],"id":"2412.04467","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":419},{"title":"Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models","arxiv_id":"2503.09573","year":2025,"url":"https://arxiv.org/abs/2503.09573","citing_submissions":249,"explicit_arxiv_matches":82,"examples":[{"id":"Zf68SX9abN","title":"S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation","forum_url":"https://openreview.net/forum?id=Zf68SX9abN","excerpt":" Anthropic. Claude Fable 5 and Claude Mythos 5, 2026. URL https://www.anthropic. com/news/claude-fable-5-mythos-5. Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, and Volodymyr Kuleshov. Block diffusion: Interpolating between autoregressive and diffusion language models. In International Conference on Learning Representations, 2025. URL https://arxiv.org/abs/2503.09573. Hritik Bansal, Clark Peng, Yonatan Bitton, Roman Goldenberg, Aditya Grover, and Kai-Wei Chang. Vid","arxiv_match":true},{"id":"UtSpCnqHk8","title":"Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving","forum_url":"https://openreview.net/forum?id=UtSpCnqHk8","excerpt":" Marianne Arriola, Aaron Gokaslan, Justin Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sahoo, and Volodymyr Kuleshov. Block diffusion: Interpolating between autoregressive and diffusion language models. In International Conference on Learning Representations, volume 2025, pp. 50726–50753, 2025. Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising","arxiv_match":false},{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"ce on Machine Learning, 2026. URL https://arxiv.org/abs/2607. 01775. Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, and Volodymyr Kuleshov. Block diffusion: Interpolating between autoregressive and diffusion language models. In International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=tyEyYT267x. Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. S","arxiv_match":false}],"id":"2503.09573","metadata_source":"Cached arXiv API metadata","rank":420},{"title":"The curious case of neural text degeneration","arxiv_id":"1904.09751","year":2019,"url":"https://arxiv.org/abs/1904.09751","citing_submissions":248,"explicit_arxiv_matches":82,"examples":[{"id":"mVJzfFoRun","title":"Checking Leakage Witnesses versus Certifying Bounded Non-Leakage","forum_url":"https://openreview.net/forum?id=mVJzfFoRun","excerpt":"e understanding. In International Conference on Learning Representations (ICLR), 2021. URL https://openreview.net/forum?id= d7KBjmI3GmQ. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In International Conference on Learning Representations (ICLR), 2020. URL https://openreview.net/forum?id=rygGQyrFvH. Alexey Ignatiev, António Morgado, and João Marques-Silva. PySAT: A Python tool","arxiv_match":false},{"id":"MfgDKKxrns","title":"DistDNA: Language Model Fingerprinting under Stochastic Decoding","forum_url":"https://openreview.net/forum?id=MfgDKKxrns","excerpt":"a. A kernel two-sample test. Journal of Machine Learning Research, 13(25):723–773, 2012. URL http://jmlr.org/papers/v13/gretton12a.html. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In International Conference on Learning Representations, 2020. URL https: //openreview.net/forum?id=rygGQyrFvH. Pingyi Hu, Xiaofan Bai, Xiaojing Ma, Chaoxiang He, Dongmei Zhang, and Bin Benjamin Zhu","arxiv_match":false},{"id":"f7VURhhLEL","title":"The Cost Of Knowing: A Resource-Aware Protocol For Benchmarking Hallucination Beyond Static Leaderboards","forum_url":"https://openreview.net/forum?id=f7VURhhLEL","excerpt":"rching and revising what language models say, using language models. arXiv preprint arXiv:2210.08726, 2022. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang. Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680, 2024. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751, 2019. Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, S","arxiv_match":true}],"id":"1904.09751","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":421},{"title":"PaLM-E: An embodied multimodal language model","arxiv_id":"2303.03378","year":2023,"url":"https://arxiv.org/abs/2303.03378","citing_submissions":246,"explicit_arxiv_matches":99,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"ianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Pete Florence. PaLM-E: An embodied multimodal language model. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 8469–8488, 2023. URL https://proceedings.mlr.press/v202/driess23a","arxiv_match":false},{"id":"ylrbHy6YvK","title":"KI-VLA: Kinematics-Informed Action Decoding for Flow-Matching Robot Policies","forum_url":"https://openreview.net/forum?id=ylrbHy6YvK","excerpt":"Robotics Research, 44(10-11):1684–1704, 2025. Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. PaLM-E: An embodied multimodal language model. In International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 8469–8488, 2023. Tamar Flash and Neville Hogan. The coordination of arm movements: an exp","arxiv_match":false},{"id":"f7uO3rAyIZ","title":"STAR: Practical Backdoor Attacks Against Vision-Language-Action Models via Semantic Triggers","forum_url":"https://openreview.net/forum?id=f7uO3rAyIZ","excerpt":"al Conference on Computer Vision, pp. 11966–11976, 2021. Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, et al. PaLM-E: An embodied multimodal language model. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 8469–8488. PMLR, 2023. Tianyu Gu, Brendan Dolan-Gavitt, and Siddh","arxiv_match":false}],"id":"2303.03378","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":422},{"title":"Qwen3.5-Omni Technical Report","arxiv_id":"2604.15804","year":2026,"url":"https://arxiv.org/abs/2604.15804","citing_submissions":246,"explicit_arxiv_matches":224,"examples":[{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":". Sculley, Slav Petrov, Noah Fiedel, Noam Shazeer, Oriol Vinyals, Jeff Dean, Demis Hassabis, Koray Kavukcuoglu, Clement Farabet, Elena Buchatskaya, Jean-Baptiste Alayrac, Rohan Anil, Dmitry, Lepikhin, Sebastian Borgeaud, Olivier Bachem, Armand Joulin, Alek Andreev, Cassidy Hardin, Robert Dadashi, and Léonard Hussenot. Gemma 3 technical report, 2025. URL https://arxiv.org/abs/2503.19786. Qwen Team. Qwen3.5-omni technical report, 2026. URL https://arxiv.org/abs/2604. 15804. Jintao Tong, Jiaqi Gu, Yujing Lou, Lubin Fan, Yixiong Zou, Yue Wu, Jieping Ye, and Ruixuan Li. Ske","arxiv_match":true},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"ising in representation space, 2026. URL https://arxiv.org/abs/2606.14700. Wujian Peng, Lingchen Meng, Yuxuan Cai, Xianwei Zhuang, Yuhuan Yang, Rongyao Fang, Chenfei Wu, Junyang Lin, Zuxuan Wu, and Shuai Bai. Unified multimodal autoregressive modeling with shared context — visual tokenizer is key to unification. In ICML, 2026. Qwen3-vl. Qwen3-vl technical report. arXiv preprint arXiv:2511.21631, 2025. Qwen3.5-omni. Qwen3.5-omni technical report. arXiv preprint arXiv:2604.15804, 2026. Ali Razavi, Aaron van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images w","arxiv_match":true},{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":"eter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, et al. A strongreject for empty jailbreaks. Advances in Neural Information Processing Systems, 37:125416–125440, 2024. Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, Ian Ballantyne, Cormac Brick, Victor Cărbune, Michelle Casbon, et al. Gemma 4 technical report. arXiv preprint arXiv:2607.02770, 2026. Qwen Team. Qwen3. 5-omni technical report. arXiv preprint arXiv:2604.15804, 2026. Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. The i","arxiv_match":true}],"id":"2604.15804","metadata_source":"Cached arXiv API metadata","rank":423},{"title":"Cosmos 3: Omnimodal World Models for Physical AI","arxiv_id":"2606.02800","year":2026,"url":"https://arxiv.org/abs/2606.02800","citing_submissions":245,"explicit_arxiv_matches":213,"examples":[{"id":"Zf68SX9abN","title":"S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation","forum_url":"https://openreview.net/forum?id=Zf68SX9abN","excerpt":"al Conference on Machine Learning, 2026. URL https://research.nvidia.com/labs/ genair/flex-forcing/. Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, and Robert Geirhos. Do generative video models understand physical principles? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 948–958, 2026. NVIDIA. Cosmos 3: Omnimodal world models for physical ai. arXiv preprint arXiv:2606.02800, 2026. URL https://arxiv.org/abs/2606.02800. Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, and Satinder Singh. Action-conditional vi","arxiv_match":true},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":" Pengfei Wan, and Shao-Lun Huang. Beyond the Golden Data: Resolving the Motion-Vision Quality Dilemma via Timestep Selective Training. In CVPR, 2026. Shuailei Ma, Jiaqi Liao, Xinyang Wang, Jingjing Wang, Chaoran Feng, Zijing Hu, Chong Bao, Zichen Xi, Yuqi Gan, Weisen Wang, et al. Scaling mixture-of-experts video pretraining for embodied intelligence. arXiv preprint arXiv:2607.07675, 2026. NVIDIA. Cosmos 3: Omnimodal World Models for Physical AI. arXiv preprint arXiv:2606.02800, 2026. William Peebles and Saining Xie. Scalable Diffusion Models with Transformers. In ICCV, 2023.","arxiv_match":true},{"id":"fKq8qGzBwD","title":"Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning","forum_url":"https://openreview.net/forum?id=fKq8qGzBwD","excerpt":"ang, Hans Yang, Xiaodong Yang, Xuning Yang, Yichu Yang, Yurong You, Zhiding Yu, Hao Yuan, Simon Yuen, Xiaohui Zeng, Pengcuo Zeren, Cindy Zha, Haotian Zhang, Jenny Zhang, Jing Zhang, Liangkai Zhang, Paris Zhang, Shun Zhang, Xuanmeng Zhang, Zhizheng Zhang, Ann Zhao, Yilin Zhao, Yuliya Zhautouskaya, Charles Zhou, Fengzhe Zhou, Shilin Zhu, Yuke Zhu, Dima Zhylko, and Artur Zolkowski. Cosmos 3: Omnimodal world models for physical ai, 2026. URL https://arxiv.org/abs/2606.02800. Chaoyi Pan, Giri Anantharaman, Nai-Chieh Huang, Claire Jin, Daniel Pfrommer, Chenyang Yuan, Frank ","arxiv_match":true}],"id":"2606.02800","metadata_source":"Cached arXiv API metadata","rank":424},{"title":"Explaining and harnessing adversarial examples","arxiv_id":"1412.6572","year":2014,"url":"https://arxiv.org/abs/1412.6572","citing_submissions":244,"explicit_arxiv_matches":132,"examples":[{"id":"KY19PuvbEj","title":"Fragility-Aware Noise Training for Robust Split Computing over Noisy Channels","forum_url":"https://openreview.net/forum?id=KY19PuvbEj","excerpt":"Aldo Faisal, Luc PJ Selen, and Daniel M Wolpert. Noise in the nervous system. Nature reviews neuroscience, 9(4):292–303, 2008. Angélica Pérez Fornos, Raymond Van de Berg, Jörg Sommerhalder, and Nils Guinand. Designing artificial senses: steps from physiology to clinical implementation. Swiss Medical Weekly, 149 (2122):w20061–w20061, 2019. Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014. Yuntao Hao, Nan Ding, Weiguo Xia, Hongwei Ge, and Li Xu. Dnn partitioning for cooperative in","arxiv_match":true},{"id":"lHQyU7Hywl","title":"When and Why Adversarial Training Improves Time-Series Forecasting","forum_url":"https://openreview.net/forum?id=lHQyU7Hywl","excerpt":"and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, 2019. Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In International Conference on Learning Representations (ICLR), 2015. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In European Conference on Co","arxiv_match":false},{"id":"jXc2rEbQ41","title":"Rethinking Image Immunization for Transferable and Purification-Robust Protection","forum_url":"https://openreview.net/forum?id=jXc2rEbQ41","excerpt":"nd R Venkatesh Babu. Fda: Feature disruptive attack. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019. Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In Proceedings of the International Conference on Learning Representations (ICLR), 2015. Dongnan Gui, Xun Guo, Wengang Zhou, and Yan Lu. I2vguard: Safeguarding images against misuse in diffusion-bas","arxiv_match":false}],"id":"1412.6572","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":425},{"title":"Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning","arxiv_id":"2506.01939","year":2025,"url":"https://arxiv.org/abs/2506.01939","citing_submissions":243,"explicit_arxiv_matches":106,"examples":[{"id":"9XqqxYouLj","title":"Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning","forum_url":"https://openreview.net/forum?id=9XqqxYouLj","excerpt":" Yu, Chang Gao, Chujie Zheng, Shixuan Liu, Rui Lu, Kai Dang, Xiong-Hui Chen, Jianxin Yang, Zhenru Zhang, Yuqiong Liu, An Yang, Andrew Zhao, Yang Yue, Shiji Song, Bowen Yu, Gao Huang, and Junyang Lin. Beyond the 80/20 rule: High-entropy minority tokens drive effective reinforcement learning for LLM reasoning. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, NeurIPS 2025, San Diego, CA, USA, December 2-7, 2025 / Mexico City, Mexico, ","arxiv_match":false},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":" Yu, Chang Gao, Chujie Zheng, Shixuan Liu, Rui Lu, Kai Dang, Xiong-Hui Chen, Jianxin Yang, Zhenru Zhang, Yuqiong Liu, An Yang, Andrew Zhao, Yang Yue, Shiji Song, Bowen Yu, Gao Huang, and Junyang Lin. Beyond the 80/20 rule: High-entropy minority tokens drive effective reinforcement learning for LLM reasoning. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, NeurIPS 2025, San Diego, CA, USA, December 2-7, 2025 / Mexico City, Mexico, ","arxiv_match":false},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":" Yu, Chang Gao, Chujie Zheng, Shixuan Liu, Rui Lu, Kai Dang, Xiong-Hui Chen, Jianxin Yang, Zhenru Zhang, Yuqiong Liu, An Yang, Andrew Zhao, Yang Yue, Shiji Song, Bowen Yu, Gao Huang, and Junyang Lin. Beyond the 80/20 rule: High-entropy minority tokens drive effective reinforcement learning for LLM reasoning. In Advances in Neural Information Processing Systems, volume 38, 2025b. doi: 10.52202/085713-3850. URL https://proceedings.neurips.cc/paper_files/paper/2025/hash/ a797c2d2e0c1fdabf4d1ab8cd0b465c6-Ab","arxiv_match":false}],"id":"2506.01939","metadata_source":"Cached arXiv API metadata","rank":426},{"title":"C-pack: Packed resources for general chinese embeddings","arxiv_id":"2309.07597","year":2023,"url":"https://arxiv.org/abs/2309.07597","citing_submissions":243,"explicit_arxiv_matches":84,"examples":[{"id":"MdyFYfABEK","title":"How Evaluation Choices Affect MAUVE Scores and Generator Rankings","forum_url":"https://openreview.net/forum?id=MdyFYfABEK","excerpt":"njie Li, and Roberto Navigli (eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2192– 2198, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021. findings-acl.193. URL https://aclanthology.org/2021.findings-acl.193/. Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. C-pack: Packed resources for general chinese embeddings, 2024. URL https://arxiv.org/abs/ 2309.07597. An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Da","arxiv_match":true},{"id":"vvAAVy99dI","title":"Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking","forum_url":"https://openreview.net/forum?id=vvAAVy99dI","excerpt":": The covid-19 open research dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, 2020. Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. C-pack: Packed resources for general chinese embeddings. In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp. 641–649, 2024a. Shitao Xiao, Zheng Liu, Peitian Zhang, and Xingrun Xing. Lm-co","arxiv_match":false},{"id":"RCTq1qLhoj","title":"Can Deep Research Agents Retrieve and Organize? Evaluating the Synthesis Gap with Expert Taxonomies","forum_url":"https://openreview.net/forum?id=RCTq1qLhoj","excerpt":", 2025. xAI. Grok. https://grok.com/, 2025. xAI. Grok model documentation. https://docs.x.ai/docs/models, 2026. Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. C-pack: Packed resources for general chinese embeddings. In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp. 641–649, 2024. Xiangchao Yan, Shiyang Feng, Jiakang Yuan, Renqiu Xia, Bin Wang","arxiv_match":false}],"id":"2309.07597","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":427},{"title":"Jailbreakbench: An open robustness benchmark for jailbreaking large language models","arxiv_id":"2404.01318","year":2024,"url":"https://arxiv.org/abs/2404.01318","citing_submissions":243,"explicit_arxiv_matches":43,"examples":[{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":"3628–3646, 2024. Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J Pappas, Florian Tramer, et al. Jailbreakbench: An open robustness benchmark for jailbreaking large language models. Advances in Neural Information Processing Systems, 37:55005–55029, 2024. Runjin Chen, Andy Arditi, Henry Sleight, Owain Evans, and Jack Lindsey. Persona vectors: Monitoring and controlling character","arxiv_match":false},{"id":"zEvJmHCOUL","title":"SteerProbe: Learning to Bypass Safety Steering in Vision–Language Models","forum_url":"https://openreview.net/forum?id=zEvJmHCOUL","excerpt":".13923, 2025b. Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, et al. JailbreakBench: An open robustness benchmark for jailbreaking large language models. In Advances in Neural Information Processing Systems, volume 37, 2024. Renmiao Chen, Shiyao Cui, Xuancheng Huang, Chengwei Pan, Victor Shea-Jay Huang, Qinglin Zhang, Xuan Ouyang, Zhexin Zhang, Hongn","arxiv_match":false},{"id":"ygrx8bN3MO","title":"Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning","forum_url":"https://openreview.net/forum?id=ygrx8bN3MO","excerpt":"403.05030, 2024. Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J Pappas, Florian Tramer, et al. Jailbreakbench: An open robustness benchmark for jailbreaking large language models. Advances in Neural Information Processing Systems, 37:55005–55029, 2024. Junqi Chen, Sirui Chen, and Chaochao Lu. Can post-training transform llms into causal reasoners? In Findings of the Associati","arxiv_match":false}],"id":"2404.01318","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":428},{"title":"What disease does this patient have? a large-scale open domain question answering dataset from medical exams","arxiv_id":"2009.13081","year":2020,"url":"https://arxiv.org/abs/2009.13081","citing_submissions":243,"explicit_arxiv_matches":49,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":"els cannot self-correct reasoning yet. In The Twelfth International Conference on Learning Representations, 2024. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. Applied Sciences, 11(14):6421, 2021. Lars Benedikt Kaesberg, Jonas Becker, Jan Philip Wahle, Terry Ruas, and Bela Gipp. Voting or consensus? decision-making in multi-agent debate. In Findings of the","arxiv_match":false},{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"ep networks, 2018. URL https://arxiv.org/abs/1808.07172. Y. Jiang, H. Kang, J. Liu, and D. Xu. On the convergence of decentralized stochastic gradient descent with biased gradients. IEEE Transactions on Signal Processing, 73:549–558, 2025. doi: 10.1109/TSP.2025.3531356. D. Jin, E. Pan, N. Oufattole, W.-H. Weng, H. Fang, and P. Szolovits. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. arXiv preprint arXiv:2009.13081, 2020. K. Jordan, Y. Jin, V. Boza, Y. Jiacheng, F. Cesista, L. Newhouse, and J. Bernstein. Muon: An","arxiv_match":true},{"id":"2te1UDMg3T","title":"MedRouter: Demystifying Knowledge Differences Across Medical LLMs for Routing-Based Reasoning","forum_url":"https://openreview.net/forum?id=2te1UDMg3T","excerpt":"1610–21628, 2025b. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han. Search-r1: Training llms to reason and leverage search engines with reinforcement learning. arXiv preprint arXiv:2503.09516, 2025. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. arXiv preprint arXiv:2009.13081, 2020. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. Pubmedqa: A dataset ","arxiv_match":true}],"id":"2009.13081","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":429},{"title":"Discovering latent knowledge in language models without supervision","arxiv_id":"2212.03827","year":2022,"url":"https://arxiv.org/abs/2212.03827","citing_submissions":242,"explicit_arxiv_matches":97,"examples":[{"id":"QLMHtA7hoi","title":"Representation--Behavior Alignment for Explainable Weakly-Supervised Video Anomaly Detection","forum_url":"https://openreview.net/forum?id=QLMHtA7hoi","excerpt":"omises, shortcomings, and advances. Computational Linguistics, 48(1):207–219, 2022. [4] Nora Belrose, Igor Ostrovsky, Lev McKinney, Zach Furman, Logan Smith, Danny Halawi, Stella Biderman, and Jacob Steinhardt. Eliciting latent predictions from transformers with the tuned lens. arXiv preprint arXiv:2303.08112, 2023. [5] Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. arXiv preprint arXiv:2212.03827, 2022. [6] Zhaolin Cai, Fan Li, Ziwei Zheng, Haixia Bi, and Lijun He. Headhunt-vad: Hunting robust ","arxiv_match":true},{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"e models with dictionary learning. Transformer Circuits Thread, 2023. https://transformer-circuits.pub/2023/monosemanticfeatures/index.html. Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. In ICLR, 2023. Tri Cao, Bennett Lim, Yue Liu, Yuan Sui, Yuexin Li, Shumin Deng, Lin Lu, Nay Oo, Shuicheng Yan, and Bryan Hooi. Vpi-bench: Visual prompt injection attacks for computer-use agents. In ","arxiv_match":false},{"id":"xRgpRQaqcN","title":"Decodable Need Not Be Reward-Reachable: A Controlled Study of Direct-Answer Learning in RLVR","forum_url":"https://openreview.net/forum?id=xRgpRQaqcN","excerpt":"Raff, and Stella Biderman. LEACE: Perfect linear concept erasure in closed form. In Advances in Neural Information Processing Systems, 2023. Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=ETKGuby0hcs. Pengxiang Cai, Tianchen Fang, Xiaohan Li, Qingyuan Zeng, Guocong Li, and ","arxiv_match":false}],"id":"2212.03827","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":430},{"title":"SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?","arxiv_id":"2509.16941","year":2025,"url":"https://arxiv.org/abs/2509.16941","citing_submissions":242,"explicit_arxiv_matches":181,"examples":[{"id":"t8Pg5VElI5","title":"Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments","forum_url":"https://openreview.net/forum?id=t8Pg5VElI5","excerpt":". URL https://arxiv.org/abs/2605.20876. Xiang Deng, Jeff Da, Edwin Pan, Yannis Yiming He, Charles Ide, Kanak Garg, Niklas Lauffer, Andrew Park, Nitin Pasari, Chetan Rane, Karmini Sampath, Maya Krishnan, Srivatsa Kundurthy, Sean Hendryx, Zifan Wang, Vijay Bharadwaj, Jeff Holm, Raja Aluri, Chen Bo Calvin Zhang, Noah Jacobson, Bing Liu, and Brad Kenstler. SWE-Bench Pro: Can AI agents solve long-horizon software engineering tasks?, 2025. URL https://arxiv.org/abs/2509.16941. Zhiyuan Fan, Tinghao Yu, Yuanjun Cai, Jiangtao Guan, Yun Yang, Dingxin Hu, Jiang Zhou, Xing Wu, Zh","arxiv_match":true},{"id":"qXSQKMA9CQ","title":"UMAO: Coupling Cache Lifecycle and Admission for Agents on Unified Memory","forum_url":"https://openreview.net/forum?id=qXSQKMA9CQ","excerpt":"ak Garg, Niklas Lauffer, Andrew Park, Chetan Rane, Karmini Sampath, Maya Krishnan, Srivatsa R Kundurthy, Sean M. Hendryx, Zifan Wang, Chen Bo Calvin Zhang, Noah Jacobson, Bing Liu, and Brad Kenstler. SWE-bench pro: Can AI agents solve long-horizon software engineering tasks? In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/ forum?id=uEVTdoAbnK. Bin Gao, Zhuomin He, Puru Sharma, Qingxuan Kang, Djordje Jevdjic, Junbo Deng, Xing","arxiv_match":false},{"id":"I35nG4HR52","title":"When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making","forum_url":"https://openreview.net/forum?id=I35nG4HR52","excerpt":"ng Feng, Chimaobi Okite, Kaijian Zou, Lu Wang, Rada Mihalcea, and Yulong Chen. The wrong kind of right: Quantifying and localizing misfired alignment in llms. arXiv preprint arXiv:2606.18656, 2026. URL https://arxiv.org/abs/2606.18656. Xiang Deng, Jeff Da, Edwin Pan, Yannis Yiming He, Charles Ide, Kanak Garg, Niklas Lauffer, Andrew Park, Nitin Pasari, Chetan Rane, et al. Swe-bench pro: Can ai agents solve long-horizon software engineering tasks? arXiv preprint arXiv:2509.16941, 2025. dots.studio. dots3-note-prev model card. https://huggingface.co/dots-studio/ dots3-note-prev","arxiv_match":true}],"id":"2509.16941","metadata_source":"Cached arXiv API metadata","rank":431},{"title":"BGE M3-Embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation","arxiv_id":"2402.03216","year":2024,"url":"https://arxiv.org/abs/2402.03216","citing_submissions":241,"explicit_arxiv_matches":83,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":" Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, 2024. Yuxin Chen, Yi Zhang, Zhengzhou Cai, Yaorui Shi, Zhiyuan Yao, Chenhang Cui, Jingnan Zheng, Yaqi Huo, Xi Su, Qi Gu, et al. Vitabench 2.0: Evaluating personalized and proactive agents in long-te","arxiv_match":false},{"id":"1V3066nDJF","title":"Contract Memory Compiler: Resolve, Then Traverse","forum_url":"https://openreview.net/forum?id=1V3066nDJF","excerpt":"rsational AI, pages 38–45, 2020. doi: 10.18653/v1/2020. nlp4convai-1.5. URL https://aclanthology.org/2020.nlp4convai-1.5/. Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the Association for Computational Linguistics: ACL 2024, pages 2318–2335, Bangkok, Thailand, August 2024. Association for Compu","arxiv_match":false},{"id":"Xt9oJoQrHM","title":"Light-DiffRetriever: Budget-Flexible Retrieval with Diffusion Language Models via Matryoshka Representative Tokens","forum_url":"https://openreview.net/forum?id=Xt9oJoQrHM","excerpt":"iv preprint arXiv:2602.16609, 2026. doi: 10.48550/ARXIV.2602.16609. URL https://dblp.org/rec/journals/corr/abs-2602-16609. Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. M3-Embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. In Findings of the Association for Computational Linguistics: ACL 2024, pp. 2318– 2335, 2024. doi: 10.18653/v1/2024.findings-acl.137. Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. O","arxiv_match":false}],"id":"2402.03216","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":432},{"title":"PyramidKV: Dynamic KV cache compression based on pyramidal information funneling","arxiv_id":"2406.02069","year":2024,"url":"https://arxiv.org/abs/2406.02069","citing_submissions":241,"explicit_arxiv_matches":151,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"u, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 3119–3137, 2024. Zefan Cai, Yichi Zhang, Bofei Gao, Yuliang Liu, Yucheng Li, Tianyu Liu, Keming Lu, Wayne Xiong, Yue Dong, Junjie Hu, et al. Pyramidkv: Dynamic kv cache compression based on pyramidal information funneling. arXiv preprint arXiv:2406.02069, 2024. Yuan Feng, Junlin Lv, Yukun Cao, Xike Xie, and S Kevin Zhou. Ada-kv: Optimizing kv cache evi","arxiv_match":true},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"er/ 2025/file/57e0358aed314784cad14a26e1eba642-Paper-Conference.pdf. Zefan Cai, Yichi Zhang, Bofei Gao, Yuliang Liu, Yucheng Li, Tianyu Liu, Keming Lu, Wayne Xiong, Yue Dong, Junjie Hu, and Wen Xiao. PyramidKV: Dynamic KV cache compression based on pyramidal information funneling. In Second Conference on Language Modeling, 2025b. URL https://openreview.net/forum?id=ayi7qezU87. Ting-Yun Chang, Harvey Yiyun Fu, Deqing Fu, Chenghao Yang, Jesse Thomason, and Robin Jia. Value-awar","arxiv_match":false},{"id":"okGS6velwf","title":"Focus-KV: Head-Wise KV Cache Compression Focus On Reasoning Heads","forum_url":"https://openreview.net/forum?id=okGS6velwf","excerpt":"5. Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, and Alexey Tumanov. Rocketkv: Accelerating long-context llm inference via two-stage kv cache compression, 2025. URL https://arxiv.org/abs/2502.14051. Zefan Cai, Yichi Zhang, Bofei Gao, Yuliang Liu, Yucheng Li, Tianyu Liu, Keming Lu, Wayne Xiong, Yue Dong, Junjie Hu, and Wen Xiao. Pyramidkv: Dynamic kv cache compression based on pyramidal information funneling, 2025. URL https://arxiv.org/abs/2406.02069. Zefan Cai, Wen Xiao, Hanshi Sun, Cheng Luo, Yikai Zhang, Ke Wan, Yucheng Li, Yeyang Zhou, Li-Wen C","arxiv_match":true}],"id":"2406.02069","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":433},{"title":"X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model","arxiv_id":"2510.10274","year":2025,"url":"https://arxiv.org/abs/2510.10274","citing_submissions":241,"explicit_arxiv_matches":98,"examples":[{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"ust world action models. arXiv preprint arXiv:2608.04404, 2026b. Jinliang Zheng, Jianxiong Li, Zhihao Wang, Dongxiu Liu, Xirui Kang, Yuchun Feng, Yinan Zheng, Jiayin Zou, Yilun Chen, Jia Zeng, et al. X-vla: Soft-prompted transformer as scalable crossembodiment vision-language-action model. International Conference on Learning Representation, 2026. Kaichen Zhou, Yuzhen Chen, Fangneng Zhan, Hang Hua, Grace Chen, Xinhai Chang, Ao Qu, Yilun Du, Zhuang Liu, Paul Pu Liang, and Mengyu Wang. ","arxiv_match":false},{"id":"8afp5PuOg2","title":"Verifiable 4D World-Action Model for Robust Robot Control","forum_url":"https://openreview.net/forum?id=8afp5PuOg2","excerpt":"rdware, 2023. URL https://arxiv.org/abs/2304.13705. Haoyu Zhen, Qiao Sun, Hongxin Zhang, Junyan Li, Siyuan Zhou, Yilun Du, and Chuang Gan. Tesseract: Learning 4d embodied world models. arXiv preprint arXiv:2504.20995, 2025. Jinliang Zheng, Jianxiong Li, Zhihao Wang, Dongxiu Liu, Xirui Kang, Yuchun Feng, Yinan Zheng, Jiayin Zou, Yilun Chen, Jia Zeng, et al. X-vla: Soft-prompted transformer as scalable crossembodiment vision-language-action model. arXiv preprint arXiv:2510.10274, 2025. Kaichen Zhou, Yuzhen Chen, Fangneng Zhan, Hang Hua, Grace Chen, Xinhai Chang, Ao Qu, Yilun D","arxiv_match":true},{"id":"Rb57sy6lCT","title":"SHARP-WAM: Sparse Hierarchical Attention with Probe Tokens for Bounded-Context World Action Modeling","forum_url":"https://openreview.net/forum?id=Rb57sy6lCT","excerpt":"inliang Zheng, Jianxiong Li, Zhihao Wang, Dongxiu Liu, Xirui Kang, Yuchun Feng, Yinan Zheng, Jiayin Zou, Yilun Chen, Jia Zeng, Ya-Qin Zhang, Jiangmiao Pang, Jingjing Liu, Tai Wang, and Xianyuan Zhan. X-VLA: Soft-prompted transformer as scalable cross-embodiment visionlanguage-action model. In The Fourteenth International Conference on Learning Representations, 2026. A I MPLEMENTATION AND ROUTING D ETAILS A.1 I MPLEMENTATION D ETAILS Table 6 summarizes the shared model, memory, and opt","arxiv_match":false}],"id":"2510.10274","metadata_source":"Cached arXiv API metadata","rank":434},{"title":"$π^3$: Permutation-Equivariant Visual Geometry Learning","arxiv_id":"2507.13347","year":2025,"url":"https://arxiv.org/abs/2507.13347","citing_submissions":240,"explicit_arxiv_matches":91,"examples":[{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":"ric 3d vision made easy, 2024. URL https://arxiv.org/abs/2312.14132. Yifan Wang, Jianjun Zhou, Haoyi Zhu, Wenzheng Chang, Yang Zhou, Zizun Li, Junyi Chen, Jiangmiao Pang, Chunhua Shen, and Tong He. π 3 : Permutation-equivariant visual geometry learning. In International Conference on Learning Representations, volume 2026, pp. 10481– 10497, 2026. Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. Detr3d:","arxiv_match":false},{"id":"uqg7gDThwD","title":"Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation","forum_url":"https://openreview.net/forum?id=uqg7gDThwD","excerpt":"ation. Advances in Neural Information Processing Systems, 38, 2026b. Yifan Wang, Jianjun Zhou, Haoyi Zhu, Wenzheng Chang, Yang Zhou, Zizun Li, Junyi Chen, Jiangmiao Pang, Chunhua Shen, and Tong He. π 3 : Permutation-equivariant visual geometry learning. In International Conference on Learning Representations, volume 2026, pp. 10481–10497, 2026c. Hang Wu, Yujun Cai, Haonan Ge, Hongkai Chen, Ming-Hsuan Yang, and Yiwei Wang. Refineshot: Rethinking cin","arxiv_match":false},{"id":"gXl1y5L562","title":"CLASP: Complementary Chunk and Long Passes for Feed-Forward 3D Reconstruction","forum_url":"https://openreview.net/forum?id=gXl1y5L562","excerpt":" DUSt3R: Geometric 3D vision made easy. In CVPR, pp. 20697–20709, 2024. Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer. TartanAir: A dataset to push the limits of visual SLAM. In IROS, 2020. Yifan Wang, Jianjun Zhou, Haoyi Zhu, Wenzheng Chang, Yang Zhou, Zizun Li, Junyi Chen, Jiangmiao Pang, Chunhua Shen, and Tong He. π 3 : Permutation-equivariant visual geometry learning. arXiv preprint arXiv:2507.13347, 2025c. Zipeng Wang and Dan Xu. FlashVGGT: Efficient and scalable visual geometry transformers with","arxiv_match":true}],"id":"2507.13347","metadata_source":"Cached arXiv API metadata","rank":435},{"title":"The internal state of an llm knows when it’s lying","arxiv_id":"2304.13734","year":2023,"url":"https://arxiv.org/abs/2304.13734","citing_submissions":240,"explicit_arxiv_matches":41,"examples":[{"id":"U9lsoJxNae","title":"Beyond Confidence? Auditing Routing Entropy in Attention-Residual Transformers","forum_url":"https://openreview.net/forum?id=U9lsoJxNae","excerpt":" Amos Azaria and Tom Mitchell. The internal state of an LLM knows when it’s lying. arXiv preprint arXiv:2304.13734, 2023. Thomas B. Berrett, Yi Wang, Rina Foygel Barber, and Richard J. Samworth. The conditional per","arxiv_match":true},{"id":"zFcnFuaRmq","title":"Search-G1: Co-Evolving Representation-Based Rewards for Selective Grounded Search","forum_url":"https://openreview.net/forum?id=zFcnFuaRmq","excerpt":"elf-rag: Learning to retrieve, generate, and critique through self-reflection. In International conference on learning representations, volume 2024, pp. 9112–9141, 2024. Amos Azaria and Tom Mitchell. The internal state of an llm knows when it’s lying. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 967–976, 2023. Dingwei Chen, Zefang Zong, Zhipeng Ma, Leo Luo, Yang Li, Chengming Li, Peng Chen, and Jie Jiang. Efficien","arxiv_match":false},{"id":"FrxiIrkLSN","title":"The Anatomy of Conversational Consistency: Linear Readouts and Control Under Pressure","forum_url":"https://openreview.net/forum?id=FrxiIrkLSN","excerpt":"d. Many-shot jailbreaking. In Advances in Neural Information Processing Systems, volume 37, pp. 129696– 129742. Curran Associates, Inc., 2024. doi: 10.52202/079017-4121. Amos Azaria and Tom Mitchell. The internal state of an LLM knows when it’s lying. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 967–976, Singapore, 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.findings-emnlp.68. Collin Bur","arxiv_match":false}],"id":"2304.13734","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":436},{"title":"Llama guard: Llm-based input-output safeguard for human-ai conversations","arxiv_id":"2312.06674","year":2023,"url":"https://arxiv.org/abs/2312.06674","citing_submissions":239,"explicit_arxiv_matches":212,"examples":[{"id":"mVJzfFoRun","title":"Checking Leakage Witnesses versus Certifying Bounded Non-Leakage","forum_url":"https://openreview.net/forum?id=mVJzfFoRun","excerpt":"nd Francis Zane. Which problems have strongly exponential complexity? Journal of Computer and System Sciences, 63(4):512–530, 2001. doi: 10.1006/jcss.2001.1774. Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa. Llama Guard: LLMbased input-output safeguard for human-AI conversations. arXiv preprint arXiv:2312.06674, 2023. URL https://arxiv.org/abs/2312.06674. Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer. Reluplex: An effic","arxiv_match":true},{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":"Price, Aengus Lynch, Rylan Schaeffer, Fazl Barez, Sanmi Koyejo, Henry Sleight, Erik Jones, Ethan Perez, and Mrinank Sharma. Best-of-n jailbreaking, 2024. URL https: //arxiv.org/abs/2412.03556. Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa. Llama guard: Llm-based input-output safeguard for human-ai conversations, 2023. URL https://arxiv.org/abs/ 2312.06674. Fengqing Jiang, Zhangchen Xu, Luyao Niu, Zhen Xiang, Bhaskar Ramasubramanian, Bo Li, and Radha Poo","arxiv_match":true},{"id":"y89ckFndSX","title":"HazardAuditor: From Executable Threats to Safer Computer-Use Agents","forum_url":"https://openreview.net/forum?id=y89ckFndSX","excerpt":" Yuchen Lin, Nathan Lambert, Yejin Choi, and Nouha Dziri. Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms. Advances in neural information processing systems, 37:8093–8131, 2024. Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, et al. Llama guard: Llm-based input-output safeguard for human-ai conversations. arXiv preprint arXiv:2312.06674, 2023. Jev Model Guide. Jev AI: TypeSafe’s Model for Typed Decisions, 2026. jevmodel.org/. Updated ","arxiv_match":true}],"id":"2312.06674","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":437},{"title":"SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics","arxiv_id":"2506.01844","year":2025,"url":"https://arxiv.org/abs/2506.01844","citing_submissions":239,"explicit_arxiv_matches":227,"examples":[{"id":"p6thn2PhEo","title":"Don't Throw Away the Solution: Training-Free Acceleration of Flow-Based Robot Policies","forum_url":"https://openreview.net/forum?id=p6thn2PhEo","excerpt":"ience and Systems, 2024. Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, and Nima Anari. Parallel sampling of diffusion models. In Advances in Neural Information Processing Systems, 2023. Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, et al. Smolvla: A vision-language-action model for affordable and efficient robotics. arXiv preprint arXiv:2506.01844, 2025. Junhyuk So, Jiwoong Shin, Chaeyeon Jang, and Eunhyeok Park. PCM: Picard consistency model fo","arxiv_match":true},{"id":"eANQWkH13Z","title":"PriGo: Primitive Guidance to Diffusion and Flow Policies for Adaptive Robotic Manipulation","forum_url":"https://openreview.net/forum?id=eANQWkH13Z","excerpt":", James Tanner, Anna Walling, Haohuan Wang, Niccolo Fusai, et al. Hi robot: Open-ended instruction following with hierarchical vision-language-action models. arXiv preprint arXiv:2502.19417, 2025. Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, et al. Smolvla: A vision-language-action model for affordable and efficient robotics. arXiv:2506.01844, 2025. Qiao Sun, Liujia Yang, Wei Tang, Wei Huang, Kaixin Xu, Yongchao Chen, Mingyu Liu, Jiange Yan","arxiv_match":true},{"id":"hjPoL9WGRc","title":"AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents","forum_url":"https://openreview.net/forum?id=hjPoL9WGRc","excerpt":"of an autonomous underwater vehicle using Lyapunov-based model predictive control. IEEE Transactions on Industrial Electronics, 65(7):5796–5805, 2018. doi: 10.1109/TIE.2017.2779442. Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, Simon Alibert, et al. SmolVLA: A vision-language-action model for affordable and efficient robotics. arXiv preprint arXiv:2506.01844, 2025. Satja Sivčev, Joseph Coleman, Edin Omerdić, Gerard Dooly, and Daniel Toal. Underwater mani","arxiv_match":true}],"id":"2506.01844","metadata_source":"Cached arXiv API metadata","rank":438},{"title":"Spinquant: Llm quantization with learned rotations","arxiv_id":"2405.16406","year":2024,"url":"https://arxiv.org/abs/2405.16406","citing_submissions":239,"explicit_arxiv_matches":63,"examples":[{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":" 2024. arXiv preprint arXiv:2306.00978, 2024. Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort. Spinquant: Llm quantization with learned rotations. In International Conference on Learning Representations, volume 2025, pp. 92009–92032, 2025. Xinyin Ma, Gongfan Fang, and Xinchao Wang. Llm-pruner: On the structural pruning of large language models","arxiv_match":false},{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"sfer data with rectified flow. In ICLR, 2023. Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort. SpinQuant: LLM quantization with learned rotations. In ICLR, 2025. Lichen Ma, Zipeng Guo, Yu He, Xiaolong Fu, Luohang Liu, Jingling Fu, Junshi Huang, and Yan Li. Frequencybooster: Full-frequency modeling for high-fidelity pixel diffusion. arXiv prepr","arxiv_match":false},{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"raining approach. CoRR, abs/1907.11692, 2019. Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort. Spinquant: LLM quantization with learned rotations. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. Zhenyi Lu, Chenghao Fan, Wei Wei, Xiaoye Qu, Dangyang Chen, and","arxiv_match":false}],"id":"2405.16406","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":439},{"title":"Language agents as optimizable graphs","arxiv_id":"2402.16823","year":2024,"url":"https://arxiv.org/abs/2402.16823","citing_submissions":238,"explicit_arxiv_matches":66,"examples":[{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"hang, Xiang Feng, Qiming Zhang, Haibo Qiu, Lihuo He, Dengpan Ye, Xinbo Gao, and Jing Zhang. Omni-i2c: A holistic benchmark for high-fidelity image-to-code generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 37761–37798, 2026. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber. Language agents as optimizable graphs. arXiv preprint arXiv:2402.16823, 2024. A DATA C ONSTRUCTION D ETAILS A.1 S OURCE TASKS AND DATA C OLLECTION Training tasks are draw","arxiv_match":true},{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"25b. doi: 10.48550/ARXIV.2510.04618. URL https://doi.org/ 10.48550/arXiv.2510.04618. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber. Gptswarm: Language agents as optimizable graphs. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, volume 235 of Proceedings of Machine Learning Research, pp. 62743–62767. PMLR / OpenReview.","arxiv_match":false},{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"er_files/paper/2024/hash/ e41efb03e20ca3c231940a3c6917ef6f-Abstract-Conference.html. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber. GPTSwarm: Language agents as optimizable graphs. In International Conference on Machine Learning (ICML), 2024. https://proceedings.mlr.press/v235/zhuge24a.html. ","arxiv_match":false}],"id":"2402.16823","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":440},{"title":"ScienceWorld: Is your agent smarter than a 5th grader?","arxiv_id":"2203.07540","year":2022,"url":"https://arxiv.org/abs/2203.07540","citing_submissions":238,"explicit_arxiv_matches":30,"examples":[{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":"Computational Linguistics, 2024. doi: 10.18653/V1/2024. ACL-LONG.850. URL https://doi.org/10.18653/v1/2024.acl-long.850. Ruoyao Wang, Peter A. Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. Scienceworld: Is your agent smarter than a 5th grader? In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, Decemb","arxiv_match":false},{"id":"pZqfqmVvoU","title":"ReTTT: Reflection-Driven Test-Time Training via Experience-Based Parametric Memory","forum_url":"https://openreview.net/forum?id=pZqfqmVvoU","excerpt":"nima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023. Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. Scienceworld: Is your agent smarter than a 5th grader? In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 11279–11298, 2022. Yanbo Wang, Jinhua Hao, Yuze Shi, Kun Yuan, and Ming Sun. No time like the present: ","arxiv_match":false},{"id":"D0cLnmHjUF","title":"Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction","forum_url":"https://openreview.net/forum?id=D0cLnmHjUF","excerpt":"nima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023. Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. Scienceworld: Is your agent smarter than a 5th grader? In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 11279–11298, 2022. Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiy","arxiv_match":false}],"id":"2203.07540","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":441},{"title":"Self-instruct: Aligning language models with self-generated instructions","arxiv_id":"2212.10560","year":2022,"url":"https://arxiv.org/abs/2212.10560","citing_submissions":238,"explicit_arxiv_matches":48,"examples":[{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"Machine. arXiv preprint arXiv:2510.21614, 2025b. URL https://arxiv.org/abs/2510.21614. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self-generated instructions. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13484– 13508, Toro","arxiv_match":false},{"id":"07pvXM8POP","title":"Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills","forum_url":"https://openreview.net/forum?id=07pvXM8POP","excerpt":"lf-improvement via strategic gamified self-play. arXiv preprint arXiv:2509.25541, 2025. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self-generated instructions. In Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers), pp. 13484–13508, 2023. Yuxiang Wei, Zhiqing Sun, Emily McMilin, Jonas Gehring, Dav","arxiv_match":false},{"id":"OFVWcaGfdi","title":"The Delta Is Not the Skill: Zero-Prior Elicitation Transfers LoRA Behavior Across Model Families","forum_url":"https://openreview.net/forum?id=OFVWcaGfdi","excerpt":"eparameteref f icientf inetuning.Advances in Neural Information P 61217 − −61237, 2024. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language models with self-generated instructions. In Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers), pp. 13484–13508, 2023. Feifan Xia, Mingyang Liao, Yuyang Fang, Defang Li, Yantong ","arxiv_match":false}],"id":"2212.10560","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":442},{"title":"Fine-tuning aligned language models compromises safety, even when users do not intend to!","arxiv_id":"2310.03693","year":2023,"url":"https://arxiv.org/abs/2310.03693","citing_submissions":237,"explicit_arxiv_matches":49,"examples":[{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":"g, and Shengjia Zhao. gpt-oss-120b & gpt-oss-20b model card, 2025. URL https://arxiv.org/abs/2508.10925. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. Fine-tuning aligned language models compromises safety, even when users do not intend to! In International Conference on Learning Representations, volume 2024, pp. 30988–31043, 2024. Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Li","arxiv_match":false},{"id":"ygrx8bN3MO","title":"Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning","forum_url":"https://openreview.net/forum?id=ygrx8bN3MO","excerpt":"On evaluating the durability of safeguards for open-weight llms. arXiv preprint arXiv:2412.07097, 2024b. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. Fine-tuning aligned language models compromises safety, even when users do not intend to! In International Conference on Learning Representations, 2024c. Domenic Rosati, Jan Wehner, Kai Williams, Łukasz Bartoszcze, David Atanasov, Robie Gonzales, Subhabrata Majumdar, Carsten Maple, Hassa","arxiv_match":false},{"id":"bCiKlMuOkz","title":"SafeOPD: Safety-Preserving On-Policy Distillation from Domain-Specialized Teachers","forum_url":"https://openreview.net/forum?id=bCiKlMuOkz","excerpt":"eralizing verifiable instruction following. Advances in Neural Information Processing Systems, 38, 2026. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. Fine-tuning aligned language models compromises safety, even when users do not intend to! In International Conference on Learning Representations, volume 2024, pp. 30988–31043, 2024. Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, and Peter","arxiv_match":false}],"id":"2310.03693","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":443},{"title":"SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning","arxiv_id":"2602.08234","year":2026,"url":"https://arxiv.org/abs/2602.08234","citing_submissions":237,"explicit_arxiv_matches":190,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":" Zixuan Wang, Yuchen Yan, Hongxing Li, Teng Pan, Dingming Li, Ruiqing Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, and Yongliang Shen. Milestone-guided policy learning for long-horizon language agents. arXiv preprint arXiv:2605.06078, 2026c. Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, et al. Skillrl: Evolving agents via recursive skill-augmented reinforcement learning. arXiv preprint arXiv:2602.08234, 2026. Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja ","arxiv_match":true},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"ie Jin, Yuhao Zhou, Jian Guan, Wei Wu, Tao Ji, Tao Gui, Qi Zhang, and Xuanjing Huang. Agentprm: Process reward models for LLM agents via step-wise promise and progress. In Proceedings of the ACM Web Conference 2026, WWW, pp. 4184–4195, 2026. Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, et al. Skillrl: Evolving agents via recursive skill-augmented reinforcement learning. arXiv preprint arXiv:2602.08234, 2026. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-mem: Agentic m","arxiv_match":true},{"id":"x3rKKKW2Wg","title":"VideoEvolve: Co-Evolving Memory and Retrieval for Long Video Understanding","forum_url":"https://openreview.net/forum?id=x3rKKKW2Wg","excerpt":"Xiv:2510.16079, 2025. Peng Xia, Kaide Zeng, Jiaqi Liu, Can Qin, Fang Wu, Yiyang Zhou, Caiming Xiong, and Huaxiu Yao. Agent0: Unleashing self-evolving agents from zero data via tool-integrated reasoning. arXiv preprint arXiv:2511.16043, 2025. Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, et al. Skillrl: Evolving agents via recursive skill-augmented reinforcement learning. arXiv preprint arXiv:2602.08234, 2026. Jingkang Yang, Shuai Liu, Hongming Guo, Yuhao Dong, Xiamengwei Zhang, Sicheng Zhang, Pengyun","arxiv_match":true}],"id":"2602.08234","metadata_source":"Cached arXiv API metadata","rank":444},{"title":"BLINK: Multimodal large language models can see but not perceive","arxiv_id":"2404.12390","year":2024,"url":"https://arxiv.org/abs/2404.12390","citing_submissions":236,"explicit_arxiv_matches":58,"examples":[{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"aching mllms to think with images. arXiv preprint arXiv:2505.15879, 2025. Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei-Chiu Ma, and Ranjay Krishna. Blink: Multimodal large language models can see but not perceive. In European Conference on Computer Vision (ECCV), 2024. Xingyu Fu, Minqian Liu, Zhengyuan Yang, John Corring, Yijuan Lu, Jianwei Yang, Dan Roth, Dinei Florencio, and Cha Zhang. Refocus: Visual editi","arxiv_match":false},{"id":"Bb2HSkeepN","title":"An Exam for Active Observers","forum_url":"https://openreview.net/forum?id=Bb2HSkeepN","excerpt":" Drury. Inspection performance. In Gavriel Salvendy (ed.), Handbook of Industrial Engineering, pp. 2282–2314. Wiley, 2nd edition, 1992. John M. Findlay and Iain D. Gilchrist. Active Vision: The Psychology of Looking and Seeing. Oxford University Press, 2003. Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A Smith, Wei-Chiu Ma, and Ranjay Krishna. Blink: Multimodal large language models can see but not perceive. arXiv preprint arXiv:2404.12390, 2024. Andreas Gegenfurtner, Erno Lehtinen, and Roger Säljö. Expertise differences in the comprehen","arxiv_match":true},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"ssociation for Computational Linguistics (Volume 2: Short Papers), pp. 346–355, Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10.18653/v1/2024.acl-short.33. URL https://aclanthology.org/2024.acl-short.33/. Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei-Chiu Ma, and Ranjay Krishna. Blink: Multimodal large language models can see but not perceive, 2024. URL https://arxiv.org/abs/2404.12390. Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan, Joshua Ainslie, Jean-Baptiste Alayrac,","arxiv_match":true}],"id":"2404.12390","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":445},{"title":"Causal abstractions of neural networks","arxiv_id":"2106.02997","year":2021,"url":"https://arxiv.org/abs/2106.02997","citing_submissions":236,"explicit_arxiv_matches":37,"examples":[{"id":"gbzRgMFFW6","title":"Fixed-Input State Dependence of Residual Transmission in Spiking Neural Networks","forum_url":"https://openreview.net/forum?id=gbzRgMFFW6","excerpt":". Fractional-order spiking neural network. In International Conference on Learning Representations, volume 2026, pp. 79651–79696, 2026. Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts. Causal abstractions of neural networks. volume 34, pp. 9574–9586, 2021. Atticus Geiger, Duligur Ibeling, Amir Zur, Maheep Chaudhary, Sonakshi Chauhan, Jing Huang, Aryaman Arora, Zhengxuan Wu, Noah Goodman, Christopher Potts, et al. Causal","arxiv_match":false},{"id":"fVlHzXO4UN","title":"When Does a Novel Form Become a Word? Causal Evidence for Reusable Lexical Function in Frozen Language Models","forum_url":"https://openreview.net/forum?id=fVlHzXO4UN","excerpt":"068431-2217. URL https://proceedings.neurips.cc/paper_files/paper/2022/hash/ c529dba08a146ea8d6cf715ae8930cbe-Abstract-Conference.html. Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts. Causal abstractions of neural networks. In Advances in Neural Information Processing Systems, volume 34, pp. 9574–9586, 2021. URL https://proceedings.neurips.cc/paper/2021/hash/ 4f5c422f4d49a5a807eda27434231040-Abstract.html. Gemma Team. ","arxiv_match":false},{"id":"aE2Bm53N5i","title":"Behaviorally Silent Now, Causally Active Later: Deferred State Effects in Recurrent Policies for Reinforcement Learning","forum_url":"https://openreview.net/forum?id=aE2Bm53N5i","excerpt":" Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts. Causal abstractions of neural networks. Advances in neural information processing systems, 34:9574–9586, 2021. Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, and Noah Goodman. Finding alignments between interpretable causa","arxiv_match":false}],"id":"2106.02997","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":446},{"title":"DeepEyes: Incentivizing \"Thinking with Images\" via Reinforcement Learning","arxiv_id":"2505.14362","year":2025,"url":"https://arxiv.org/abs/2505.14362","citing_submissions":236,"explicit_arxiv_matches":107,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"l learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Ziwei Zheng, Minghao Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, and Chao Shen. Deepeyes: Incentivizing” thinking with images” via reinforcement learning. In International Conference on Learning Representations, volume 2026, pp. 126775–126798, 2026. Query State Retrieved Top-5 Skill Initial State Seed: 10179 skill_36: 1.00 skill_38: 0.91 skill_26: 0.8","arxiv_match":false},{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"Shuangqing Zhang, Junfei Wu, Feng Li, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. Mme-realworld: Could your multimodal llm challenge high-resolution real-world scenarios that are difficult for humans?, 2025d. URL https://arxiv.org/abs/2408.13257. Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. Deepeyes: Incentivizing\" thinking with images\" via reinforcement learning. arXiv preprint arXiv:2505.14362, 2025. Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vi","arxiv_match":true},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"tion Processing Systems, 38:168990–169012, 2026b. Zhi Zheng, Yu Gu, Wei Liu, Yee Whye Teh, and Wee Sun Lee. Soft-grpo: Surpassing discrete-token llm reinforcement learning via gumbel-reparameterized soft-thinking policy optimization, 2026. URL https://arxiv.org/abs/2511.06411. Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. Deepeyes: Incentivizing\" thinking with images\" via reinforcement learning. arXiv preprint arXiv:2505.14362, 2025. A T RAINING D ETAILS A.1 S UPERVISED TARGETS AND AUXILIARY LOSSES Stage 1 minimizes the next","arxiv_match":true}],"id":"2505.14362","metadata_source":"Cached arXiv API metadata","rank":447},{"title":"LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models","arxiv_id":"2510.13626","year":2025,"url":"https://arxiv.org/abs/2510.13626","citing_submissions":236,"explicit_arxiv_matches":227,"examples":[{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":" 2025a. Yi Chen, Yuying Ge, Weiliang Tang, Yizhuo Li, Yixiao Ge, Mingyu Ding, Ying Shan, and Xihui Liu. Moto: Latent motion token as the bridging language for learning robot manipulation from videos. In ICCV, 2025b. Senyu Fei, Siyin Wang, Junhao Shi, Zihao Dai, Jikun Cai, Pengfang Qian, Li Ji, Xinzhe He, Shiduo Zhang, Zhaoye Fei, Jinlan Fu, Jingjing Gong, and Xipeng Qiu. LIBERO-Plus: In-depth robustness analysis of vision-language-action models. arXiv:2510.13626, 2025. Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel. Deep s","arxiv_match":true},{"id":"vwDnaAnR3T","title":"When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models","forum_url":"https://openreview.net/forum?id=vwDnaAnR3T","excerpt":"machine learning. Journal of Machine Learning Research, 23(226):1–61, 2022. Nastaran Darabi and Amit Ranjan Trivedi. Progal-vla: Grounded alignment through prospective reasoning in vision-language-action models. arXiv preprint arXiv:2604.09824, 2026. Senyu Fei, Siyin Wang, Junhao Shi, Zihao Dai, Jikun Cai, Pengfang Qian, Li Ji, Xinzhe He, Shiduo Zhang, Zhaoye Fei, et al. Libero-plus: In-depth robustness analysis of vision-language-action models. arXiv preprint arXiv:2510.13626, 2025. Yu Fang, Yuchun Feng, Dong Jing, Jiaqi Liu, Yue Yang, Zhenyu Wei, Daniel Szafir, and Mingyu ","arxiv_match":true},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"t-guided video generation. Advances in neural information processing systems, 36:9156–9172, 2023. Gunnar Farnebäck. Two-frame motion estimation based on polynomial expansion. In Scandinavian conference on Image analysis, pp. 363–370. Springer, 2003. Senyu Fei, Siyin Wang, Junhao Shi, Zihao Dai, Jikun Cai, Pengfang Qian, Li Ji, Xinzhe He, Shiduo Zhang, Zhaoye Fei, et al. Libero-plus: In-depth robustness analysis of vision-language-action models. arXiv preprint arXiv:2510.13626, 2025. Chelsea Finn and Sergey Levine. Deep visual foresight for planning robot motion. In 2017 IEE","arxiv_match":true}],"id":"2510.13626","metadata_source":"Cached arXiv API metadata","rank":448},{"title":"Mass-editing memory in a transformer","arxiv_id":"2210.07229","year":2022,"url":"https://arxiv.org/abs/2210.07229","citing_submissions":236,"explicit_arxiv_matches":66,"examples":[{"id":"W1xeiKanFu","title":"Do Correct Answers Have Stable Latent Knowledge Support?","forum_url":"https://openreview.net/forum?id=W1xeiKanFu","excerpt":"ocating and editing factual associations in gpt. Advances in neural information processing systems, 35:17359–17372, 2022. Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. Mass-editing memory in a transformer. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=MkbcAHIYgyS. Aaron Meurer, Christopher P Smith, Mateusz Paprocki, Ondřej Čertı́k, Sergey B Kirpic","arxiv_match":false},{"id":"mL2znR6ezq","title":"Domain-Grounded Counterfactual Augmentation for Mitigating Knowledge Conflicts in LLM Fine-Tuning","forum_url":"https://openreview.net/forum?id=mL2znR6ezq","excerpt":"n Cho (eds.), Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/ forum?id=-h6WAS6eE4. Kevin Meng, Arnab Sen Sharma, Alex J Andonian, Yonatan Belinkov, and David Bau. Mass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=MkbcAHIYgyS. Ella Neeman, Roee Aharoni, Or Honovich, Leshem Choshen, Idan Szpektor, an","arxiv_match":false},{"id":"bdZAOqTSho","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","forum_url":"https://openreview.net/forum?id=bdZAOqTSho","excerpt":"Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in GPT. In NeurIPS, 2022. Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. Mass-editing memory in a transformer. In ICLR, 2023. Meta AI. Llama 3.2: Revolutionizing edge AI and vision with open, customizable models. Meta AI release documentation, 2024. Kyle O’Brien, Stephen Casper, Quentin Anthony, Tomek Korbak","arxiv_match":false}],"id":"2210.07229","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":449},{"title":"Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet","arxiv_id":"2605.29358","year":2024,"url":"https://arxiv.org/abs/2605.29358","citing_submissions":236,"explicit_arxiv_matches":36,"examples":[{"id":"iksnFm6vFW","title":"Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage","forum_url":"https://openreview.net/forum?id=iksnFm6vFW","excerpt":"es, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan. Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet. Transformer Circuits Thread, 2024. URL https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html. Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J Vazquez, Ulis","arxiv_match":false},{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"k Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan. Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet, 2026. URL https://arxiv.org/abs/2605.29358. Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. Language Models Don’t Always Say ","arxiv_match":true},{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"es, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan. Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet. Transformer Circuits Thread, 2024. URL https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html. Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffan","arxiv_match":false}],"id":"2605.29358","metadata_source":"Cached arXiv API metadata","rank":450},{"title":"Spatialvlm: Endowing vision-language models with spatial reasoning capabilities","arxiv_id":"2401.12168","year":2024,"url":"https://arxiv.org/abs/2401.12168","citing_submissions":236,"explicit_arxiv_matches":36,"examples":[{"id":"6MjQdxXZoS","title":"VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=6MjQdxXZoS","excerpt":"et al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022. Boyuan Chen, Zhuo Xu, Sean Kirmani, Brain Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. Spatialvlm: Endowing vision-language models with spatial reasoning capabilities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14455–14465, 2024. Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Zixuan Li, Qiwei Liang, X","arxiv_match":false},{"id":"TA0thWBRuQ","title":"ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence","forum_url":"https://openreview.net/forum?id=TA0thWBRuQ","excerpt":"rom RGB-D data in indoor environments. In International Conference on 3D Vision, pp. 667–676. IEEE, 2017. Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. SpatialVLM: Endowing vision-language models with spatial reasoning capabilities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14455– 14465, 2024a. Joya Chen, Zhaoyang Lv, Shiwei Wu, Kevin Qinghong Lin, Chenan Song, Difei Gao, Jia-Wei ","arxiv_match":false},{"id":"PjUHgdvzhE","title":"A Recursive Self-Improving Harness for Training-Free Generalist Navigation","forum_url":"https://openreview.net/forum?id=PjUHgdvzhE","excerpt":"GB-D data in indoor environments. In 2017 International Conference on 3D Vision (3DV), pp. 667–676, 2017. Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. Spatialvlm: Endowing vision-language models with spatial reasoning capabilities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14455– 14465, June 2024a. Jiaqi Chen, Bingqian Lin, Ran Xu, Zhenhua Chai, Xiaodan Liang, and Kwan-Yee","arxiv_match":false}],"id":"2401.12168","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":451},{"title":"DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model","arxiv_id":"2405.04434","year":2024,"url":"https://arxiv.org/abs/2405.04434","citing_submissions":235,"explicit_arxiv_matches":212,"examples":[{"id":"PCAODQ61Qw","title":"BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation","forum_url":"https://openreview.net/forum?id=PCAODQ61Qw","excerpt":" you are looking for before generation. Advances in Neural Information Processing Systems, 37:22947–22970, 2024. Yujun Lin, Haotian Tang, Shang Yang, Zhekai Zhang, Guangxuan Xiao, Chuang Gan, and Song Han. Qserve: W4a8kv4 quantization and system co-design for efficient llm serving, 2025. URL https://arxiv.org/abs/2405.04532. Aixin Liu, Hao Bei, Haowei Chen, et al. DeepSeek-V2: A strong, economical, and efficient mixtureof-experts language model. arXiv preprint arXiv:2405.04434, 2024a. Di Liu, Meng Chen, Baotong Lu, Huiqiang Jiang, Zhenhua Han, Qianxi Zhang, Qi Chen, Chengrui","arxiv_match":true},{"id":"LxwIDZer16","title":"When Cleaner Caches Break: Training Recipes Shape MLA Cache Quantizability","forum_url":"https://openreview.net/forum?id=LxwIDZer16","excerpt":"oldi, and Jonas Geiping. Training dynamics impact post-training quantization robustness. arXiv preprint arXiv:2510.06213, 2025. Chi-Chih Chang, Wei-Cheng Lin, Chien-Yu Lin, Chong-Yan Chen, Yu-Fang Hu, Pei-Shuo Wang, Ning-Chi Huang, Luis Ceze, Mohamed S. Abdelfattah, and Kai-Chiang Wu. Palu: KV-cache compression with low-rank projection. In ICLR, 2025. DeepSeek-AI. DeepSeek-V2: A strong, economical, and efficient Mixture-of-Experts language model. arXiv preprint arXiv:2405.04434, 2024a. DeepSeek-AI. DeepSeek-V3 technical report. arXiv preprint arXiv:2412.19437, 2024b. DeepSeek","arxiv_match":true},{"id":"Z8p0l7Gya9","title":"What Does Language-Model Pretraining Contribute to Time-Series Understanding","forum_url":"https://openreview.net/forum?id=Z8p0l7Gya9","excerpt":"es llm capability for time series classification. ArXiv, abs/2601.03464, 2026. URL https://api. semanticscholar.org/CorpusID:284532767. Zhihong Shao, Damai Dai, Daya Guo, Zihan Wang, and Huajian Xin. Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model. ArXiv, abs/2405.04434, 2024. URL https://api.semanticscholar.org/CorpusID:269613809. Mingtian Tan, Mike A Merrill, Vinayak Gupta, Tim Althoff, and Thomas Hartvigsen. Are language models actually use","arxiv_match":false}],"id":"2405.04434","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":452},{"title":"Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting","arxiv_id":"2310.11324","year":2023,"url":"https://arxiv.org/abs/2310.11324","citing_submissions":235,"explicit_arxiv_matches":68,"examples":[{"id":"ldfZCUZMiQ","title":"Interface-Induced Trajectory Censoring: When Tool-Call Interfaces Distort Evaluation and RL Experience","forum_url":"https://openreview.net/forum?id=ldfZCUZMiQ","excerpt":"odel or harness? an interaction-centric taxonomy for localizing agent failures. arXiv preprint arXiv:2607.28802, 2026. Submitted 2026-07-30. Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. Quantifying language models’ sensitivity to spurious features in prompt design, or: How i learned to start worrying about prompt formatting. In International Conference on Learning Representations (ICLR), 2024. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya ","arxiv_match":false},{"id":"w7XgVxBf1Y","title":"Two Vectors Replace In-Context Demos: Structured Task Adaptation via Embeddings","forum_url":"https://openreview.net/forum?id=w7XgVxBf1Y","excerpt":" task representations from in-context learning. In Findings of the Association for Computational Linguistics: ACL 2025, pp. 6634–6663, 2025. Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting. In International Conference on Learning Representations, volume 2024, pp. 25055–25083, 2024. Nishant Subramani, Nivedita Suresh, and Matthew E Peters. Extracting latent steering vectors from pretrai","arxiv_match":false},{"id":"Ci1BmUIqt6","title":"Learned Reporting Preferences in RLVR Can Conflict with the Current Request","forum_url":"https://openreview.net/forum?id=Ci1BmUIqt6","excerpt":"ip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting. In International Conference on Learning Representations, volume 2024, pp. 25055–25083, 2024. Rulin Shao, Shuyue Stella Li, Rui Xin, Scott Geng, Yiping Wang, Sewoong Oh, Simon Shaolei Du, Nathan Lamb","arxiv_match":false}],"id":"2310.11324","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":453},{"title":"Cambrian-1: A fully open, vision-centric exploration of multimodal LLMs","arxiv_id":"2406.16860","year":2024,"url":"https://arxiv.org/abs/2406.16860","citing_submissions":234,"explicit_arxiv_matches":33,"examples":[{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":" Shengbang Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Manoj Middepogu, Sai Charitha Akula, Jihan Yang, Shusheng Yang, Adithya Iyer, Xichen Pan, Austin Wang, Rob Fergus, Yann LeCun, and Saining Xie. Cambrian-1: A fully open, vision-centric exploration of multimodal LLMs. In Advances in Neural Information Processing Systems, volume 37, pp. 87310–87356. Curran Associates, Inc., 2024. Qineng Wang, Baiqiao Yin, Pingyue Zhang, Jianshu Zhang, Kangrui Wang, Zihan Wang, Jie","arxiv_match":false},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"//thinkingmachines.ai/blog/interaction-models/. Shengbang Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Manoj Middepogu, Sai Charitha Akula, Jihan Yang, Shusheng Yang, Adithya Iyer, Xichen Pan, et al. Cambrian-1: A fully open, vision-centric exploration of multimodal llms. In NeurIPS, 2024a. Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms. In CVPR, 2024b. Shengbang Tong","arxiv_match":false},{"id":"Bb2HSkeepN","title":"An Exam for Active Observers","forum_url":"https://openreview.net/forum?id=Bb2HSkeepN","excerpt":" arXiv preprint arXiv:2408.15998, 2024. Muzi Tao and Saining Xie. What does a visual formal analysis of the world’s 500 most famous paintings tell us about multimodal llms? In The Second Tiny Papers Track at ICLR 2024, 2024. Shengbang Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Manoj Middepogu, Sai Charitha Akula, Jihan Yang, Shusheng Yang, Adithya Iyer, Xichen Pan, et al. Cambrian-1: A fully open, vision-centric exploration of multimodal llms. arXiv preprint arXiv:2406.16860, 2024a. Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide sh","arxiv_match":true}],"id":"2406.16860","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":454},{"title":"LLM evaluators recognize and favor their own generations","arxiv_id":"2404.13076","year":2024,"url":"https://arxiv.org/abs/2404.13076","citing_submissions":234,"explicit_arxiv_matches":56,"examples":[{"id":"ROquSvIhjs","title":"Inverse Garment Design with Visual Agents","forum_url":"https://openreview.net/forum?id=ROquSvIhjs","excerpt":"isco Massa, Alaaeldin El-Nouby, et al. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024. Arjun Panickssery, Samuel R. Bowman, and Shi Feng. Llm evaluators recognize and favor their own generations. In Advances in Neural Information Processing Systems, 2024. Chaitanya Patel, Zhouyingcheng Liao, and Gerard Pons-Moll. Tailornet: Predicting clothing in 3d as a function of human pose, shape and gar","arxiv_match":false},{"id":"1H9OftLafB","title":"Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling","forum_url":"https://openreview.net/forum?id=1H9OftLafB","excerpt":"election. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 32441–32466, 2026. Arjun Panickssery, Samuel R. Bowman, and Shi Feng. LLM evaluators recognize and favor their own generations. In Advances in Neural Information Processing Systems, 2024. Sumaya Abdul Rahman, Seckhen Ariel Andrade Cuellar, Ghani Raissov, and Mohammad Raza. VeriSimpl: Robust optimization modeling from natural","arxiv_match":false},{"id":"FrxiIrkLSN","title":"The Anatomy of Conversational Consistency: Linear Readouts and Control Under Pressure","forum_url":"https://openreview.net/forum?id=FrxiIrkLSN","excerpt":"instructions with human feedback. In Advances in Neural Information Processing Systems, volume 35, pp. 27730–27744, 2022. doi: 10.52202/068431-2011. Arjun Panickssery, Samuel R. Bowman, and Shi Feng. LLM evaluators recognize and favor their own generations. In Advances in Neural Information Processing Systems, volume 37, pp. 68772– 68802, 2024. doi: 10.52202/079017-2197. Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Hei","arxiv_match":false}],"id":"2404.13076","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":455},{"title":"Towards automated circuit discovery for mechanistic interpretability","arxiv_id":"2304.14997","year":2023,"url":"https://arxiv.org/abs/2304.14997","citing_submissions":234,"explicit_arxiv_matches":34,"examples":[{"id":"ncOxUOWsiS","title":"Frequency-Steered VAEs: Restoring Fine Details in Low-Resolution Latents","forum_url":"https://openreview.net/forum?id=ncOxUOWsiS","excerpt":"coder for efficient high-resolution diffusion models, 2025. URL https://arxiv.org/abs/2410.10733. Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso. Towards automated circuit discovery for mechanistic interpretability. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL https: //openreview.net/forum?id=89ia77nZ8u. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. I","arxiv_match":false},{"id":"WmdhapRKFO","title":"Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits","forum_url":"https://openreview.net/forum?id=WmdhapRKFO","excerpt":"eurips.cc/paper_files/paper/2025/hash/ dd37fdb24a4e1cfa3ed5c247217a7394-Abstract-Conference.html. Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso. Towards automated circuit discovery for mechanistic interpretability. In Advances in Neural Information Processing Systems, volume 36, pp. 16318–16352, 2023. doi: 10.52202/075280-0719. URL https://proceedings.neurips.cc/paper_files/paper/2023/hash/ 34e1dbe95d34d7ebaf9","arxiv_match":false},{"id":"CONFJT1Bkx","title":"Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning","forum_url":"https://openreview.net/forum?id=CONFJT1Bkx","excerpt":"5-7. doi: 10.18653/v1/2025.findings-naacl.170. URL https: //aclanthology.org/2025.findings-naacl.170/. Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià GarrigaAlonso. Towards automated circuit discovery for mechanistic interpretability. Advances in Neural Information Processing Systems, 36:16318–16352, 2023. Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Las","arxiv_match":false}],"id":"2304.14997","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":456},{"title":"RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning","arxiv_id":"2504.20073","year":2025,"url":"https://arxiv.org/abs/2504.20073","citing_submissions":233,"explicit_arxiv_matches":210,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"ing Systems, 38:172871–172933, 2026a. Pan Wang, Yihao Hu, Xiujin Liu, Jingchu Yang, Hang Wang, and Zhihao Wen. Atlasva: Selfevolving visual skill memory for teacher-free vlm agents. arXiv preprint arXiv:2605.17933, 2026b. Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, et al. Ragen: Understanding self-evolution in llm agents via multi-turn reinforcement learning. arXiv preprint arXiv:2504.20073, 2025. Zixuan Wang, Yuchen Yan, Hongxing Li, Teng Pan, Dingming Li, Ruiqing Zhang, Weiming Lu, Jun ","arxiv_match":true},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":": An Open Platform for AI Software Developers as Generalist Agents. In International Conference on Learning Representations, 2025b. URL https://arxiv.org/abs/2407.16741. Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, et al. RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning. arXiv preprint arXiv:2504.20073, 2025c. URL https://arxiv.org/abs/2504.20073v2. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, ","arxiv_match":true},{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":"g, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, and Manling Li. RAGEN: understanding self-evolution in LLM agents via multi-turn reinforcement learning. CoRR, abs/2504.20073, 2025. doi: 10.48550/ ARXIV.2504.20073. URL https://doi.org/10.48550/arXiv.2504.20073. Tianxin Wei, Ting-Wei Li, Zhining Liu, Xuying Ning, Ze Yang, Jiaru Zou, Zhichen Zeng, Ruiz","arxiv_match":false}],"id":"2504.20073","metadata_source":"Cached arXiv API metadata","rank":457},{"title":"Sdedit: Guided image synthesis and editing with stochastic differential equations","arxiv_id":"2108.01073","year":2021,"url":"https://arxiv.org/abs/2108.01073","citing_submissions":232,"explicit_arxiv_matches":78,"examples":[{"id":"oq0BXtA19v","title":"Test-Time Compositional Generalization in Diffusion Models via Prototype Discovery","forum_url":"https://openreview.net/forum?id=oq0BXtA19v","excerpt":"the mean of a normal population. Bulletin of the International Statistical Institute, 38(4):181–188, 1961. Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations, 2022. George L. Nemhauser, Laurence A. Wolsey, and Marshall L. Fisher. An analysis of approximations for maximizing submodular set functions—","arxiv_match":false},{"id":"SiF9YBf3x7","title":"Situated Inference: Adapting Frozen Generative Speech Enhancement under Acoustic Distribution Shift","forum_url":"https://openreview.net/forum?id=SiF9YBf3x7","excerpt":"Based on Optimal Smoothing and Minimum Statistics. IEEE Transactions on Speech and Audio Processing, 2001. Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. In International Conference on Learning Representations, 2022. Eloi Moliner, Jean-Marie Lemercier, Simon Welker, Timo Gerkmann, and Vesa Välimäki. BUDDy: Single-Channel Blind Unsupervised Dereverb","arxiv_match":false},{"id":"ugeUCOyVn0","title":"FlowTalker: Causal Streaming Audio-Driven 3D Head Animation via Factorized Motion Modeling with Rectified Flow","forum_url":"https://openreview.net/forum?id=ugeUCOyVn0","excerpt":"2, 2024a. Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li, Zhifu Gao, Shiliang Zhang, and Xie Chen. emotion2vec: Self-supervised pre-training for speech emotion representation. In Findings of the Association for Computational Linguistics: ACL 2024, pp. 15747–15760, 2024b. Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021. Federico Nocentini, Thomas Besnier, Claudio Ferrari, Sylvain Arguillere, Stefano Berretti, a","arxiv_match":true}],"id":"2108.01073","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":458},{"title":"Zep: A Temporal Knowledge Graph Architecture for Agent Memory","arxiv_id":"2501.13956","year":2025,"url":"https://arxiv.org/abs/2501.13956","citing_submissions":232,"explicit_arxiv_matches":216,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum? id=Z7Rhzk13NT. Qwen. Qwen3-Embedding-8B. https://huggingface.co/Qwen/ Qwen3-Embedding-8B, 2025. Accessed: 2026-05-01. Qwen. Qwen3.8-27B. https://huggingface.co/Qwen/Qwen3.8-27B, 2026. Accessed: 2026-08-30. Preston Rasmussen, Pavlo Paliychuk, Travis Beauvais, Jack Ryan, and Daniel Chalef. Zep: A temporal knowledge graph architecture for agent memory. arXiv preprint arXiv:2501.13956, 2025. Rana Salama, Jason Cai, Michelle Yuan, Anna Currey, Monica Sunkara, Yi Zhang, and Yassine Be","arxiv_match":true},{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":"asses. IEEE Transactions on Visualization and Computer Graphics, 2025. Qwen Team. Qwen3.6-27B: Flagship-level coding in a 27B dense model, April 2026. URL https://qwen.ai/blog?id=qwen3.6-27b. Toby Perrett et al. Hd-epic: A highly-detailed egocentric video dataset. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 23901–23913, 2025. Preston Rasmussen et al. Zep: a temporal knowledge graph architecture for agent memory. arXiv preprint arXiv:2501.13956, 2025. Xubin Ren et al. Videorag: Retrieval-augmented generation with extreme long-context videos. ","arxiv_match":true},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":"en Team. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024. doi: 10.48550/ arXiv.2412.15115. URL https://arxiv.org/abs/2412.15115. Qwen Team. Qwen3.8-Max: A new bar for coding and cowork, August 2026. URL https: //qwen.ai/blog?id=qwen3.8. Preston Rasmussen, Pavlo Paliychuk, Travis Beauvais, Jack Ryan, and Daniel Chalef. Zep: A temporal knowledge graph architecture for agent memory. arXiv preprint arXiv:2501.13956, 2025. URL https://arxiv.org/abs/2501.13956. Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zet","arxiv_match":true}],"id":"2501.13956","metadata_source":"Cached arXiv API metadata","rank":459},{"title":"ToolSandbox: A stateful, conversational, interactive evaluation benchmark for LLM tool use capabilities","arxiv_id":"2408.04682","year":2024,"url":"https://arxiv.org/abs/2408.04682","citing_submissions":231,"explicit_arxiv_matches":53,"examples":[{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"ses in long-term memory, 2026. URL https://arxiv.org/ abs/2606.17328. Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Haoping Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, et al. Toolsandbox: A stateful, conversational, interactive evaluation benchmark for llm tool use capabilities. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 1160–1183, 2025. Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. Evalu","arxiv_match":false},{"id":"EUiwYKQQVt","title":"When External Memory Becomes Agent State: Recorded Lineage and Controlled Reuse in LLM Agents","forum_url":"https://openreview.net/forum?id=EUiwYKQQVt","excerpt":", 2026. URL https://arxiv.org/abs/ 2606.24322. Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Haoping Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, and Ruoming Pang. ToolSandbox: A stateful, conversational, interactive evaluation benchmark for LLM tool use capabilities. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 1160–1183. Association for Computational Linguistics, 2025. doi: 10.18653/v1/2025.findings-naacl.65. Chang Ma, Junlei Zh","arxiv_match":false},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"ch: Evaluating llms as agents. arXiv preprint arXiv:2308.03688, 2023. Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Haoping Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, et al. Toolsandbox: A stateful, conversational, interactive evaluation benchmark for llm tool use capabilities. In Proceedings of Findings of the Association for Computational Linguistics: NAACL 2025, pp. 1160–1183, 2025. Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Chengcheng Han, Qi Gu, Xunliang Cai, Weiming Lu, Ju","arxiv_match":false}],"id":"2408.04682","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":460},{"title":"Towards accurate generative models of video: A new metric & challenges","arxiv_id":"1812.01717","year":2018,"url":"https://arxiv.org/abs/1812.01717","citing_submissions":231,"explicit_arxiv_matches":224,"examples":[{"id":"Zf68SX9abN","title":"S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation","forum_url":"https://openreview.net/forum?id=Zf68SX9abN","excerpt":"Kiwhan Song, Boyuan Chen, Max Simchowitz, Yilun Du, Russ Tedrake, and Vincent Sitzmann. History-guided video diffusion. In International Conference on Machine Learning, 2025. URL https://arxiv.org/abs/2502.06764. Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. Towards accurate generative models of video: A new metric and challenges. arXiv preprint arXiv:1812.01717, 2018. URL https://arxiv.org/abs/1812.01717. Gregory K. Wallace. The JPEG still picture compression standard. Communications of the ACM, 34(4):","arxiv_match":true},{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"hrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, et al. DINOv3. arXiv:2508.10104, 2025. Jaskirat Singh, Boyang Zheng, Zongze Wu, Richard Zhang, Eli Shechtman, and Saining Xie. Improved baselines with representation autoencoders. arXiv:2605.18324, 2026. Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. Towards accurate generative models of video: A new metric and challenges. arXiv:1812.01717, 2018. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Luk","arxiv_match":true},{"id":"ncOxUOWsiS","title":"Frequency-Steered VAEs: Restoring Fine Details in Low-Resolution Latents","forum_url":"https://openreview.net/forum?id=ncOxUOWsiS","excerpt":"g/2023. acl-long.310/. Rui Tian, Qi Dai, Jianmin Bao, Kai Qiu, Yifan Yang, Chong Luo, Zuxuan Wu, and Yu-Gang Jiang. Reducio! generating 1k video within 16 seconds using extremely compressed motion latents, 2025. URL https://arxiv.org/abs/2411.13552. Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. Towards accurate generative models of video: A new metric challenges, 2019. URL https://arxiv.org/abs/1812.01717. Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao,","arxiv_match":true}],"id":"1812.01717","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":461},{"title":"Deepseekmoe: Towards ultimate expert specialization in mixture-ofexperts language models","arxiv_id":"2401.06066","year":2024,"url":"https://arxiv.org/abs/2401.06066","citing_submissions":230,"explicit_arxiv_matches":67,"examples":[{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":"stin, Sholto Douglas, Roy Frostig, Anselm Levskaya, Charlie Chen, Sharad Vikram, Federico Lebron, Peter Choy, Vinay Ramasesh, Albert Webson, and Reiner Pope. How to scale your model. Online, 2025. URL https://jax-ml.github.io/scaling-book/. Damai Dai, Chengqi Deng, Chenggang Zhao, R. X. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, et al. DeepSeekMoE: Towards ultimate expert specialization in mixture-ofexperts language models. arXiv preprint arXiv:2401.06066, 2024. DeepSeek-AI. DeepSeek-V3 technical report, 2025b. URL https://arxiv.org/abs/2412. 19437v2. D","arxiv_match":true},{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"ai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, and Wenfeng Liang. DeepSeekMoE: Towards ultimate expert specialization in mixtureof-experts language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1280–1297, 2024. URL https: //aclanthology.org/2024.acl-long.70/. Nan Du, Yanp","arxiv_match":false},{"id":"vSjw3bWmyF","title":"Who Contributes? Counterfactual Attribution in Sparse Mixture-of-Experts","forum_url":"https://openreview.net/forum?id=vSjw3bWmyF","excerpt":"ai, Chengqi Deng, Chenggang Zhao, R.X. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.K. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, and Wenfeng Liang. DeepSeekMoE: Towards ultimate expert specialization in mixture-ofexperts language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1280–1297. Association for Computational Linguistics, 2024. doi: 10.18653/v1/2","arxiv_match":false}],"id":"2401.06066","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":462},{"title":"Medusa: Simple LLM inference acceleration framework with multiple decoding heads","arxiv_id":"2401.10774","year":2024,"url":"https://arxiv.org/abs/2401.10774","citing_submissions":230,"explicit_arxiv_matches":87,"examples":[{"id":"e9RG7RBDSX","title":"Cadence: Regulating the Speculation Externality in LLM Serving","forum_url":"https://openreview.net/forum?id=e9RG7RBDSX","excerpt":" Symposium on Operating Systems Design and Implementation (OSDI 23), pp. 623–643. USENIX Association, 2023. Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, and Tri Dao. Medusa: Simple LLM inference acceleration framework with multiple decoding heads. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 5209–5235. PMLR, 2024. Charlie Chen, Sebastian Borgeaud, Geoffrey","arxiv_match":false},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"g/introducing-system-one-models-and-jev. TypeSafe AI Blog. Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Lena Trigg, Ali Subhan, Muhammad Ali, and Dean F Hougen. Refining over resampling: Test-time self-correction for llm reasoning. arXiv preprint arXiv:2608.05643, 2026. Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D Lee, Deming Chen, and Tri Dao. Medusa: Simple llm inference acceleration framework with multiple decoding heads. arXiv preprint arXiv:2401.10774, 2024. Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and","arxiv_match":true},{"id":"ie4ipFVb04","title":"LAM++: Enhanced LocateAnything via High-Resolution Post-Training and Geometry-Aware Speculative Decoding","forum_url":"https://openreview.net/forum?id=ie4ipFVb04","excerpt":"e, et al. Qwen3-vl technical report. arXiv preprint arXiv:2511.21631, 2025. Aviraj Bevli, Sofian Chaybouti, Yasser Dahou, Hakim Hacid, Ngoc Dung Huynh, Phuc H Le Khac, Sanath Narayan, Wamiq Reyaz Para, and Ankit Singh. Falcon perception. arXiv preprint arXiv:2603.27365, 2026. Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D Lee, Deming Chen, and Tri Dao. Medusa: Simple llm inference acceleration framework with multiple decoding heads. arXiv preprint arXiv:2401.10774, 2024. Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and ","arxiv_match":true}],"id":"2401.10774","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":463},{"title":"Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains","arxiv_id":"2507.17746","year":2025,"url":"https://arxiv.org/abs/2507.17746","citing_submissions":230,"explicit_arxiv_matches":98,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"os Tsimpourlas, Michael Sharman, Meghan Shah, Andrea Vallone, Alex Beutel, Johannes Heidecke, and Karan Singhal. HealthBench: Evaluating large language models towards improved human health, 2025. URL https://arxiv.org/abs/2505.08775. Anisha Gunjal, Anthony Wang, Elaine Lau, Vaskar Nath, Yunzhong He, Bing Liu, and Sean Hendryx. Rubrics as rewards: Reinforcement learning beyond verifiable domains. arXiv preprint arXiv:2507.17746, 2025. URL https://arxiv.org/abs/2507.17746. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and","arxiv_match":true},{"id":"O0MnDF6uMA","title":"Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents","forum_url":"https://openreview.net/forum?id=O0MnDF6uMA","excerpt":"ds.), International Conference on Learning Representations (ICLR), volume 2026, pp. 107331–107352, 2026. Anisha Gunjal, Anthony Wang, Elaine Lau, Vaskar Nath, Yunzhong He, Bing Liu, and Sean Hendryx. Rubrics as rewards: Reinforcement learning beyond verifiable domains. In International Conference on Learning Representations (ICLR), volume 2026, pp. 127924–127945, 2026. Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiaw","arxiv_match":false},{"id":"mqy5nGDnra","title":"SIGHT: Compositional Skill Routing for Instance-Specific Rubric Generation across Heterogeneous Multimodal Tasks","forum_url":"https://openreview.net/forum?id=mqy5nGDnra","excerpt":" reward models for RLHF. In International Conference on Learning Representations, pp. 18128–18163, 2025. Anisha Gunjal, Anthony Wang, Elaine Lau, Vaskar Nath, Yunzhong He, Bing Liu, and Sean Hendryx. Rubrics as Rewards: Reinforcement learning beyond verifiable domains. In International Conference on Learning Representations, pp. 127924–127945, 2026. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao B","arxiv_match":false}],"id":"2507.17746","metadata_source":"Cached arXiv API metadata","rank":464},{"title":"KVQuant: Towards 10 million context length LLM inference with KV cache quantization","arxiv_id":"2401.18079","year":2024,"url":"https://arxiv.org/abs/2401.18079","citing_submissions":229,"explicit_arxiv_matches":39,"examples":[{"id":"mhr6MQbs2E","title":"CheeSe : Channel Permutation for Semi-structured KV Cache Pruning","forum_url":"https://openreview.net/forum?id=mhr6MQbs2E","excerpt":"lex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W Mahoney, Yakun S Shao, Kurt Keutzer, and Amir Gholami. Kvquant: Towards 10 million context length llm inference with kv cache quantization. Advances in Neural Information Processing Systems, 37:1270–1303, 2024. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. Ruler: Wha","arxiv_match":false},{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"er_files/paper/ 2022/file/127179162bfe4c422325ee7d05ad9cd8-Paper-Conference.pdf. Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, and Amir Gholami. Kvquant: Towards 10 million context length llm inference with kv cache quantization. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 1270–1303. Curran Associates, Inc., 202","arxiv_match":false},{"id":"PCAODQ61Qw","title":"BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation","forum_url":"https://openreview.net/forum?id=PCAODQ61Qw","excerpt":"entification. Advances in Neural Information Processing Systems, 37:68287–68307, 2024. Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W Mahoney, Yakun S Shao, Kurt Keutzer, and Amir Gholami. Kvquant: Towards 10 million context length llm inference with kv cache quantization. Advances in Neural Information Processing Systems, 37:1270–1303, 2024. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. Ruler: Wha","arxiv_match":false}],"id":"2401.18079","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":465},{"title":"GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning","arxiv_id":"2507.01006","year":2025,"url":"https://arxiv.org/abs/2507.01006","citing_submissions":228,"explicit_arxiv_matches":218,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"ui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, et al. InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency. arXiv preprint arXiv:2508.18265, 2025. URL https://arxiv.org/abs/2508.18265. V Team, Wenyi Hong, Wenmeng Yu, et al. GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning. arXiv preprint arXiv:2507.01006, 2025. URL https://arxiv.org/abs/2507.01006. Ziyi Wang, Xumin Yu, Yongming Rao, Yonggen Ling, Yunheng Li, Oran Wang, Mingqi Gao, Yuchen Zhou, Y","arxiv_match":true},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"ing Wang, Yadong Xue, Yanfeng Wang, Yanzi Wang, Yifan An, Yifan Du, Yiming Shi, Yiheng Huang, Yilin Niu, Yuan Wang, Yuanchang Yue, Yuchen Li, Yutao Zhang, Yuting Wang, Yu Wang, Yuxuan Zhang, Zhao Xue, Zhenyu Hou, Zhengxiao Du, Zihan Wang, Peng Zhang, Debing Liu, Bin Xu, Juanzi Li, Minlie Huang, Yuxiao Dong, and Jie Tang. Glm-4.5v and glm-4.1v-thinking: Towards versatile multimodal reasoning with scalable reinforcement learning, 2025. URL https://arxiv.org/abs/2507.01006. Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikh","arxiv_match":true},{"id":"YzI2kFui6F","title":"CoMo: Efficient Video Transformer via Codec Residual and Motion-Field Geometry","forum_url":"https://openreview.net/forum?id=YzI2kFui6F","excerpt":" Motionbench: Benchmarking and improving fine-grained video motion understanding for vision language models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8450–8460. IEEE, 2025a. Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang, Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, et al. Glm-4.5 v and glm-4.1 v-thinking: Towards versatile multimodal reasoning with scalable reinforcement learning. arXiv preprint arXiv:2507.01006, 2025b. Berthold KP Horn and Brian G Schunck. Determining optical flow. Artificial intelligence, 17","arxiv_match":true}],"id":"2507.01006","metadata_source":"Cached arXiv API metadata","rank":466},{"title":"Socialiqa: Commonsense reasoning about social interactions","arxiv_id":"1904.09728","year":2019,"url":"https://arxiv.org/abs/1904.09728","citing_submissions":227,"explicit_arxiv_matches":34,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":", and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106, 2021. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. Social iqa: Commonsense reasoning about social interactions. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp. 4463–4473, 201","arxiv_match":false},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"ne Calderara. Transporting task vectors across different architectures without training. arXiv preprint arXiv:2602.12952, 2026. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. Social iqa: Commonsense reasoning about social interactions. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp. 4463–4473, 201","arxiv_match":false},{"id":"ToTuhzZPcv","title":"How Does Loss Landscape Geometry Transfer Across Scales?","forum_url":"https://openreview.net/forum?id=ToTuhzZPcv","excerpt":"ltitask prompted training enables zero-shot task generalization. In International Conference on Learning Representations, 2022. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. Social IQA: Commonsense reasoning about social interactions. In Proceedings of EMNLP-IJCNLP, pp. 4463–4473, 2019. Alexander Shevchenko and Marco Mondelli. Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In","arxiv_match":false}],"id":"1904.09728","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":467},{"title":"Very deep convolutional networks for large-scale image recognition","arxiv_id":"1409.1556","year":2014,"url":"https://arxiv.org/abs/1409.1556","citing_submissions":227,"explicit_arxiv_matches":109,"examples":[{"id":"rqKuKaFBIK","title":"MeshSplatBench: A Unified Benchmark for Triangle- and Mesh-Based Neural Rendering","forum_url":"https://openreview.net/forum?id=rqKuKaFBIK","excerpt":"r Vision, pp. 14335–14345, 2024. Kaifeng Sheng, Zheng Zhou, Yingliang Peng, and Qianwei Wang. 2D triangle splatting for direct differentiable mesh training, 2025. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015. Kenji Tojo, Bernd Bickel, and Nobuyuki Umetani. Diffsoup: Direct differentiable rasterization of triangle soup for extreme radiance fie","arxiv_match":false},{"id":"KY19PuvbEj","title":"Fragility-Aware Noise Training for Robust Split Computing over Noisy Channels","forum_url":"https://openreview.net/forum?id=KY19PuvbEj","excerpt":", 23(5):4141–4154, 2023. Mohammad Q. Shatnawi, Qusai Abuein, and Romesaa Al-Quraan. Deep learning-based approach to diagnose lung cancer using ct-scan images. Intelligence-Based Medicine, 11:100188, 2025. ISSN 2666-5212. doi: https://doi.org/10.1016/j.ibmed.2024.100188. URL https://www. sciencedirect.com/science/article/pii/S2666521224000553. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. Daouda Sow, Sen Lin, Zhangyang Wang, and Yingbin Liang. Doubly robust instance-reweighted ad","arxiv_match":true},{"id":"StuW6HvvPT","title":"Diagnosing Pixel-Space Diffusion Transformers: From Depth-Wise Connectivity to Layer-Wise Attention Patterns","forum_url":"https://openreview.net/forum?id=StuW6HvvPT","excerpt":" image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10684–10695, 2022. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy, Artem Babenko, and Dmitry Baranchuk. R","arxiv_match":true}],"id":"1409.1556","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":468},{"title":"AgentHarm: A benchmark for measuring harmfulness of LLM agents","arxiv_id":"2410.09024","year":2024,"url":"https://arxiv.org/abs/2410.09024","citing_submissions":226,"explicit_arxiv_matches":51,"examples":[{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":" PA, 2006. Reporter: Deborah A. DeMott. Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian, Derek Duenas, Maxwell Lin, Justin Wang, Dan Hendrycks, Andy Zou, Zico Kolter, Matt Fredrikson, et al. Agentharm: A benchmark for measuring harmfulness of llm agents. In International Conference on Learning Representations, volume 2025, pp. 79185–79220, 2025. Anthropic. How we built Claude Code auto mode: a safer way to skip permissions. https: //www.anthropic.co","arxiv_match":false},{"id":"y89ckFndSX","title":"HazardAuditor: From Executable Threats to Safer Computer-Use Agents","forum_url":"https://openreview.net/forum?id=y89ckFndSX","excerpt":" arXiv preprint arXiv:2303.08774, 2023. Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian, Derek Duenas, Maxwell Lin, Justin Wang, Dan Hendrycks, Andy Zou, Zico Kolter, Matt Fredrikson, et al. Agentharm: A benchmark for measuring harmfulness of llm agents. In International Conference on Learning Representations, volume 2025, pp. 79185–79220, 2025. Anthropic. Claude code. https://www.anthropic.com/claude-code, 2025. Antônio João Azambuja, Marcos Guil","arxiv_match":false},{"id":"Uowq0ts0EJ","title":"AdaGuard: An Adaptive Guard Model with User-defined Policies","forum_url":"https://openreview.net/forum?id=Uowq0ts0EJ","excerpt":" Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian, Derek Duenas, Maxwell Lin, Justin Wang, Dan Hendrycks, Andy Zou, Zico Kolter, Matt Fredrikson, et al. Agentharm: A benchmark for measuring harmfulness of llm agents. In International Conference on Learning Representations, volume 2025, pp. 79185–79220, 2025. Convai Innovations. Laya: A multilingual, non-autoregressive system 1 decision engine. GitHub repository,","arxiv_match":false}],"id":"2410.09024","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":469},{"title":"Holistic evaluation of language models","arxiv_id":"2211.09110","year":2022,"url":"https://arxiv.org/abs/2211.09110","citing_submissions":226,"explicit_arxiv_matches":101,"examples":[{"id":"W1xeiKanFu","title":"Do Correct Answers Have Stable Latent Knowledge Support?","forum_url":"https://openreview.net/forum?id=W1xeiKanFu","excerpt":"Inger, Yehonatan Elisha, Bracha Shapira, Lior Rokach, and Seffi Cohen. Forget what you know about llms evaluations-llms are like a chameleon. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 21675–21688, 2025. Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110, 2022. Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan ","arxiv_match":true},{"id":"8zRnR4jkT9","title":"HealthCoreBench: Measuring Evaluation Artifacts for Reliable Medical LLM Evaluation","forum_url":"https://openreview.net/forum?id=8zRnR4jkT9","excerpt":"ttps://www.nature.com/articles/ sdata2018251. Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. Holistic evaluation of language models. Transactions on Machine Learning Research, 2023. URL https://openreview. net/forum?id=iO4LZibEqW. Bo Liu, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang, and Xiao-Ming Wu. SLAKE: A semanticallylabeled knowle","arxiv_match":false},{"id":"fFJ4m1Ru9U","title":"Hypothesis Testing with Conditional Queries: Learnability and the Value of Interaction","forum_url":"https://openreview.net/forum?id=fFJ4m1Ru9U","excerpt":"pp. 648–657, 2016. doi: 10.18653/v1/D16-1062. Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. Holistic evaluation of language models. Transactions on Machine Learning Research, 2023. Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin. tinyBenchmarks: Evaluating LLMs with fewer examples. In","arxiv_match":false}],"id":"2211.09110","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":470},{"title":"Kimi k1.5: Scaling Reinforcement Learning with LLMs","arxiv_id":"2501.12599","year":2025,"url":"https://arxiv.org/abs/2501.12599","citing_submissions":226,"explicit_arxiv_matches":202,"examples":[{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":"Renren Jin, Pengzhi Gao, Yuqi Ren, Zhuowen Han, Tongxuan Zhang, Wuwei Huang, Wei Liu, Jian Luan, and Deyi Xiong. Revisiting entropy in reinforcement learning for large reasoning models. In Findings of the Association for Computational Linguistics: ACL 2026. Association for Computational Linguistics, 2026. Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, et al. Kimi k1.5: Scaling reinforcement learning with LLMs. arXiv preprint arXiv:2501.12599, 2025. James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andre","arxiv_match":true},{"id":"1Kxk30PvqF","title":"From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery","forum_url":"https://openreview.net/forum?id=1Kxk30PvqF","excerpt":"ic systems. In International Conference on Learning Representations, volume 2025, pp. 21344–21377, 2025. Jenny Y Huang, Mehul Damani, Yousef El-Kurdi, Ramon Astudillo, and Wei Sun. Latency and token-aware test-time compute. arXiv preprint arXiv:2509.09864, 2025. Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. Kimi k1.5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501.12599, 2025. Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, and Chelsea Finn. Meta-h","arxiv_match":true},{"id":"hVl6H6DnYO","title":"Consistency as an Inductive Bias: Mitigating Cross-View Reliability Gap in MLLMs","forum_url":"https://openreview.net/forum?id=hVl6H6DnYO","excerpt":"soning capability in multimodal large language models. In International Conference on Learning Representations, volume 2026, pp. 63794–63812, 2026b. Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6700–6709, 2019. Kimi Team. Kimi k1.5: Scaling reinforcement learning with LLMs. arXiv preprint arXiv:2501.12599, 2025. Yann LeCun. Generalization and network design strategies. In R. Pfeifer, Z. Schreter, F. Fog","arxiv_match":true}],"id":"2501.12599","metadata_source":"Cached arXiv API metadata","rank":471},{"title":"Text embeddings by weakly-supervised contrastive pre-training","arxiv_id":"2212.03533","year":2022,"url":"https://arxiv.org/abs/2212.03533","citing_submissions":226,"explicit_arxiv_matches":207,"examples":[{"id":"Y15G7fdvPv","title":"S$^2$Aligner: Pair-Efficient Transferable Pre-Training for Sparse Text-Attributed Graphs","forum_url":"https://openreview.net/forum?id=Y15G7fdvPv","excerpt":"izing data using t-sne. Journal of Machine Learning Research, 9(86):2579–2605, 2008. URL http://jmlr.org/papers/v9/ vandermaaten08a.html. Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. stat, 1050:21, 2018. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep self-atten","arxiv_match":true},{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"haran Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C Machado, Pablo Samuel Castro, and Nicolas Le Roux. A general class of surrogate functions for stable and efficient reinforcement learning. arXiv preprint arXiv:2108.05828, 2022. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhif","arxiv_match":true},{"id":"Xt9oJoQrHM","title":"Light-DiffRetriever: Budget-Flexible Retrieval with Diffusion Language Models via Matryoshka Representative Tokens","forum_url":"https://openreview.net/forum?id=Xt9oJoQrHM","excerpt":"rk for zero-shot evaluation of information retrieval models. In Advances in Neural Information Processing Systems Datasets and Benchmarks Track, 2021. URL https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ 65b9eea6e1cc6bb9f0cd2a47751a186f-Abstract-round2.html. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. doi: 10.48550/arXiv.2212.03533. URL https://dblp.org/rec/journals/corr/ abs-2212-03533. Lian","arxiv_match":true}],"id":"2212.03533","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":472},{"title":"Longformer: The long-document transformer","arxiv_id":"2004.05150","year":2020,"url":"https://arxiv.org/abs/2004.05150","citing_submissions":225,"explicit_arxiv_matches":211,"examples":[{"id":"XIvhtY214J","title":"SOLAR: SVD-Optimized Lifelong Attention for Recommendation","forum_url":"https://openreview.net/forum?id=XIvhtY214J","excerpt":" Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer: The long-document transformer, 2020. URL https://arxiv.org/abs/2004.05150. Zheng Chai, Qin Ren, Xijun Xiao, Huizhi Yang, Bo Han, Sijun Zhang, Di Chen, Hui Lu, Wenlin Zhao, L","arxiv_match":true},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"gqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. LongBench v2: Towards deeper understanding and reasoning on realistic long-context multitasks. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3639–3664, 2025. Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020. Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients th","arxiv_match":true},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"ssed: 2026-06-03. Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pp. 3119–3137, 2024. Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020. Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, and Jiaya Jia. Long","arxiv_match":true}],"id":"2004.05150","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":473},{"title":"SkillOpt: Executive Strategy for Self-Evolving Agent Skills","arxiv_id":"2605.23904","year":2026,"url":"https://arxiv.org/abs/2605.23904","citing_submissions":225,"explicit_arxiv_matches":194,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":" Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, et al. Embodiedbench: Comprehensive benchmarking multi-modal large language models for vision-driven embodied agents. arXiv preprint arXiv:2502.09560, 2025. Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei Gao, Qi Dai, Bei Liu, et al. Skillopt: Executive strategy for self-evolving agent skills. arXiv preprint arXiv:2605.23904, 2026. Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Yifei Zhou, Alane Suhr, Saini","arxiv_match":true},{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":"s. https://trinkle23897.github.io/learningbeyond-gradients/, May 2026. Blog post. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-MEM: Agentic memory for LLM agents. Advances in Neural Information Processing Systems, 38:17577–17604, 2026. Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei Gao, Qi Dai, Bei Liu, et al. SkillOpt: Executive strategy for self-evolving agent skills. arXiv preprint arXiv:2605.23904, 2026. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and ","arxiv_match":true},{"id":"GGUmZybpbd","title":"AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?","forum_url":"https://openreview.net/forum?id=GGUmZybpbd","excerpt":"h bidirectional evolutionary search. arXiv preprint arXiv:2605.28814, 2026. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-mem: Agentic memory for llm agents. In Proc. NeurIPS, 2025. Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei Gao, Qi Dai, Bei Liu, Kai Qiu, Yuqing Yang, Dongdong Chen, Xue Yang, and Chong Luo. Skillopt: Executive strategy for self-evolving agent skills. arXiv preprint arXiv:2605.23904, 2026a. Yutao Yang, Junsong Li, Qianjun Pan, Bihao Zhan, Yuxuan Cai, Lin Du, Jie Zhou, Kai Chen, Qi","arxiv_match":true}],"id":"2605.23904","metadata_source":"Cached arXiv API metadata","rank":474},{"title":"SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks","arxiv_id":"2602.12670","year":2026,"url":"https://arxiv.org/abs/2602.12670","citing_submissions":225,"explicit_arxiv_matches":215,"examples":[{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":" Qizheng Zhang, Changxiu Ji, Qiuyang Mang, Xiaokun Chen, Lakshya A Agrawal, Wei-Liang Liao, Eric Yang, Alvin Cheung, et al. Combee: Scaling prompt learning for self-improving language model agents. arXiv preprint arXiv:2604.04247, 2026a. Xiangyi Li, Yimin Liu, Wenbo Chen, Bingran You, Zonglin Di, Yifeng He, Shenghan Zheng, Kyoung Whan Choe, Jiankai Sun, Shuyi Wang, et al. SkillsBench: Benchmarking how well agent skills work across diverse tasks. arXiv preprint arXiv:2602.12670, 2026b. Zeyao Ma, Bohan Zhang, Jing Zhang, Jifan Yu, Xiaokang Zhang, Xiaohan Zhang, Sijia Luo, Xi W","arxiv_match":true},{"id":"69eDEysdQF","title":"Can Image Agents Complete Visual Commissions? A Benchmark and Modular Framework","forum_url":"https://openreview.net/forum?id=69eDEysdQF","excerpt":"an Xu, Zekai Zhang, Zhendong Wang, Zihao Liu, Zikai Zhou, Hongzhu Shi, Yi Wang, Bing Zhao, Hu Wei, Lin Qu, and Chenfei Wu. Qwen-image-bench: From generation to creation in text-to-image evaluation. arXiv preprint arXiv:2605.28091, 2026b. Xiangyi Li, Yimin Liu, Wenbo Chen, Bingran You, Zonglin Di, Yifeng He, Shenghan Zheng, Kyoung Whan Choe, Jiankai Sun, Shuyi Wang, et al. Skillsbench: Benchmarking how well agent skills work across diverse tasks. arXiv preprint arXiv:2602.12670, 2026c. Zongxia Li, Dawei Liu, Fuxiao Liu, Yuhang Zhou, Xiyang Wu, Jingxi Chen, Jing Xie, Xiaomin W","arxiv_match":true},{"id":"07pvXM8POP","title":"Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills","forum_url":"https://openreview.net/forum?id=07pvXM8POP","excerpt":"n Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. Api-bank: A comprehensive benchmark for tool-augmented llms. In Proceedings of the 2023 conference on empirical methods in natural language processing, pp. 3102–3116, 2023. Xiangyi Li, Wenbo Chen, Yimin Liu, Shenghan Zheng, Xiaokun Chen, Yifeng He, Yubo Li, Bingran You, Haotian Shen, Jiankai Sun, et al. Skillsbench: Benchmarking how well agent skills work across diverse tasks. arXiv preprint arXiv:2602.12670, 2026b. Yanchao Li, Wanhao Liu, Ben Gao, Jiaqing Xie, Zhehong Ai, Na Zou, Yuqiang Li, and Tianfan F","arxiv_match":true}],"id":"2602.12670","metadata_source":"Cached arXiv API metadata","rank":475},{"title":"A simple and effective pruning approach for large language models","arxiv_id":"2306.11695","year":2023,"url":"https://arxiv.org/abs/2306.11695","citing_submissions":224,"explicit_arxiv_matches":50,"examples":[{"id":"CzIctGOUNV","title":"PICO: Lossless Compression Stacked on Pruning and Quantization for Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=CzIctGOUNV","excerpt":"gwook Kim, and Dongsoo Lee. Encoding weights of irregular sparsity for fixed-to-fixed model compression. arXiv preprint arXiv:2105.01869, 2021. Mingjie Sun, Zhuang Liu, Anna Bair, and J. Zico Kolter. A simple and effective pruning approach for large language models. In The Twelfth International Conference on Learning Representations, 2024. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Pr","arxiv_match":false},{"id":"ZQOtaCagPN","title":"EviQ: Hierarchical Query-Conditioned Evidence Selection for Efficient VideoLLMs","forum_url":"https://openreview.net/forum?id=ZQOtaCagPN","excerpt":"ch for list-wise frame selection in video-llms. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 24090–24101, 2025. Mingjie Sun, Zhuang Liu, Anna Bair, and Zico Kolter. A simple and effective pruning approach for large language models. In International Conference on Learning Representations, volume 2024, pp. 4942–4964, 2024. Xi Tang, Jihao Qiu, Lingxi Xie, Yunjie Tian, Jianbin Jiao, and Qixiang Ye. Adaptive keyframe sampling for l","arxiv_match":false},{"id":"GI61L4n4Oz","title":"SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=GI61L4n4Oz","excerpt":"d Huan Wang. Rose: Reordered sparsegpt for more accurate one-shot large language models pruning. In Conference on Parsimony and Learning, 2026. Mingjie Sun, Zhuang Liu, Anna Bair, and J. Zico Kolter. A simple and effective pruning approach for large language models. In ICLR, 2024. Philippe Tillet, H. T. Kung, and David Cox. Triton: an intermediate language and compiler for tiled neural network computations. In MAPL, 2019. Huan Wang, Yulun Zhang, Can Qin, Luc Va","arxiv_match":false}],"id":"2306.11695","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":476},{"title":"Linear transformers are secretly fast weight programmers","arxiv_id":"2102.11174","year":2021,"url":"https://arxiv.org/abs/2102.11174","citing_submissions":224,"explicit_arxiv_matches":34,"examples":[{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"latent thoughts: On the power of looped transformers. In International Conference on Learning Representations, volume 2025, pp. 14855–14881, 2025. Imanol Schlag, Kazuki Irie, and Jürgen Schmidhuber. Linear transformers are secretly fast weight programmers. In Proceedings of the 38th International Conference on Machine Learning, pp. 9355–9366, 2021. Kristian Schwethelm, Daniel Rückert, and Georgios Kaissis. How much is one recurrence worth? iso-depth ","arxiv_match":false},{"id":"efYt84hV53","title":"Gap-Aware Predictive Memory for Test Time Training","forum_url":"https://openreview.net/forum?id=efYt84hV53","excerpt":"Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean. Efficiently scaling transformer inference. Proceedings of machine learning and systems, 5:606–624, 2023. Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732, 2015. Imanol Schlag, Kazuki Irie, and Jürgen Schmidhuber. Linear transformers are secretly fast weight programmers, 2021. URL https://arxiv.org/abs/2102.11174. Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt. Test-time training","arxiv_match":true},{"id":"N6ehRHGZfr","title":"STAMP: Learning Cross-Session Memory in Native Recurrent States","forum_url":"https://openreview.net/forum?id=N6ehRHGZfr","excerpt":"Simple hybrid state space models for efficient unlimited context language modeling. In International Conference on Learning Representations, 2025. Imanol Schlag, Kazuki Irie, and Jürgen Schmidhuber. Linear transformers are secretly fast weight programmers. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pp. 9355–9366. PMLR, 2021. Sainbayar Sukhbaatar, Arthur Szlam, Jason ","arxiv_match":false}],"id":"2102.11174","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":477},{"title":"Sparsevlm: Visual token sparsification for efficient vision-language model inference","arxiv_id":"2410.04417","year":2024,"url":"https://arxiv.org/abs/2410.04417","citing_submissions":224,"explicit_arxiv_matches":99,"examples":[{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":", 2026. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 11941–11952. IEEE, 2023. Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, et al. Sparsevlm: Visual token sparsification for efficient vision-language model inference. arXiv preprint arXiv:2410.04417, 2024. A ","arxiv_match":true},{"id":"Xdik5HW8VD","title":"WaLKPruner: Cognitively Inspired Visual Token Pruning for Spatial Understanding","forum_url":"https://openreview.net/forum?id=Xdik5HW8VD","excerpt":"nzhi Huang, Ellis Brown, Zihao Yang, Yue Yu, Shengbang Tong, Zihan Zheng, Yifan Xu, Muhan Wang, et al. Towards spatial supersensing in video. In The Fourteenth International Conference on Learning Representations, 2026. Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, et al. Sparsevlm: Visual token sparsification for efficient vision-language model inference. arXiv preprint arXiv:2410.04417, 2024. Duo Zheng, Shijia Huang, Yanyang Li, and Liwei Wang. Learning from videos for 3d world: Enha","arxiv_match":true},{"id":"iBCBaGASs6","title":"HEAT: Hierarchical Elastic Adaptive Token Sparsification via Depth Scaling","forum_url":"https://openreview.net/forum?id=iBCBaGASs6","excerpt":"rXiv preprint arXiv:2508.06084, 2025b. Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis A. Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, and Shanghang Zhang. Sparsevlm: Visual token sparsification for efficient vision-language model inference. In International Conference on Machine Learning, pp. 1–18, 2025c. Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, ","arxiv_match":false}],"id":"2410.04417","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":478},{"title":"A strongreject for empty jailbreaks","arxiv_id":"2402.10260","year":2024,"url":"https://arxiv.org/abs/2402.10260","citing_submissions":222,"explicit_arxiv_matches":48,"examples":[{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":"rg/abs/ 2402.03300. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in neural information processing systems, 36:8634–8652, 2023. Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, and Sam Toyer. A strongreject for empty jailbreaks, 2024. URL https://arxiv.org/abs/2402.10260. Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, and Moritz Hardt. Test-time train","arxiv_match":true},{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":"ymposium (USENIX Security 25), pp. 319–338, 2025. Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, et al. A strongreject for empty jailbreaks. Advances in Neural Information Processing Systems, 37:125416–125440, 2024. Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, Ian Ballantyne, Cormac Brick, Vi","arxiv_match":false},{"id":"gRDe98c41c","title":"The Model’s Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges","forum_url":"https://openreview.net/forum?id=gRDe98c41c","excerpt":"inions do language models reflect? In ICML, 2023. Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, et al. A strongreject for empty jailbreaks. In NeurIPS, 2024. Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 2008. David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine ","arxiv_match":false}],"id":"2402.10260","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":479},{"title":"Categorical reparameterization with gumbel-softmax","arxiv_id":"1611.01144","year":2016,"url":"https://arxiv.org/abs/1611.01144","citing_submissions":222,"explicit_arxiv_matches":69,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"i Jia, Yang Zhang, and Boris Ginsburg. RULER: What’s the real context size of your long-context language models? In First Conference on Language Modeling, 2024. Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with Gumbel-softmax. In International Conference on Learning Representations, 2017. URL https://openreview.n et/forum?id=rkE3y85ee. Simon Jégou, Maximilian Jeblick, Alessio Devoto, and David Austin. kvpress: LLM KV cach","arxiv_match":false},{"id":"mlS6HwwCm6","title":"SCOPE: Spectral-Conditioned Omic-Pathology Encoder for Interpretable Multimodal Cancer Survival Analysis","forum_url":"https://openreview.net/forum?id=mlS6HwwCm6","excerpt":"Tomczak, and Max Welling. Attention-based deep multiple instance learning. In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018. Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations (ICLR), 2017. Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. Self-normalizing neural networks. In Proceedings of th","arxiv_match":false},{"id":"iPvVOfpJOk","title":"RAMP: Reliability Adaptive Masking for Pretraining Noisy Multimodal Physiological Signals","forum_url":"https://openreview.net/forum?id=iPvVOfpJOk","excerpt":" Ye. A survey on information bottleneck. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8):5325–5344, 2024. doi: 10.1109/TPAMI.2024.3366349. Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with Gumbel-Softmax. In International Conference on Learning Representations, 2017. Published at ICLR 2017; arXiv preprint posted in 2016. Wei-Bang Jiang, Liming Zhao, and Bao-Liang Lu. Large brain model for learning ge","arxiv_match":false}],"id":"1611.01144","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":480},{"title":"Less: Selecting influential data for targeted instruction tuning","arxiv_id":"2402.04333","year":2024,"url":"https://arxiv.org/abs/2402.04333","citing_submissions":222,"explicit_arxiv_matches":63,"examples":[{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"n for Computational Linguistics. doi: 10.18653/v1/ 2023.acl-long.754. URL https://aclanthology.org/2023.acl-long.754/. Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen. LESS: Selecting influential data for targeted instruction tuning. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Proceedings of the 41st International Conference on Machine Learn","arxiv_match":false},{"id":"YedwDyhTVC","title":"Why Ghost Outputs Teach: A Kernel-Based Understanding of Subliminal Learning","forum_url":"https://openreview.net/forum?id=YedwDyhTVC","excerpt":". From data to behavior: Predicting unintended model behaviors before training. arXiv preprint arXiv:2602.04735, 2026. Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen. LESS: Selecting influential data for targeted instruction tuning. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=PG5fV50maR. Greg Yang and Edward J Hu. Tensor programs iv: Feature learning in infinite-width n","arxiv_match":false},{"id":"aZ4BU8bGIL","title":"Interaction-Aware Influence Functions for Group Attribution","forum_url":"https://openreview.net/forum?id=aZ4BU8bGIL","excerpt":"fficient fine-tuning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025. Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen. LESS: Selecting influential data for targeted instruction tuning. In International Conference on Machine Learning, 2024. Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint ","arxiv_match":false}],"id":"2402.04333","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":481},{"title":"Process Reinforcement through Implicit Rewards","arxiv_id":"2502.01456","year":2025,"url":"https://arxiv.org/abs/2502.01456","citing_submissions":222,"explicit_arxiv_matches":168,"examples":[{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":", Yuchen Fan, Tianyu Yu, Qixin Xu, Weize Chen, Jiarui Yuan, Huayu Chen, Kaiyan Zhang, Xingtai Lv, Shuo Wang, Yuan Yao, Xu Han, Hao Peng, Yu Cheng, Zhiyuan Liu, Maosong Sun, Bowen Zhou, and Ning Ding. Process reinforcement through implicit rewards. In Transactions on Machine Learning Research (TMLR), 2026. https://openreview.net/forum?id=9SkkifLopZ. Jasper Dekoninck, Maximilian Baader, and Martin Vechev. A unified approach to routing and casca","arxiv_match":false},{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":"al Conference on Computer Vision, pp. 4637–4646, 2025. Krzysztof J Cios, Witold Pedrycz, and Roman W Swiniarski. Data mining and knowledge discovery. In Data mining methods for knowledge discovery, pp. 1–26. Springer, 1998. Ganqu Cui, Lifan Yuan, Zefan Wang, Hanbin Wang, Wendi Li, Bingxiang He, Yuchen Fan, Tianyu Yu, Qixin Xu, Weize Chen, et al. Process reinforcement through implicit rewards. arXiv preprint arXiv:2502.01456v1, 2025a. URL https://arxiv.org/abs/2502.01456v1. Ganqu Cui, Yuchen Zhang, Jiacheng Chen, Lifan Yuan, Zhi Wang, Yuxin Zuo, Haozhan Li, Yuchen Fan, H","arxiv_match":true},{"id":"BTTuPGcoJL","title":"Learning from Correctness Trends: Sample-Wise Historical Advantage Shaping for Mathematical Reasoning","forum_url":"https://openreview.net/forum?id=BTTuPGcoJL","excerpt":", 38:131646–131671, 2026. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. Ganqu Cui, Lifan Yuan, Zefan Wang, Hanbin Wang, Yuchen Zhang, Jiacheng Chen, Wendi Li, Bingxiang He, Yuchen Fan, Tianyu Yu, et al. Process reinforcement through implicit rewards. arXiv preprint arXiv:2502.01456, 2025. DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learn","arxiv_match":true}],"id":"2502.01456","metadata_source":"Cached arXiv API metadata","rank":482},{"title":"Androidworld: A dynamic benchmarking environment for autonomous agents","arxiv_id":"2405.14573","year":2024,"url":"https://arxiv.org/abs/2405.14573","citing_submissions":219,"explicit_arxiv_matches":70,"examples":[{"id":"IMcQz8TBut","title":"DreamUI: Scaling Interactive Mobile Agent Environments with Generative UI World Model","forum_url":"https://openreview.net/forum?id=IMcQz8TBut","excerpt":"inthewild: A large-scale dataset for android device control. Advances in Neural Information Processing Systems, 36:59708–59728, 2023. Christopher Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz, Gabrielle Lau, Marybeth Fair, Alice Li, William Bishop, Wei Li, Folawiyo Campbell-Ajala, Daniel Toyama, Robert Berry, Divya Tyamagundlu, Timothy Lillicrap, and Oriana Riva. AndroidWorld: A dynamic benchmarking environment for autonomous agents. arXiv preprint arXiv:2405.14573, 2024. ByteDance Seed. Seed1.5-vl technical report. arXiv preprint arXiv:2505.07062, 2025a. Bytedan","arxiv_match":true},{"id":"dmf4jsd81g","title":"Cobra: Co-Training Bi-Level Observation Routing and Action Policies for GUI Agents","forum_url":"https://openreview.net/forum?id=dmf4jsd81g","excerpt":"arXiv preprint arXiv:2501.12326, 2025. Chris Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz, Gabrielle Lau, Marybeth Fair, Alice Li, William Bishop, Wei Li, Folawiyo Campbell-Ajala, et al. Androidworld: A dynamic benchmarking environment for autonomous agents. In International Conference on Learning Representations, volume 2025, pp. 406–441, 2025. Donald B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of e","arxiv_match":false},{"id":"9xv29O2jyK","title":"EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments","forum_url":"https://openreview.net/forum?id=9xv29O2jyK","excerpt":"han Waltz, Gabrielle Lau, Marybeth Fair, Alice Li, William E Bishop, Wei Li, Folawiyo Campbell-Ajala, Daniel Kenji Toyama, Robert James Berry, Divya Tyamagundlu, Timothy P Lillicrap, and Oriana Riva. Androidworld: A dynamic benchmarking environment for autonomous agents. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum? id=il5yUQsrjC. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, an","arxiv_match":false}],"id":"2405.14573","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":483},{"title":"Promptto-prompt image editing with cross attention control","arxiv_id":"2208.01626","year":2022,"url":"https://arxiv.org/abs/2208.01626","citing_submissions":218,"explicit_arxiv_matches":92,"examples":[{"id":"wm97mtaTWL","title":"SolveEdit: Benchmarking Visual Problem Solving in Generative Models","forum_url":"https://openreview.net/forum?id=wm97mtaTWL","excerpt":"ference on computer vision and pattern recognition, pp. 6904–6913, 2017. Feng Han, Yibin Wang, Chenglin Li, Zheming Liang, Dianyi Wang, Yang Jiao, Zhipeng Wei, Chao Gong, Cheng Jin, and Jiaqi Wang. Unireditbench: A unified reasoning-based image editing benchmark. arXiv preprint arXiv:2511.01295, 2025. Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Promptto-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022. Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A refer","arxiv_match":true},{"id":"nnTmJKkU4n","title":"Refinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network","forum_url":"https://openreview.net/forum?id=nnTmJKkU4n","excerpt":"efinement networks for visual synthesis. In European Conference on Computer Vision, pp. 566–585. Springer, 2026. Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross-attention control. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum? id=_CDixzkzeyb. Taihang Hu, Linxuan Li, Kai Wang, Yaxing Wang, Jian Yang, and Ming-Ming ","arxiv_match":false},{"id":"X8RkZ2dUPJ","title":"Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling","forum_url":"https://openreview.net/forum?id=X8RkZ2dUPJ","excerpt":"tion matching for flow-based text-to-image distillation. arXiv preprint arXiv:2506.00523, 2025. Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems, 36: 52132–52152, 2023. Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022. Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Saliman","arxiv_match":true}],"id":"2208.01626","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":484},{"title":"Learn then test: Calibrating predictive algorithms to achieve risk control","arxiv_id":"2110.01052","year":2021,"url":"https://arxiv.org/abs/2110.01052","citing_submissions":217,"explicit_arxiv_matches":74,"examples":[{"id":"GmJ1aYr4Ae","title":"Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents","forum_url":"https://openreview.net/forum?id=GmJ1aYr4Ae","excerpt":" Michael Ahn, Anthony Brohan, Noah Brown, et al. Do as I can, not as I say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691, 2022. Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, and Lihua Lei. Learn then test: Calibrating predictive algorithms to achieve risk control. arXiv preprint arXiv:2110.01052, 2021. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, E","arxiv_match":true},{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":" Anastasios N Angelopoulos, Stephen Bates, Emmanuel J Candès, Michael I Jordan, and Lihua Lei. Learn then test: Calibrating predictive algorithms to achieve risk control. The Annals of Applied Statistics, 19(2):1641–1662, 2025. Ibragim Badertdinov, Alexander Golubev, Maksim Nekrashevich, Anton Shevtsov, Simon Karasik, Andrei Andriushchenko, Maria Trofimova, Daria Lit","arxiv_match":false},{"id":"hj4i75KSaF","title":"FS-Cert-U: Risk Certification Before Utility Optimization for Non-Monotonic Two-Stage Model Routing","forum_url":"https://openreview.net/forum?id=hj4i75KSaF","excerpt":"ference on Learning Representations, 2024. URL https://research.google/ pubs/conformal-risk-control/. Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, and Lihua Lei. Learn then test: Calibrating predictive algorithms to achieve risk control. The Annals of Applied Statistics, 19(2): 1641–1662, 2025. doi: 10.1214/24-AOAS1998. Stephen Bates, Anastasios N. Angelopoulos, Lihua Lei, Jitendra Malik, and Michael I. Jordan. Distribution-free, ri","arxiv_match":false}],"id":"2110.01052","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":485},{"title":"Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes","arxiv_id":"2305.02301","year":2023,"url":"https://arxiv.org/abs/2305.02301","citing_submissions":216,"explicit_arxiv_matches":33,"examples":[{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"l-long.830. URL https://aclanthology.org/2023.acl-long.830/. Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. In Anna Rogers, Jordan BoydGraber, and Naoaki Okazaki (eds.), Findings of the Association for Computational Linguistics: ACL 2023, pp. 8003–8017, Toronto, Canada, July 2023. Association for Computat","arxiv_match":false},{"id":"yECLVPwR3R","title":"Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation","forum_url":"https://openreview.net/forum?id=yECLVPwR3R","excerpt":"2025. URL https: //proceedings.mlr.press/v267/helff25a.html. Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. In Findings of the Association for Computational Linguistics: ACL 2023, pp. 8003–8017. Association for Computational Linguistics, 2023. doi: 10.18653/v1/2023.findings-acl.507. URL https://aclantholo","arxiv_match":false},{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":"l-perspective recipe. arXiv preprint arXiv:2605.03677, 2026. Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. In Anna Rogers, Jordan BoydGraber, and Naoaki Okazaki (eds.), Findings of the Association for Computational Linguistics: ACL 2023, pp. 8003–8017, Toronto, Canada, July 2023. Association for Computat","arxiv_match":false}],"id":"2305.02301","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":486},{"title":"RDT-1B: A diffusion foundation model for bimanual manipulation","arxiv_id":"2410.07864","year":2024,"url":"https://arxiv.org/abs/2410.07864","citing_submissions":216,"explicit_arxiv_matches":75,"examples":[{"id":"dthpKuMMBX","title":"A2DB: Affordance as a Distillation Bottleneck for Compressing Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dthpKuMMBX","excerpt":"-set object detection. In European Conference on Computer Vision, pp. 38–55. Springer, 2024. Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation. In International Conference on Learning Representations, volume 2025, pp. 29982–30009, 2025. Yuanzhe Liu, Jingyuan Zhu, Yuchen Mo, Gen Li, Xu Cao, Jin Jin, Yifan Shen, Zhengyuan Li, Tianjiao Yu, Wen","arxiv_match":false},{"id":"6MjQdxXZoS","title":"VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=6MjQdxXZoS","excerpt":" Bo Zhao. Evo-0: Vision-language-action model with implicit spatial understanding. arXiv preprint arXiv:2507.00416, 2025. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36:34892–34916, 2023. Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation. arXiv preprint arXiv:2410.07864, 2024. Yulin Luo, Hao Chen, Zhuangzhe Wu, Bowen Sui, Jiaming Liu, Chenyang Gu, Zhuoyang Liu, Qiuxua","arxiv_match":true},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"ong robot learning. Advances in Neural Information Processing Systems, 36:44776–44791, 2023. Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation. In International Conference on Learning Representations, volume 2025, pp. 29982–30009, 2025. Yushan Liu, Peibo Sun, Shoujie Li, Yifan Xie, Lingfeng Zhang, Xintao Chao, Shiyuan Dong, Fang Chen, Xiao-","arxiv_match":false}],"id":"2410.07864","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":487},{"title":"Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling","arxiv_id":"2412.05271","year":2024,"url":"https://arxiv.org/abs/2412.05271","citing_submissions":215,"explicit_arxiv_matches":203,"examples":[{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"Zixuan Chen, Jiapeng Luo, Jiahao Wang, Tan Jiang, Bo Wang, Conghui He, Botian Shi, Xingcheng Zhang, Han Lv, Yi Wang, Wenqi Shao, Pei Chu, Zhongying Tu, Tong He, Zhiyong Wu, Huipeng Deng, Jiaye Ge, Kai Chen, Kaipeng Zhang, Limin Wang, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling, 2025c. URL https: //arxiv.org/abs/2412.05271. Mengfei Du, Binhao Wu, Zejun Li, Xuanjing Huang, and Zhongyu Wei. EmbSpatial-bench: Benchmarking s","arxiv_match":true},{"id":"1ds3a38Ku9","title":"Geo-REPA: Geometry-Aware Representation Alignment Enhances Native Spatial Intelligence in MLLMs","forum_url":"https://openreview.net/forum?id=1ds3a38Ku9","excerpt":", Xunliang Cai, et al. Think with 3d: Geometric imagination grounded spatial reasoning from limited views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613–2624, 2026b. Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271, 2024c. Zhenyu Chen, Ali Gholami, Matthias Nießner, and Angel X Chang. Scan2cap: Context-aware dens","arxiv_match":true},{"id":"Xdik5HW8VD","title":"WaLKPruner: Cognitively Inspired Visual Token Pruning for Spatial Understanding","forum_url":"https://openreview.net/forum?id=Xdik5HW8VD","excerpt":"i, Xunliang Cai, et al. Think with 3d: Geometric imagination grounded spatial reasoning from limited views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613–2624, 2026. Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271, 2024c. Zhiwen Fan, Jian Zhang, Renjie Li, Junge Zhang, Runjin Chen, Hezhen Hu, Kevin Wang, Peihao ","arxiv_match":true}],"id":"2412.05271","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":488},{"title":"Structured 3d latents for scalable and versatile 3d generation","arxiv_id":"2412.01506","year":2024,"url":"https://arxiv.org/abs/2412.01506","citing_submissions":215,"explicit_arxiv_matches":36,"examples":[{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":"gentic 3d scene generation for embodied ai. arXiv preprint arXiv:2602.10116, 2026b. Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 21469–21480, 2025. Chenfeng Xu, Huan Ling, Sanja Fidler, and Or Litany. 3difftection: 3d object detection wi","arxiv_match":false},{"id":"DmH3HpzlV6","title":"From Parts to Sim: Bottom-Up Autoregressive Generation of  Sim-Ready 3D Assets","forum_url":"https://openreview.net/forum?id=DmH3HpzlV6","excerpt":" of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. URL https://openaccess.thecvf. com/content_CVPR_2020/html/Xiang_SAPIEN_A_SimulAted_Part-Based_ Interactive_ENvironment_CVPR_2020_paper.html. Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3D latents for scalable and versatile 3D generation. arXiv preprint arXiv:2412.01506, 2024. URL https://arxiv.org/abs/2412.01506. Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu, Yue Don","arxiv_match":true},{"id":"6HZCBDqoCa","title":"DisCoMesh: Efficient and Flexible Artist-Mesh Generation via Sparse Computation powered Discrete Diffusion","forum_url":"https://openreview.net/forum?id=6HZCBDqoCa","excerpt":"cognition Conference, pp. 11093–11103, 2025. URL https://arxiv.org/abs/2411. 07025. Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21469–21480. IEEE, 2025. Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu,","arxiv_match":false}],"id":"2412.01506","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":489},{"title":"Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization","arxiv_id":"1911.08731","year":2019,"url":"https://arxiv.org/abs/1911.08731","citing_submissions":214,"explicit_arxiv_matches":72,"examples":[{"id":"Y15G7fdvPv","title":"S$^2$Aligner: Pair-Efficient Transferable Pre-Training for Sparse Text-Attributed Graphs","forum_url":"https://openreview.net/forum?id=Y15G7fdvPv","excerpt":"NLP-IJCNLP), pp. 3982–3992, 2019. Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, and Chao Huang. A survey of large language models for graphs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 6616–6626, 2024. Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731, 2019. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Elias","arxiv_match":true},{"id":"LBibdel21S","title":"ParaDa: Pretrained Parameters as Data for Federated Few-Shot Learning","forum_url":"https://openreview.net/forum?id=LBibdel21S","excerpt":"d, Jong Wook Kim, Chris Hallacy, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. URL https://proceedings.mlr.press/ v139/radford21a. 5 Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. In International Conference on Learning Representations, 2020. URL https://arxiv.org/abs/1911.08731. 5 Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, and Richard E","arxiv_match":true},{"id":"lMGk9GrpHK","title":"Enabling Validation for Robust Few-Shot Recognition","forum_url":"https://openreview.net/forum?id=lMGk9GrpHK","excerpt":"o reweight examples for robust deep learning. In International Conference on Machine Learning, 2018. Shuvendu Roy and Ali Etemad. Consistency-guided prompt learning for vision-language models. In International Conference on Learning Representations, 2024. Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731, 2019. Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko. Semi-supervised","arxiv_match":true}],"id":"1911.08731","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":490},{"title":"LongLive: Real-time Interactive Long Video Generation","arxiv_id":"2509.22622","year":2025,"url":"https://arxiv.org/abs/2509.22622","citing_submissions":214,"explicit_arxiv_matches":131,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025. Yilun Xu, Weili Nie, and Arash Vahdat. One-step diffusion models with f -divergence distribution matching. arXiv preprint arXiv:2502.15681, 2025. Shuai Yang, Wei Huang, Ruihang Chu, Yicheng Xiao, Yuyang Zhao, Xianbang Wang, Muyang Li, Enze Xie, Yingcong Chen, Yao Lu, et al. Longlive: Real-time interactive long video generation. arXiv preprint arXiv:2509.22622, 2025. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, W","arxiv_match":true},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"s for Text-to-Image Generation. In NeurIPS, 2023b. Shuai Yang, Wei Huang, Ruihang Chu, Yicheng Xiao, Yuyang Zhao, Xianbang Wang, Muyang Li, Enze Xie, Yingcong Chen, Yao Lu, Song Han, and Yukang Chen. LongLive: Real-Time Interactive Long Video Generation. In ICLR, 2026. Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, and Yoon Kim. Gated Linear Attention Transformers with Hardware-Efficient Training. In ICML, 2024. Zhuoyi Yang, Jiayan Teng, We","arxiv_match":false},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"eo diffusion. arXiv preprint arXiv:2608.26794, 2026. Haoning Yang, Xinyuan Chen, Yaohui Wang, and Guo Lu. Diff-vf: Training-free high-quality long video generation via diffusion model. ACM Transactions on Multimedia Computing, Communications and Applications, 2026. Shuai Yang, Wei Huang, Ruihang Chu, Yicheng Xiao, Yuyang Zhao, Xianbang Wang, Muyang Li, Enze Xie, Yingcong Chen, Yao Lu, et al. Longlive: Real-time interactive long video generation. arXiv preprint arXiv:2509.22622, 2025a. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, ","arxiv_match":true}],"id":"2509.22622","metadata_source":"Cached arXiv API metadata","rank":491},{"title":"Mlvu: Benchmarking multi-task long video understanding","arxiv_id":"2406.04264","year":2024,"url":"https://arxiv.org/abs/2406.04264","citing_submissions":214,"explicit_arxiv_matches":48,"examples":[{"id":"ZQOtaCagPN","title":"EviQ: Hierarchical Query-Conditioned Evidence Selection for Efficient VideoLLMs","forum_url":"https://openreview.net/forum?id=ZQOtaCagPN","excerpt":"ruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024. Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, et al. Mlvu: Benchmarking multi-task long video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13691–13701, 2025. Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar, Zhenheng Yang, and Yang You. Fo","arxiv_match":false},{"id":"gX30Aapm6u","title":"VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning","forum_url":"https://openreview.net/forum?id=gX30Aapm6u","excerpt":"anhao Zhang, He Tang, and Xiang Bai. Owlcap: Harmonizing motion-detail for video captioning via HMD-270K and caption set equivalence reward. ArXiv preprint, abs/2508.18634, 2025. URL https://arxiv. org/abs/2508.18634. Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, et al. MLVU: Benchmarking multi-task long video understanding. arXiv preprint arXiv:2406.04264, 2024. URL https://arxiv.org/abs/2406.04264. LLM U SAGE D ECLARATION LLMs were used solely to check grammar and improve the clarity of the manu","arxiv_match":true},{"id":"iBCBaGASs6","title":"HEAT: Hierarchical Elastic Adaptive Token Sparsification via Depth Scaling","forum_url":"https://openreview.net/forum?id=iBCBaGASs6","excerpt":"nal Conference on Machine Learning, pp. 1–18, 2025c. Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. MLVU: benchmarking multi-task long video understanding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13691–13701, 2025. Xin Zou, Di Lu, Yizhou Wang, Yibo Yan, Yuanhuiyi Lyu, Xu Zheng, Linfeng Zhang, and Xuming Hu. Don’t just cha","arxiv_match":false}],"id":"2406.04264","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":492},{"title":"Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions","arxiv_id":"2507.05257","year":2025,"url":"https://arxiv.org/abs/2507.05257","citing_submissions":213,"explicit_arxiv_matches":70,"examples":[{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"esh, Fei Jia, Yang Zhang, and Boris Ginsburg. Ruler: What’s the real context size of your long-context language models? arXiv preprint arXiv:2404.06654, 2024. Yuanzhe Hu, Yu Wang, and Julian McAuley. Evaluating memory in llm agents via incremental multi-turn interactions. In International Conference on Learning Representations, volume 2026, pp. 156259–156291, 2026. Kaixuan Huang, Jiacheng Guo, Zihao Li, Xiang Ji, Jiawei Ge, Wenzhe Li, Yingqing Guo, Tianle Cai, Hui Yu","arxiv_match":false},{"id":"EUiwYKQQVt","title":"When External Memory Becomes Agent State: Recorded Lineage and Controlled Reuse in LLM Agents","forum_url":"https://openreview.net/forum?id=EUiwYKQQVt","excerpt":"nce semirings. In Proceedings of the 26th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 31–40, 2007. doi: 10.1145/1265530.1265535. Yuanzhe Hu, Yu Wang, and Julian McAuley. Evaluating memory in LLM agents via incremental multi-turn interactions. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=DT7JyQC3MR. Xiaoyang Li, Yiqi Wang, Haohui Lu, Zhi Chen, Mo Li, Pingan Song, Mingkai","arxiv_match":false},{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"s. In Proceedings of the 28th International Conference on Computational Linguistics, pp. 6609–6625, 2020. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. RULER: What’s the real context size of your long-context language models? arXiv preprint arXiv:2404.06654, 2024. Yuanzhe Hu, Yu Wang, and Julian McAuley. Evaluating memory in LLM agents via incremental multi-turn interactions. arXiv preprint arXiv:2507.05257, 2025. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. LLMLingua: Compressing ","arxiv_match":true}],"id":"2507.05257","metadata_source":"Cached arXiv API metadata","rank":493},{"title":"Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding","arxiv_id":"2505.22618","year":2025,"url":"https://arxiv.org/abs/2505.22618","citing_submissions":213,"explicit_arxiv_matches":95,"examples":[{"id":"UtSpCnqHk8","title":"Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving","forum_url":"https://openreview.net/forum?id=UtSpCnqHk8","excerpt":"ENIX Symposium on Networked Systems Design and Implementation (NSDI 26), pp. 57–74, 2026a. Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and Enze Xie. Fast-dLLM: Training-free acceleration of diffusion LLM by enabling KV cache and parallel decoding. In International Conference on Learning Representations, volume 2026, pp. 57027–57051, 2026b. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Hua","arxiv_match":false},{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"g discrete diffusion models with inference-time scaling. In Advances in Neural Information Processing Systems, volume 38, 2025. URL https://arxiv.org/abs/2503.00307. Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and Enze Xie. Fast-dLLM: Training-free acceleration of diffusion LLM by enabling KV cache and parallel decoding. In International Conference on Learning Representations, 2026a. URL https://arxiv.org/abs/2505.22618. Yifan Wu, Yufeng Zhang, and Kenli Li. CORA-Diff: Confidence-oriented residual acceptance for effic","arxiv_match":true},{"id":"6zSuqyB4a9","title":"What Makes a Good Decoding Path? Selecting Parallel-Decodable Trajectories for Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=6zSuqyB4a9","excerpt":"is of multivariate correlation. IBM Journal of research and development, 4(1):66–82, 1960. Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and Enze Xie. Fast-dLLM: Training-free acceleration of diffusion LLM by enabling KV cache and parallel decoding. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=3Z3Is6hnOT. Chenkai Xu, Yijie Jin, Jiajun Li, Yi Tu, Guoping Long, Dandan Tu, Mingco","arxiv_match":false}],"id":"2505.22618","metadata_source":"Cached arXiv API metadata","rank":494},{"title":"Inner monologue: Embodied reasoning through planning with language models","arxiv_id":"2207.05608","year":2022,"url":"https://arxiv.org/abs/2207.05608","citing_submissions":213,"explicit_arxiv_matches":86,"examples":[{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"eprint arXiv:2308.08998, 2023. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al. Inner monologue: Embodied reasoning through planning with language models. arXiv preprint arXiv:2207.05608, 2022. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostro","arxiv_match":true},{"id":"eipQbEsWLA","title":"Adaptive Consistency Graph for Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=eipQbEsWLA","excerpt":"sks with large language model. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2025. URL https://aclanthology.org/2025.acl-long.1575/. Wenlong Huang et al. Inner monologue: Embodied reasoning through planning with language models. In Proceedings of the 6th Conference on Robot Learning, pp. 1769–1782, 2023. URL https://proceedings.mlr.press/v205/huang23c.html. Gautier Izacard and Edouard Grave. Leveraging passage retrieval wit","arxiv_match":false},{"id":"PjUHgdvzhE","title":"A Recursive Self-Improving Harness for Training-Free Generalist Navigation","forum_url":"https://openreview.net/forum?id=PjUHgdvzhE","excerpt":"s Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Tomas Jackson, Noah Brown, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter. Inner monologue: Embodied reasoning through planning with language models. In Karen Liu, Dana Kulic, and Jeff Ichnowski (eds.), Proceedings of The 6th Conference on Robot Learning, volume 205 of Proceedings of Machine Learning Research, pp. 1769–1782. PMLR, 2023. Brian Ich","arxiv_match":false}],"id":"2207.05608","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":495},{"title":"LLMs Get Lost In Multi-Turn Conversation","arxiv_id":"2505.06120","year":2025,"url":"https://arxiv.org/abs/2505.06120","citing_submissions":213,"explicit_arxiv_matches":68,"examples":[{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":"nd Huaxiu Yao. ClawArena: Benchmarking AI Agents in Evolving Information Environments, 2026. URL https://arxiv.org/abs/2604. 04202. Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, and Jennifer Neville. LLMs Get Lost In MultiTurn Conversation. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 54738–54778, 2026. URL https://proceedings.icl","arxiv_match":false},{"id":"UeO5A1vhy1","title":"Correcting Is Not Reusing: What a Failed Attempt Contributes Beyond the Verdict","forum_url":"https://openreview.net/forum?id=UeO5A1vhy1","excerpt":"ement for large language model serving with PagedAttention. In Proceedings of the 29th Symposium on Operating Systems Principles (SOSP), 2023. Philippe Laban, Lidiya Murakhovs’ka, Caiming Xiong, and Chien-Sheng Wu. Are you sure? Challenging LLMs leads to performance drops in the FlipFlop experiment. arXiv preprint arXiv:2311.08596, 2023. Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, and Jennifer Neville. LLMs get lost in multi-turn conversation. arXiv preprint arXiv:2505.06120, 2025. Jipeng Li, Zeyu Gao, Yubin Qi, Hande Dong, Weijian Chen, and Qiang Lin. Unleashing the true ","arxiv_match":true},{"id":"Wk2GhJs99T","title":"Resolving Action Bottleneck: Agentic Reinforcement Learning Informed by Token-Level Energy","forum_url":"https://openreview.net/forum?id=Wk2GhJs99T","excerpt":"Beutel, Alex Carney, et al. Openai o1 system card. arXiv preprint arXiv:2412.16720, 2024. Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, and Nicolas Le Roux. Vineppo: Refining credit assignment in rl training of llms, 2025. URL https://arxiv.org/abs/2410.01679. Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, and Jennifer Neville. Llms get lost in multi-turn conversation, 2025. URL https://arxiv.org/abs/2505.06120. Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy-based out-of-distribution detection. ","arxiv_match":true}],"id":"2505.06120","metadata_source":"Cached arXiv API metadata","rank":496},{"title":"Visualwebarena: Evaluating multimodal agents on realistic visual web tasks","arxiv_id":"2401.13649","year":2024,"url":"https://arxiv.org/abs/2401.13649","citing_submissions":212,"explicit_arxiv_matches":39,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":". URL https://books. google.co.jp/books?id=xiZmAAAAIAAJ. Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, and Daniel Fried. Visualwebarena: Evaluating multimodal agents on realistic visual web tasks. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 881–905, 2024. Julian Lienen, Caglar Demir, and Eyke Hüllermeier. Conformal c","arxiv_match":false},{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"smartphone users. arXiv preprint arXiv:2503.02268, 2025. Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, and Daniel Fried. Visualwebarena: Evaluating multimodal agents on realistic visual web tasks. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 881–905, 2024. Sunjae Lee, Junyoung Choi, Jungjae Lee, Munim Hasan Wasi, Hojun","arxiv_match":false},{"id":"YAVRc0xfa9","title":"Game-Evolver: Dual-Loop Evolution Elicits Better End-to-End Game Orchestration","forum_url":"https://openreview.net/forum?id=YAVRc0xfa9","excerpt":"ernational Conference on Learning Representations, 2024. Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, and Daniel Fried. Visualwebarena: Evaluating multimodal agents on realistic visual web tasks. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 881–905, 2024. Hanyu Lai, Xiao Liu, Yanxiao Zhao, Han Xu, Hanchen Zhang, Bohao","arxiv_match":false}],"id":"2401.13649","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":497},{"title":"DiffusionNFT: Online Diffusion Reinforcement with Forward Process","arxiv_id":"2509.16117","year":2025,"url":"https://arxiv.org/abs/2509.16117","citing_submissions":211,"explicit_arxiv_matches":93,"examples":[{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":"ul nowcasting of extreme precipitation with nowcastnet. Nature, 2023. Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, and Ming-Yu Liu. Diffusionnft: Online diffusion reinforcement with forward process. In ICLR, 2026. Jincheng Zhong, Xiangcheng Zhang, Jianmin Wang, and Mingsheng Long. Domain guidance: A simple transfer approach for a pre-trained diffusion model. In ICLR, 2025. A M ODEL A RCHITECTUR","arxiv_match":false},{"id":"qNhfYunjC7","title":"FASE: Factor-Aware Spectral Evolution for Gradient-Free Continuation of Diffusion Post-Training","forum_url":"https://openreview.net/forum?id=qNhfYunjC7","excerpt":"ances in Neural Information Processing Systems, 36:15903–15935, 2023. Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, and Ming-Yu Liu. Diffusionnft: Online diffusion reinforcement with forward process. In International Conference on Learning Representations, volume 2026, pp. 134129–134150, 2026. Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi, Haitz Sáez De Ocáriz Borde, Rickard Brüel Gabrielsson","arxiv_match":false},{"id":"tSpuyBaWWf","title":"Displacement Matching: One Objective for Reward Learning and Distillation in Few-Step Generators","forum_url":"https://openreview.net/forum?id=tSpuyBaWWf","excerpt":"reeman, and Taesung Park. One-step diffusion with distribution matching distillation. In CVPR, 2024b. Z-Image Team. Z-image: An efficient image generation foundation model with single-stream diffusion transformer. arXiv preprint arXiv:2511.22699, 2025. Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, and Ming-Yu Liu. Diffusionnft: Online diffusion reinforcement with forward process. arXiv preprint arXiv:2509.16117, 2025. A All experiments share the training pipeline of Section 2.3: anchor rollouts, re-noised per","arxiv_match":true}],"id":"2509.16117","metadata_source":"Cached arXiv API metadata","rank":498},{"title":"EAGLE: Speculative sampling requires rethinking feature uncertainty","arxiv_id":"2401.15077","year":2024,"url":"https://arxiv.org/abs/2401.15077","citing_submissions":211,"explicit_arxiv_matches":74,"examples":[{"id":"e9RG7RBDSX","title":"Cadence: Regulating the Speculation Externality in LLM Serving","forum_url":"https://openreview.net/forum?id=e9RG7RBDSX","excerpt":" and SLO-aware batching. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 10(2):55:1–55:31, 2026b. doi: 10.1145/3805653. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. EAGLE: Speculative sampling requires rethinking feature uncertainty. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 28935–28948. PMLR, 2024a. Yuhui Li, Fangyun Wei, Chao Zhang, and ","arxiv_match":false},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":". 19274–19286. PMLR, 2023. Jia Li, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, and Stanislas Polu. Numinamath. https://huggingface. co/datasets/AI-MO/NuminaMath-1.5, 2024a. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. Eagle: Speculative sampling requires rethinking feature uncertainty. arXiv preprint arXiv:2401.15077, 2024b. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. Eagle-2: Faster inference of languag","arxiv_match":true},{"id":"ie4ipFVb04","title":"LAM++: Enhanced LocateAnything via High-Resolution Post-Training and Geometry-Aware Speculative Decoding","forum_url":"https://openreview.net/forum?id=ie4ipFVb04","excerpt":"ning. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10955–10965. IEEE, 2022. Xiang Li, Jian Ding, and Mohamed Elhoseiny. Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding. Advances in Neural Information Processing Systems, 37:3229–3242, 2024a. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. Eagle: Speculative sampling requires rethinking feature uncertainty. arXiv preprint arXiv:2401.15077, 2024b. Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pi","arxiv_match":true}],"id":"2401.15077","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":499},{"title":"Continuous 3D Perception Model with Persistent State","arxiv_id":"2501.12387","year":2025,"url":"https://arxiv.org/abs/2501.12387","citing_submissions":210,"explicit_arxiv_matches":19,"examples":[{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":"ing latent video diffusion. In European Conference on Computer Vision, pp. 439–457. Springer, 2024. Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer, 2025a. URL https://arxiv.org/ abs/2503.11651. Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A. Efros, and Angjoo Kanazawa. Continuous 3d perception model with persistent state, 2025b. URL https://arxiv.org/abs/ 2501.12387. Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric ","arxiv_match":true},{"id":"dmWiArTSoB","title":"Validation over Contexts: Robust Long-Context Inference for 3D Reconstruction Models","forum_url":"https://openreview.net/forum?id=dmWiArTSoB","excerpt":"hristian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In CVPR, pp. 5294–5306, 2025a. Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A Efros, and Angjoo Kanazawa. Continuous 3d perception model with persistent state. In CVPR, pp. 10510–10522, 2025b. Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In CVPR, pp. 20697–20709, 2024. Yifan Wang, Ji","arxiv_match":false},{"id":"VemSN4uC6Q","title":"Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs","forum_url":"https://openreview.net/forum?id=VemSN4uC6Q","excerpt":"ed transformer. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5294–5306, 2025a. Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A Efros, and Angjoo Kanazawa. Continuous 3d perception model with persistent state. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 10510–10522, 2025b. Qineng Wang, Baiqiao Yin, Pingyue Zhang, Jianshu Zhang, Kangrui Wang, Zihan Wang, Jieyu Zhang, Keshi","arxiv_match":false}],"id":"2501.12387","metadata_source":"Cached arXiv API metadata","rank":500},{"title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","arxiv_id":"2207.01848","year":2022,"url":"https://arxiv.org/abs/2207.01848","citing_submissions":210,"explicit_arxiv_matches":44,"examples":[{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":"ve empirical study on semi-supervised learning. In 2010 IEEE International Conference on Data Mining, pp. 186–195. IEEE, 2010. Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. In The Eleventh International Conference on Learning Representations, 2023. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, ","arxiv_match":false},{"id":"mZ3FfjH6Gm","title":"Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=mZ3FfjH6Gm","excerpt":" Hollmann, and Frank Hutter. TabPFN-2.5: Advancing the state of the art in tabular foundation models. arXiv: 2511.08667, 2025. Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. TabPFN: A transformer that solves small tabular classification problems in a second. In International Conference on Learning Representations, 2023. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hut","arxiv_match":false},{"id":"wSHvuy1308","title":"Training Fair Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=wSHvuy1308","excerpt":" Srebro. Equality of Opportunity in Supervised Learning. In Advances in Neural Information Processing Systems, volume 29, 2016. Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. In International Conference on Learning Representations, 2023. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutte","arxiv_match":false}],"id":"2207.01848","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":501},{"title":"The Pile: An 800GB dataset of diverse text for language modeling","arxiv_id":"2101.00027","year":2021,"url":"https://arxiv.org/abs/2101.00027","citing_submissions":210,"explicit_arxiv_matches":204,"examples":[{"id":"M82NayqkbE","title":"Dataset Inference via Cumulative Evidence","forum_url":"https://openreview.net/forum?id=M82NayqkbE","excerpt":". Mia-tuner: adapting large language models as pre-training text detector. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 27295–27303, 2025. Jean-loup Gailly and Mark Adler. Zlib compression library. 2004. Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smol","arxiv_match":true},{"id":"GI61L4n4Oz","title":"SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference","forum_url":"https://openreview.net/forum?id=GI61L4n4Oz","excerpt":"decoding. In ICML, 2024. Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen. Sparse GPU kernels for deep learning. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2020. Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. The Pile: An 800GB dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020. Aaron Grattafiori et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. ","arxiv_match":true},{"id":"Q1MdZrN6Sv","title":"Beyond Singular Values: Geometry and Numerical Fidelity in Muon","forum_url":"https://openreview.net/forum?id=Q1MdZrN6Sv","excerpt":"imizer. arXiv:2604.01472, 2026. John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(61):2121–2159, 2011. Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. The Pile: An 800GB dataset of diverse text for language modeling. arXiv:2101.00027, 2020. Evan S. Gawlik and Melvin Leok. Iterative computation of the Fréchet derivative of the polar","arxiv_match":true}],"id":"2101.00027","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":502},{"title":"The platonic representation hypothesis","arxiv_id":"2405.07987","year":2024,"url":"https://arxiv.org/abs/2405.07987","citing_submissions":210,"explicit_arxiv_matches":72,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"udson and Christopher D. Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In CVPR, 2019. Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. The platonic representation hypothesis. In ICML, 2024. Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, et al. Pi0.7: a steerable gene","arxiv_match":false},{"id":"o6IsUtKlgQ","title":"Learning Spatial-Pyramid Visual Representations for Autoregressive Image Generation","forum_url":"https://openreview.net/forum?id=o6IsUtKlgQ","excerpt":"nd Jiwen Lu. Spectralar: Spectral autoregressive visual generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15842–15852, 2025a. Zhihao Huang, Xi Qiu, Yukuo Ma, Yifu Zhou, Chi Zhang, and Xuelong Li. Nfig: Autoregressive image generation with next-frequency prediction. arXiv preprint arXiv:2503.07076, 2025b. Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. The platonic representation hypothesis. arXiv preprint arXiv:2405.07987, 2024. Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. I","arxiv_match":true},{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"t, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding, 2021. URL https: //arxiv.org/abs/2009.03300. Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Cosmos qa: Machine reading comprehension with contextual commonsense reasoning, 2019. URL https://arxiv.org/ abs/1909.00277. Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. The platonic representation hypothesis. arXiv preprint arXiv:2405.07987, 2024. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zh","arxiv_match":true}],"id":"2405.07987","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":503},{"title":"Time-llm: Time series forecasting by reprogramming large language models","arxiv_id":"2310.01728","year":2023,"url":"https://arxiv.org/abs/2310.01728","citing_submissions":210,"explicit_arxiv_matches":41,"examples":[{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"ning, pp. 577–588, 2025. doi: 10.1145/3690624.3709201. Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pinyu Chen, Yuxuan Liang, Yuanfang Li, Shirui Pan, and Qingsong Wen. Time-llm: Time series forecasting by reprogramming large language models. In Proceedings of the International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Unb5CVPtae. Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marv","arxiv_match":false},{"id":"qpcwuOWDCv","title":"TimeSelect: Variable-Wise Historical Context Selection for Time Series Forecasting","forum_url":"https://openreview.net/forum?id=qpcwuOWDCv","excerpt":"ei Shen, Ruixin Zhang, Shouhong Ding, Binwu Wang, Zhengyang Zhou, and Yang Wang. Crossgnn: Confronting noisy multivariate time series via cross interaction refinement. Advances in Neural Information Processing Systems, 36:46885–46902, 2023. Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al. Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728, 2023. Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan","arxiv_match":true},{"id":"iaMmGPCgNV","title":"Recursive Inference for Test-Time Compute Allocation in Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=iaMmGPCgNV","excerpt":"2018. URL https://openreview.net/forum?id=SJa9iHgAZ. Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen. Time-LLM: Time series forecasting by reprogramming large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Unb5CVPtae. Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber, Oliver Neumann, Ch","arxiv_match":false}],"id":"2310.01728","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":504},{"title":"Training software engineering agents and verifiers with SWE-Gym","arxiv_id":"2412.21139","year":2024,"url":"https://arxiv.org/abs/2412.21139","citing_submissions":210,"explicit_arxiv_matches":90,"examples":[{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"low instructions with human feedback. Advances in neural information processing systems, 35:27730– 27744, 2022. Charles Packer, Vivian Fang, Shishir G. Patil, Kevin Lin, Sarah Wooders, and Joseph E. Gonzalez. MemGPT: Towards LLMs as operating systems. arXiv preprint arXiv:2310.08560, 2023. Jiayi Pan, Xingyao Wang, Graham Neubig, Navdeep Jaitly, Heng Ji, Alane Suhr, and Yizhe Zhang. Training software engineering agents and verifiers with SWE-Gym. arXiv preprint arXiv:2412.21139, 2024. Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Che","arxiv_match":true},{"id":"AnxK3Tb5VS","title":"To Ask or Not to Ask: Backdooring the Learned Consultation Policy of Collaborative LLM Agents","forum_url":"https://openreview.net/forum?id=AnxK3Tb5VS","excerpt":"o route LLMs from preference data. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=8sSqNntaMr. Jiayi Pan, Xingyao Wang, Graham Neubig, Navdeep Jaitly, Heng Ji, Alane Suhr, and Yizhe Zhang. Training software engineering agents and verifiers with swe-gym. In Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), 2025. URL https://arxiv. org/abs/2412.21139. arXiv:2412.21139, accepted at ICML 2025. PyCQA. Bandit: A security linter for Python. https://github.com/PyCQA/bandi","arxiv_match":true},{"id":"t8Pg5VElI5","title":"Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments","forum_url":"https://openreview.net/forum?id=t8Pg5VElI5","excerpt":" agents on hard, realistic tasks in command line interfaces, 2026. URL https://arxiv.org/ab s/2601.11868. Jiayi Pan, Xingyao Wang, Graham Neubig, Navdeep Jaitly, Heng Ji, Alane Suhr, and Yizhe Zhang. Training software engineering agents and verifiers with SWE-Gym, 2024. URL https://arxiv. org/abs/2412.21139. Zhihong Pan, Jiyuan He, Kai Zhang, Yupeng Han, Ze Liu, Yuze Zhao, Yongcong Ye, and Zhaohua Yang. Meta-Task: Turning terminal task synthesis into a termin","arxiv_match":false}],"id":"2412.21139","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":505},{"title":"MOMENT: A family of open time-series foundation models","arxiv_id":"2402.03885","year":2024,"url":"https://arxiv.org/abs/2402.03885","citing_submissions":209,"explicit_arxiv_matches":50,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":"tt. Can a Transformer Represent a Kalman Filter? In Learning for Dynamics and Control Conference (L4DC), 2024. Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. MOMENT: A Family of Open Time-Series Foundation Models. In International Conference on Machine Learning (ICML), 2024. James D. Hamilton. Time Series Analysis. Princeton University Press, 1994. Andrew C. Harvey. Forecasting, Structural Time Series Models ","arxiv_match":false},{"id":"0LJ106OAjs","title":"Forecasting Before the Outcome: Catalogue Gated Prequential Cognitive Performance under Participant and Domain Shift","forum_url":"https://openreview.net/forum?id=0LJ106OAjs","excerpt":"tracing. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 2330–2339, 2020. W Brier Glenn et al. Verification of forecasts expressed in terms of probability. Monthly weather review, 78(1):1–3, 1950. Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. Moment: A family of open time-series foundation models. arXiv preprint arXiv:2402.03885, 2024. URL https://arxiv.org/abs/2402.03885. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yujiu Yang, Nan Duan, Weizhu Chen, et al. Critic: Large lang","arxiv_match":true},{"id":"LJ3tnmkITu","title":"Recurrent Domain Incremental Adaptation of Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=LJ3tnmkITu","excerpt":"training with prototype-guided normalization. IEEE Transactions on Neural Networks and Learning Systems, 2026. Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. MOMENT: A family of open time-series foundation models. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, volume 235 of Proceedings of Machine Learning Research, pp. 16115–16152. PMLR / OpenReview.","arxiv_match":false}],"id":"2402.03885","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":506},{"title":"Parallelizing Linear Transformers with the Delta Rule over Sequence Length","arxiv_id":"2406.06484","year":2024,"url":"https://arxiv.org/abs/2406.06484","citing_submissions":208,"explicit_arxiv_matches":46,"examples":[{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"mers with hardware-efficient training. In Proceedings of the 41st International Conference on Machine Learning, pp. 56501–56523, 2024a. Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, and Yoon Kim. Parallelizing linear transformers with the delta rule over sequence length. Advances in neural information processing systems, 37: 115491–115522, 2024b. Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated delta networks: Improving mamba2 with delta rule. In International Co","arxiv_match":false},{"id":"b47jrUdTcU","title":"StacKV: Spatio-Temporally Aware Compression of KV Caches in Hybrid Language Models","forum_url":"https://openreview.net/forum?id=b47jrUdTcU","excerpt":"ameswar Panda, and Yoon Kim. Gated linear attention transformers with hardware-efficient training, 2024. URL https://arxiv.org/abs/ 2312.06635. Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated delta networks: Improving mamba2 with delta rule, 2025b. URL https://arxiv.org/abs/2412.06464. Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, and Yoon Kim. Parallelizing linear transformers with the delta rule over sequence length, 2025c. URL https://arxiv.org/abs/ 2406.06484. Haoyue Zhang, Hualei Zhang, Xiaosong Ma, Jie Zhang, and Song Guo. Lazyeviction: Lagged kv eviction","arxiv_match":true},{"id":"xUxa60BiCG","title":"SLiK: Learned Sturm–Liouville Spectral Geometry for Sequence Modeling","forum_url":"https://openreview.net/forum?id=xUxa60BiCG","excerpt":". Zexue Wu, Yifeng Gong, and Aoqian Zhang. Dtmamba: Dual twin mamba for time series forecasting. arXiv preprint arXiv:2405.07022, 2024. Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, and Yoon Kim. Parallelizing linear transformers with the delta rule over sequence length. Advances in neural information processing systems, 37:115491–115522, 2024. Xihang Yue, Yi Yang, and Linchao Zhu. Holistic physics solver: Learning pdes in a unified spectralphysical space. arXiv pre","arxiv_match":false}],"id":"2406.06484","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":507},{"title":"SWE-smith: Scaling Data for Software Engineering Agents","arxiv_id":"2504.21798","year":2025,"url":"https://arxiv.org/abs/2504.21798","citing_submissions":208,"explicit_arxiv_matches":97,"examples":[{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":" Liangli Zhen, and Jiancheng Lv. Rethinking KV cache eviction via a unified information-theoretic objective. In Forty-third International Conference on Machine Learning, 2026a. URL https://openreview.net/forum?id=2fPbFUJ87k. John Yang, Kilian Lieret, Carlos E. Jimenez, Alexander Wettig, Kabir Khandpur, Yanzhe Zhang, Binyuan Hui, Ofir Press, Ludwig Schmidt, and Diyi Yang. Swe-smith: Scaling data for software engineering agents, 2025b. URL https://arxiv.org/abs/2504.21798. Xintong Yang, Hao Gu, Binxing Xu, Lujun Li, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Yike Guo, and Sirui","arxiv_match":true},{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"ffrey Ma, Parth Thakkar, Dmitrii Pedchenko, Sten Sootla, Emily McMilin, Pengcheng Yin, Rui Hou, Gabriel Synnaeve, Diyi Yang, and Ofir Press. ProgramBench: Can language models rebuild programs from scratch? arXiv preprint arXiv:2605.03546, 2026. John Yang, Kilian Lieret, Carlos E Jimenez, Alexander Wettig, Kabir Khandpur, Yanzhe Zhang, Binyuan Hui, Ofir Press, Ludwig Schmidt, and Diyi Yang. SWE-smith: Scaling data for software engineering agents. arXiv preprint arXiv:2504.21798, 2025. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, an","arxiv_match":true},{"id":"AnxK3Tb5VS","title":"To Ask or Not to Ask: Backdooring the Learned Consultation Policy of Collaborative LLM Agents","forum_url":"https://openreview.net/forum?id=AnxK3Tb5VS","excerpt":"nical report. arXiv preprint arXiv:2505.09388, 2025a. John Yang, Kilian Lieret, Carlos Jimenez, Alexander Wettig, Kabir Khandpur, Yanzhe Zhang, Binyuan Hui, Ofir Press, Ludwig Schmidt, and Diyi Yang. Swe-smith: Scaling data for software engineering agents. In D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, and N. Chen (eds.), Advances in Neural Information Processing Systems, volume 38, Main Conference. Curran Associates, Inc., 202","arxiv_match":false}],"id":"2504.21798","metadata_source":"Cached arXiv API metadata","rank":508},{"title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","arxiv_id":"1610.02136","year":2016,"url":"https://arxiv.org/abs/1610.02136","citing_submissions":207,"explicit_arxiv_matches":65,"examples":[{"id":"uTIoY3tprZ","title":"More Information, Less Uncertainty? Monotonic Uncertainty Regularization under Progressive Information","forum_url":"https://openreview.net/forum?id=uTIoY3tprZ","excerpt":"r image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2015. URL https://api.semanticscholar.org/CorpusID:206594692. Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. ArXiv, abs/1610.02136, 2016. URL https://api. semanticscholar.org/CorpusID:13046179. Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. Distilling the knowledge in a neural network. ArXiv, abs/150","arxiv_match":false},{"id":"1wsVNB7eBv","title":"WHEN ROUTING PROBES IMPROVE WITHOUT NEW INFORMATION: AUDITING UNCERTAINTY BEYOND MODEL OUTPUTS","forum_url":"https://openreview.net/forum?id=1wsVNB7eBv","excerpt":"rint Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. In International Conference on Machine Learning (ICML), 2017. Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In International Conference on Learning Representations (ICLR), 2017. John Hewitt and Percy Liang. Designing and interpreting probes with control tasks. In Empirical Methods in Natural Language Proc","arxiv_match":false},{"id":"EUwi8JmVHj","title":"Evaluating Vision-Language Models on Images of Individuals with Limb Deficiencies","forum_url":"https://openreview.net/forum?id=EUwi8JmVHj","excerpt":"z grand challenge: Answering visual questions from blind people. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3608–3617, 2018. Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2017. Drew A. Hudson and Christopher D. Manning. Gqa: A new dataset for real-world visual reasoning and composition","arxiv_match":false}],"id":"1610.02136","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":509},{"title":"Self-Distilled RLVR","arxiv_id":"2604.03128","year":2026,"url":"https://arxiv.org/abs/2604.03128","citing_submissions":207,"explicit_arxiv_matches":187,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"i, Tianyi Tang, Wenbiao Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yuqiong Liu, Zekun Wang, Zeyu Cui, Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu. Qwen3 technical report, 2025. URL https://arxiv.org/abs/2505.09388. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. Self-Distilled RLVR, 2026. URL https://arxiv.org/abs/ 2604.03128. Junkai Zhang, Zihao Wang, Lin Gui, Swarnashree Mysore Sathyendra, Jaehwan Jeong, Victor Veitch, We","arxiv_match":true},{"id":"1UUiXNNCyo","title":"Sparse Where, Dense Within: Contrastive Privileged Action Distillation for Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=1UUiXNNCyo","excerpt":"ve reinforced policy optimization via privileged self-distillation, 2026b. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. Self-distilled RLVR, 2026a. Shuo Yang, Jinyang Wu, Zhengxi Lu, Yuhao Shen, Fan Zhang, Lang Feng, Shuai Zhang, Haoran Luo, Zheng Lian, Zhengqi Wen, and Jianhua Tao. OPID: On-policy skill distillation for agentic reinforc","arxiv_match":false},{"id":"7EtSASfnVn","title":"OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning","forum_url":"https://openreview.net/forum?id=7EtSASfnVn","excerpt":"Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, et al. Qwen2.5-Omni Technical Report. arXiv preprint arXiv:2503.20215, 2025a. Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, et al. Qwen3-Omni Technical Report. arXiv preprint arXiv:2509.17765, 2025b. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. Self-Distilled RLVR. arXiv preprint arXiv:2604.03128, 2026. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian ","arxiv_match":true}],"id":"2604.03128","metadata_source":"Cached arXiv API metadata","rank":510},{"title":"BERTScore: Evaluating text generation with BERT","arxiv_id":"1904.09675","year":2019,"url":"https://arxiv.org/abs/1904.09675","citing_submissions":205,"explicit_arxiv_matches":63,"examples":[{"id":"uwSMvBYqGq","title":"Auditing Optimization and Probabilistic Reliability in Frozen Vision-Language Representation Intervention","forum_url":"https://openreview.net/forum?id=uwSMvBYqGq","excerpt":"Weizhu Chen, and Tuo Zhao. Adaptive budget allocation for parameter-efficient fine-tuning. In ICLR. OpenReview.net, 2023. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with BERT. In ICLR, 2020. AI U SE S TATEMENT We used AI tools to edit the manuscript for grammar and readability, assist with literature searches and identify relevant literature, format references, and edit a","arxiv_match":false},{"id":"M82NayqkbE","title":"Dataset Inference via Cumulative Evidence","forum_url":"https://openreview.net/forum?id=M82NayqkbE","excerpt":"hine Learning, 2024. URL https: //openreview.net/forum?id=sT7UJh5CTc. Jingyang Zhang, Jingwei Sun, Eric Yeats, Yang Ouyang, Martin Kuo, Jianyi Zhang, Hao Frank Yang, and Hai Li. Min-k%++: Improved baseline for detecting pre-training data from large language models. arXiv preprint arXiv:2404.02936, 2024. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert, 2020. URL https://arxiv.org/abs/1904.09675. Bihe Zhao, Pratyush Maini, Franziska Boenisch, and Adam Dziedzic. Unlocking post-hoc dataset infer","arxiv_match":true},{"id":"Khg0qee5LV","title":"OmniPersona: Persona-Embodied Agents for Multimodal Conversation Interaction","forum_url":"https://openreview.net/forum?id=Khg0qee5LV","excerpt":". URL https://proceedings.iclr.cc/paper_files/paper/2026/ hash/91a1610c6ed9e02d33f826b46f472b92-Abstract-Conference.html. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. BERTScore: Evaluating Text Generation with BERT. In International Conference on Learning Representations, 2020. URL https://iclr.cc/virtual_2020/poster_SkeHuCVFDr.html. Youliang Zhang, Zhaoyang Li, Duomin Wang, Jiahe Zhang, Deyu Zhou, Zixin Yin, X","arxiv_match":false}],"id":"1904.09675","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":511},{"title":"Chronos-2: From Univariate to Universal Forecasting","arxiv_id":"2510.15821","year":2025,"url":"https://arxiv.org/abs/2510.15821","citing_submissions":205,"explicit_arxiv_matches":186,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":"arning Research, 2024. Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, and Michael Bohlke-Schneider. Chronos-2: From Univariate to Universal Forecasting. arXiv preprint arXiv:2510.15821, 2025. Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei. Transformers as Statisticians: Pro","arxiv_match":true},{"id":"sGVZRmZQzK","title":"Amplifying Privacy Attacks on Tabular Foundation Models via a Stolen Surrogate","forum_url":"https://openreview.net/forum?id=sGVZRmZQzK","excerpt":"i, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, and Michael Bohlke-Schneider. Chronos-2: From univariate to universal forecasting. CoRR, abs/2510.15821, 2025. doi: 10.48550/ARXIV.2510.15821. URL https://doi.org/10.48550/arXiv. 2510.15821. Giuseppe Ateniese, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, a","arxiv_match":false},{"id":"iaMmGPCgNV","title":"Recursive Inference for Test-Time Compute Allocation in Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=iaMmGPCgNV","excerpt":"tR. Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, and Michael Bohlke-Schneider. Chronos-2: From univariate to universal forecasting, 2025. URL https://arxiv.org/abs/2510.15821. Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harut","arxiv_match":true}],"id":"2510.15821","metadata_source":"Cached arXiv API metadata","rank":512},{"title":"Api-bank: A comprehensive benchmark for tool-augmented llms","arxiv_id":"2304.08244","year":2023,"url":"https://arxiv.org/abs/2304.08244","citing_submissions":204,"explicit_arxiv_matches":41,"examples":[{"id":"o7ODDwTMgU","title":"Valid but Wrong: Decomposing Language-Model Errors on Closed Vocabularies","forum_url":"https://openreview.net/forum?id=o7ODDwTMgU","excerpt":"Schema key wording as an instruction channel in structured generation under constrained decoding, 2026. URL https://arxiv.org/abs/2604.14862. Hanwool Lee. (g)i-dle: Generative inference via distribution-preserving logit exclusion with kl divergence minimization for constrained decoding, 2025. URL https://arxiv.org/abs/ 2503.18050. Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, et al. Api-bank: A comprehensive benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244, 2023. Yongmin Li, Jia Li, Ge Li, and Zhi Jin. Adaptrack: Constrained decoding without distorting l","arxiv_match":true},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"erse, realistic, and long-horizon task execution. arXiv preprint arXiv:2510.25726, 2025. Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. Api-bank: A comprehensive benchmark for tool-augmented llms. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 3102–3116, 2023. Xidong Li, Mingyu Guo, Chenchao Xu, Bailin Li, Wenjing Zhu, Yangang Zou, Rui Chen, and","arxiv_match":false},{"id":"CFVCzCNC1l","title":"Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=CFVCzCNC1l","excerpt":"execution. In The Fourteenth International Conference on Learning Representations, 2026. Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. API-Bank: A comprehensive benchmark for tool-augmented LLMs. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 3102–3116, Singapore, 2023. Association for Computati","arxiv_match":false}],"id":"2304.08244","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":513},{"title":"Invariant risk minimization","arxiv_id":"1907.02893","year":2019,"url":"https://arxiv.org/abs/1907.02893","citing_submissions":204,"explicit_arxiv_matches":175,"examples":[{"id":"Y15G7fdvPv","title":"S$^2$Aligner: Pair-Efficient Transferable Pre-Training for Sparse Text-Attributed Graphs","forum_url":"https://openreview.net/forum?id=Y15G7fdvPv","excerpt":" Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019. William Brannon, Wonjune Kang, Suyash Fulay, Hang Jiang, Brandon Roy, Deb Roy, and Jad Kabba","arxiv_match":true},{"id":"0M6c2bonFJ","title":"QuEST: Quality-guided Evidence Selection and Temporal State Control for Event-based Object Detection","forum_url":"https://openreview.net/forum?id=0M6c2bonFJ","excerpt":" Soikat Hasan Ahmed, Jan Finkbeiner, and Emre Neftci. Efficient event-based object detection: a hybrid neural network with spatial and temporal attention. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13970–13979. IEEE, 2025. Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019. Marco Cannici, Marco Ciccone, Andrea Romanoni, and Matteo Matteucci. Asynchronous convolutio","arxiv_match":true},{"id":"j7P5aVzmSN","title":"Causal Experience Instantiation: Grounding Procedural Memory with Temporal Graphs for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=j7P5aVzmSN","excerpt":" Martı́n Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019. Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Giani","arxiv_match":true}],"id":"1907.02893","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":514},{"title":"Mixed precision training","arxiv_id":"1710.03740","year":2017,"url":"https://arxiv.org/abs/1710.03740","citing_submissions":204,"explicit_arxiv_matches":30,"examples":[{"id":"U9lsoJxNae","title":"Beyond Confidence? Auditing Routing Entropy in Attention-Residual Transformers","forum_url":"https://openreview.net/forum?id=U9lsoJxNae","excerpt":"ng Representations, 2017. Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu. Mixed precision training. In International Conference on Learning Representations, 2018. Elizbar A Nadaraya. On estimating regression. Theory of Probability & Its Applications, 9(1):141–142, 1964. Mahdi Pakdaman Naeini, Greg","arxiv_match":false},{"id":"DmH3HpzlV6","title":"From Parts to Sim: Bottom-Up Autoregressive Generation of  Sim-Ready 3D Assets","forum_url":"https://openreview.net/forum?id=DmH3HpzlV6","excerpt":"ak learning rate of 5 × 10−5 , cosine decay to 10−7 , a linear warm-up from zero over the first 1,000 updates, and an effective constant weight decay of 0.033; we use a global batch size of 256, BF16 mixed-precision training, global gradient-norm clipping at 1.0, and a random seed of 42 for model, data, and modality sampling. The model is optimized for 37,000 steps in total, after which the training loss exhibits no appr","arxiv_match":false},{"id":"MOLTwuUqhX","title":"Intrinsic Alignment by Generation: Monocular Hand–Object Reconstruction via Interaction Scaffolds","forum_url":"https://openreview.net/forum?id=MOLTwuUqhX","excerpt":"ntinuous decoder with local residual and graph correction modules, producing a hand/object probability field and the final binary segmentation. Both stages use AdamW with a warmup-cosine schedule and mixed-precision training. The synthetic stages use up to eight NVIDIA A800 GPUs and take about 14 hours in total. Real-world data refinement fine-tuning. Every adapted variant, including the formulation and component ablatio","arxiv_match":false}],"id":"1710.03740","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":515},{"title":"Defeating Prompt Injections by Design","arxiv_id":"2503.18813","year":2025,"url":"https://arxiv.org/abs/2503.18813","citing_submissions":203,"explicit_arxiv_matches":170,"examples":[{"id":"FCnWjmOlAg","title":"PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust","forum_url":"https://openreview.net/forum?id=FCnWjmOlAg","excerpt":"/doi.org/10.48550/arXiv. 2406.13352. Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan, Jamie Hayes, Nicholas Carlini, Daniel Fabian, Christoph Kern, Chongyang Shi, Andreas Terzis, and Florian Tramèr. Defeating Prompt Injections by Design. arXiv, 2025. URL https://doi.org/10.48550/arXiv.2503. 18813. Shen Dong, Shaochen Xu, Pengfei He, Yige Li, Jiliang Tang, Tianming Liu, Hui Liu, and Zhen Xiang. Memory Injection Attacks on LLM Agents ","arxiv_match":false},{"id":"TO4R4c6u7V","title":"From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents","forum_url":"https://openreview.net/forum?id=TO4R4c6u7V","excerpt":"ms, volume 37, pp. 82895– 82920. Curran Associates, Inc., 2024. doi: 10.52202/079017-2636. URL https://procee dings.neurips.cc/paper_files/paper/2024/file/97091a5177d8dc64b1d a8bf3e1f6fb54-Paper-Datasets_and_Benchmarks_Track.pdf. Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan, Jamie Hayes, Nicholas Carlini, Daniel Fabian, Christoph Kern, Chongyang Shi, Andreas Terzis, and Florian Tramèr. Defeating prompt injections by design, 2025. URL https://arxiv.org/abs/2503.18813. Adrienne Porter Felt, Helen J. Wang, Alexander Moshchuk, Steve Hanna, and Erika Chin. Permission R","arxiv_match":true},{"id":"81BK0yhhXO","title":"Document-Channel Manipulation in a Text-Extracted AI Review Pipeline","forum_url":"https://openreview.net/forum?id=81BK0yhhXO","excerpt":"nce peer review: Revisiting the 2014 neurips experiment. arXiv preprint arXiv:2109.09774, 2021. Mike D’Arcy, Tom Hope, Larry Birnbaum, and Doug Downey. MARG: Multi-Agent Review Generation for Scientific Papers. arXiv preprint arXiv:2401.04259, 2024. Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan, Jamie Hayes, Nicholas Carlini, Daniel Fabian, Christoph Kern, Chongyang Shi, Andreas Terzis, and Florian Tramèr. Defeating Prompt Injections by Design. arXiv preprint arXiv:2503.18813, 2025. Nils Dycke and Iryna Gurevych. Automatic Reviewers Fail to Detect Faulty Reasoning in Resear","arxiv_match":true}],"id":"2503.18813","metadata_source":"Cached arXiv API metadata","rank":516},{"title":"Executable code actions elicit better llm agents","arxiv_id":"2402.01030","year":2024,"url":"https://arxiv.org/abs/2402.01030","citing_submissions":203,"explicit_arxiv_matches":66,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"a Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models, 2025. URL https://arxiv.org/abs/2503.20314. Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Peng, Hao Li, Hao Peng, and Heng Ji. Executable code actions elicit better LLM agents. In International Conference on Machine Learning, 2024. arXiv:2402.01030. Wenli Xiao, Jia Xie, Tonghe Zhang, Haotian Lin, Letian ”Max” Fu, Haoru Xue, Jalen Lu, Yi Yang, Cun","arxiv_match":true},{"id":"O9NsQ0QEnr","title":"DailyBrick: Language-Guided Synthesis of Brick Assembly Instructions","forum_url":"https://openreview.net/forum?id=O9NsQ0QEnr","excerpt":"nguage models. Transactions on Machine Learning Research, 2024a. URL https://openreview.net/ forum?id=ehfRiF0R3a. Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. Executable code actions elicit better LLM agents. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 50208– 50232. PMLR, 2024b. URL https://proceedings.mlr.press/v235","arxiv_match":false},{"id":"GopmTX5QGu","title":"Harness Evolution as Learning: Approximation, Generalization, and Optimization Limits of Self-Improving Personal Agents","forum_url":"https://openreview.net/forum?id=GopmTX5QGu","excerpt":"al. A survey on large language model based autonomous agents. Frontiers of computer science, 18(6):186345, 2024a. Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. Executable code actions elicit better LLM agents. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Proceedings of the 41st International Conference on Machine Learn","arxiv_match":false}],"id":"2402.01030","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":517},{"title":"Tofu: A task of fictitious unlearning for llms","arxiv_id":"2401.06121","year":2024,"url":"https://arxiv.org/abs/2401.06121","citing_submissions":203,"explicit_arxiv_matches":93,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"Computational Linguistics. doi: 10.18653/v1/2021.acl-long.353. URL https://aclanthology.org/2021.acl-long.353/. Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary Chase Lipton, and J Zico Kolter. TOFU: A task of fictitious unlearning for LLMs. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=B41hNBoWLo. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations ","arxiv_match":false},{"id":"bdZAOqTSho","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","forum_url":"https://openreview.net/forum?id=bdZAOqTSho","excerpt":"ylan Hadfield-Menell. Eight methods to evaluate robust unlearning in LLMs. arXiv preprint arXiv:2402.16835, 2024. Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter. TOFU: A task of fictitious unlearning for LLMs. In COLM, 2024. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in GPT. In NeurIPS, 2022. Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Be","arxiv_match":false},{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":"nt axis: Situating and stabilizing the default persona of language models. arXiv preprint arXiv:2601.10387, 2026. Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter. TOFU: A task of fictitious unlearning for LLMs. In COLM, 2024. Samuel Marks, Jack Lindsey, and Chris Olah. The persona selection model: Why AI assistants might behave like humans. Anthropic Alignment Science Blog, 2026. URL https://alignment.an t","arxiv_match":false}],"id":"2401.06121","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":518},{"title":"Negative preference optimization: From catastrophic collapse to effective unlearning","arxiv_id":"2404.05868","year":2024,"url":"https://arxiv.org/abs/2404.05868","citing_submissions":202,"explicit_arxiv_matches":104,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"rom large language models. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=ZGkfoufDaU. Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. Negative preference optimization: From catastrophic collapse to effective unlearning. In First Conference on Language Modeling, 2024. URL https: //openreview.net/forum?id=MXLBXjQkmb. Ziyu Zhao, Leilei Gan, Guoyin Wang, Yuwei Hu, Tao Shen, Hongxia Yang, Kun Kuang, and Fei Wu. Retrieva","arxiv_match":false},{"id":"Wui5vF8IQf","title":"E$^2$PO: Concept Erasing in Text-to-Video Models via Energy-based Erasing Preference Optimization","forum_url":"https://openreview.net/forum?id=Wui5vF8IQf","excerpt":"oncept erasing method for text-to-video diffusion models. arXiv preprint arXiv:2505.17550, 2025. Jaehong Yoon, Shoubin Yu, Vaidehi Patil, Huaxiu Yao, and Mohit Bansal. Safree: Training-free and adaptive guard for safe text-to-image and video generation. In International Conference on Learning Representations, 2025. Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. Negative preference optimization: From catastrophic collapse to effective unlearning. arXiv preprint arXiv:2404.05868, 2024a. Yimeng Zhang, Xin Chen, Jinghan Jia, Yihua Zhang, Chongyu Fan, Jiancheng Liu, Mingyi Hong, ","arxiv_match":true},{"id":"bdZAOqTSho","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","forum_url":"https://openreview.net/forum?id=bdZAOqTSho","excerpt":"n, Jiaheng Wei, Tong Zhou, Kang Liu, Jun Zhao, and Yubo Chen. RULE: Reinforcement unlearning achieves forget-retain pareto optimality. In NeurIPS, 2025. Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. Negative preference optimization: From catastrophic collapse to effective unlearning. In COLM, 2024. Yang Zhao, Hao Zhang, and Xiuyuan Hu. Penalizing gradient norm for efficiently improving generalization in deep learning. In ICML, 2022. Andy Zou, Long Phan, Sarah Chen, James Campbel","arxiv_match":false}],"id":"2404.05868","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":519},{"title":"Adaptive computation time for recurrent neural networks","arxiv_id":"1603.08983","year":2016,"url":"https://arxiv.org/abs/1603.08983","citing_submissions":201,"explicit_arxiv_matches":188,"examples":[{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":"eiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. Scaling up test-time compute with latent reasoning: A recurrent depth approach. In Advances in Neural Information Processing Systems (NeurIPS), 2025. URL https://arxiv.org/abs/2502.05171. Alex Graves. Adaptive computation time for recurrent neural networks. arXiv preprint arXiv:1603.08983, 2016. URL https://arxiv.org/abs/1603.08983. Alexia Jolicoeur-Martineau. Less is more: Recursive reasoning with tiny networks. arXiv preprint a","arxiv_match":true},{"id":"1wsVNB7eBv","title":"WHEN ROUTING PROBES IMPROVE WITHOUT NEW INFORMATION: AUDITING UNCERTAINTY BEYOND MODEL OUTPUTS","forum_url":"https://openreview.net/forum?id=1wsVNB7eBv","excerpt":"10.11929. William Fedus, Barret Zoph, and Noam Shazeer. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 23(120):1–39, 2022. Tilmann Gneiting and Adrian E. Raftery. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477):359–378, 2007. Alex Graves. Adaptive computation time for recurrent neural networks. arXiv:1603.08983, 2016. arXiv preprint Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of ","arxiv_match":true},{"id":"iaMmGPCgNV","title":"Recursive Inference for Test-Time Compute Allocation in Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=iaMmGPCgNV","excerpt":"erence, pp. 41340–41391, 2025. doi: 10.52202/085713-1380. Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. MOMENT: A family of open time-series foundation models. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 16115–16152. PMLR, 2024. Alex Graves. Adaptive computation time for recurrent neural networks, 2017. URL https:// arxiv.org/abs/1603.08983. Klaus Greff, Rupesh K. Srivastava, and Jürgen Schmidhuber. Highway and residual networks learn un","arxiv_match":true}],"id":"1603.08983","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":520},{"title":"Learning universal policies via text-guided video generation","arxiv_id":"2302.00111","year":2023,"url":"https://arxiv.org/abs/2302.00111","citing_submissions":201,"explicit_arxiv_matches":30,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":" diffusion. The International Journal of Robotics Research, 44(10-11):1684–1704, 2025. Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation. In Advances in Neural Information Processing Systems (NeurIPS), 2023. Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and J","arxiv_match":false},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"aper_files/paper/2026/hash/ a9500ba57161026d7f3f17f9f88d9a90-Abstract-Conference.html. Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation. In Advances in Neural Information Processing Systems, 2023. URL https://proceedings.neurips.cc/paper_files/paper/2023/hash/ 1d5b9233ad716a43be5c0d3023cb82d0-Abstract-Conference.html. Yilun Du, Sherr","arxiv_match":false},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"forcement learning for vision-based robotic control. arXiv preprint arXiv:1812.00568, 2018. Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation. Advances in neural information processing systems, 36:9156–9172, 2023. Gunnar Farnebäck. Two-frame motion estimation based on polynomial expansion. In Scandinavian conference on Image analysis, pp.","arxiv_match":false}],"id":"2302.00111","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":521},{"title":"Progress measures for grokking via mechanistic interpretability","arxiv_id":"2301.05217","year":2023,"url":"https://arxiv.org/abs/2301.05217","citing_submissions":201,"explicit_arxiv_matches":62,"examples":[{"id":"xRgpRQaqcN","title":"Decodable Need Not Be Reward-Reachable: A Controlled Study of Direct-Answer Learning in RLVR","forum_url":"https://openreview.net/forum?id=xRgpRQaqcN","excerpt":"ee Sun Lee, and Min Lin. Understanding R1-Zero-like training: A critical perspective. arXiv preprint arXiv:2503.20783, 2025. Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt. Progress measures for grokking via mechanistic interpretability. In ICLR, 2023. Phuc Minh Nguyen, Chinh D. La, Duy M. H. Nguyen, Nitesh V. Chawla, Binh T. Nguyen, and Khoa D. Doan. The reasoning boundary paradox: How reinforcement learning constrains language mod","arxiv_match":false},{"id":"W5LtRG0F8Z","title":"Locating, Removing, and Restoring: Auditing the Modular Account of Safety in Instruction-Tuned LLMs","forum_url":"https://openreview.net/forum?id=W5LtRG0F8Z","excerpt":"2022. URL https://proceedings.neurips.cc/paper_files/paper/ 2022/file/6f1d43d5a82a37e89b0665b33bf3a182-Paper-Conference.pdf. Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt. Progress measures for grokking via mechanistic interpretability. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=9XFSbDPmdW. Jingcheng Niu, Andrew Liu, Zining Zhu, and Gerald Penn. What does the know","arxiv_match":false},{"id":"kzNMJ3elVQ","title":"The Limits of Algorithm Acquisition in Sequence Models","forum_url":"https://openreview.net/forum?id=kzNMJ3elVQ","excerpt":"oshua B Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332–1338, 2015. Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt. Progress measures for grokking via mechanistic interpretability. In International Conference on Learning Representations (ICLR), 2023. Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. Building machines that learn and think like people. B","arxiv_match":false}],"id":"2301.05217","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":522},{"title":"Planning with diffusion for flexible behavior synthesis","arxiv_id":"2205.09991","year":2022,"url":"https://arxiv.org/abs/2205.09991","citing_submissions":200,"explicit_arxiv_matches":64,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"Guo-Jun Qi. Reinforcing the diffusion chain of lateral thought with diffusion language models. arXiv preprint arXiv:2505.10446, 2025. Michael Janner, Yilun Du, Joshua B. Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In International Conference on Machine Learning (ICML), 2022. Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Nicklas Majamaki, Navdeep Jaitly, Yi-An Ma, and Lianhui Qin. Ladir: Latent diffusion enh","arxiv_match":false},{"id":"TIpxpT8trX","title":"Auto-Bidding with Disentangled Advertiser Profiles and Train-Free Adaptation","forum_url":"https://openreview.net/forum?id=TIpxpT8trX","excerpt":"an Yu, Jian Xu, and Xiaoqiang Zhu. A unified solution to constrained bidding in online display advertising. In KDD, pp. 2993–3001, 2021. Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In ICML, pp. 9902–9915, 2022. Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit q-learning. In ICLR, pp. 1–13, 2022. Aviral Kumar, Aurick Zhou, George Tuck","arxiv_match":false},{"id":"6FEw1esZ1j","title":"Kuramoto Decision Diffusion Models","forum_url":"https://openreview.net/forum?id=6FEw1esZ1j","excerpt":"ng, and Ury Zhilinsky. π0.7 : a steerable generalist robotic foundation model with emergent capabilities, 2026. Cited on pages 1 and 21. Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In International Conference on Machine Learning, pp. 9902–9915. PMLR, 2022. Cited on pages 1, 3, 5, 6, 8, 20, 38, and 47. Adam Johansen. A tutorial on particle filtering and smoothing: Fifteen years","arxiv_match":false}],"id":"2205.09991","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":523},{"title":"On-Policy Context Distillation for Language Models","arxiv_id":"2602.12275","year":2026,"url":"https://arxiv.org/abs/2602.12275","citing_submissions":198,"explicit_arxiv_matches":184,"examples":[{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"u. Qwen3 technical report, 2025. URL https://arxiv.org/abs/2505.09388. Shuo Yang, Jinyang Wu, Zhengxi Lu, Yuhao Shen, Fan Zhang, Lang Feng, Shuai Zhang, Haoran Luo, Zheng Lian, Zhengqi Wen, and Jianhua Tao. Opid: On-policy skill distillation for agentic reinforcement learning, 2026. URL https://arxiv.org/abs/2606.26790. Tianzhu Ye, Li Dong, Xun Wu, Shaohan Huang, and Furu Wei. On-policy context distillation for language models, 2026. URL https://arxiv.org/abs/2602.12275. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, YuYue, Weinan Dai, Tiantian Fan, Ga","arxiv_match":true},{"id":"AVKgs60Glj","title":"Flux-OPD: On-Policy Distillation with Evolving Contexts","forum_url":"https://openreview.net/forum?id=AVKgs60Glj","excerpt":"u Ye, Li Dong, Guanheng Chen, He Zhu, Xun Wu, Shaohan Huang, and Furu Wei. Llm-asa-coach: Experiential learning for non-verifiable tasks. arXiv preprint arXiv:2607.18110, 2026a. Tianzhu Ye, Li Dong, Qingxiu Dong, Xun Wu, Shaohan Huang, and Furu Wei. Online experiential learning for language models. arXiv preprint arXiv:2603.16856, 2026b. Tianzhu Ye, Li Dong, Xun Wu, Shaohan Huang, and Furu Wei. On-policy context distillation for language models. arXiv preprint arXiv:2602.12275, 2026c. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover.","arxiv_match":true},{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":" Nan Duan. Self-distilled rlvr, 2026a. URL https://arxiv.org/ abs/2604.03128. Wenkai Yang, Weijie Liu, Ruobing Xie, Kai Yang, Saiyong Yang, and Yankai Lin. Learning beyond teacher: Generalized on-policy distillation with reward extrapolation, 2026b. URL https: //arxiv.org/abs/2602.12125. Tianzhu Ye, Li Dong, Xun Wu, Shaohan Huang, and Furu Wei. On-policy context distillation for language models. arXiv preprint arXiv:2602.12275, 2026. URL https://arxiv.org/abs/ 2602.12275. Xinlei Yu, Gen Li, Qingyi Si, Guibin Zhang, Yuqi Xu, Congcong Wang, Shuai Dong, Kaiwen Tuo, Xiangy","arxiv_match":true}],"id":"2602.12275","metadata_source":"Cached arXiv API metadata","rank":524},{"title":"RouteLLM: Learning to route LLMs with preference data","arxiv_id":"2406.18665","year":2024,"url":"https://arxiv.org/abs/2406.18665","citing_submissions":198,"explicit_arxiv_matches":123,"examples":[{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":" scientific and algorithmic discovery. arXiv preprint arXiv:2506.13131, 2025. URL https://arxiv. org/abs/2506.13131. NVIDIA. NVIDIA Nemotron 3 Ultra, 2026. URL https://research.nvidia.com/labs/ nemotron/Nemotron-3-Ultra/. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M. Waleed Kadous, and Ion Stoica. RouteLLM: Learning to route LLMs with preference data. arXiv preprint arXiv:2406.18665, 2024. URL https://arxiv.org/abs/2406.18665. OpenAI. Introducing SWE-bench Verified, 2024. introducing-swe-bench-verified/. URL https://openai.","arxiv_match":true},{"id":"4A9bWVkxOC","title":"Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation","forum_url":"https://openreview.net/forum?id=4A9bWVkxOC","excerpt":"ference on Learning Representations, volume 2024, pages 39578–39601, 2024. Rohin Manvi, Anikait Singh, and Stefano Ermon. Adaptive inference-time compute: Llms can predict if they can do better, even mid-generation. arXiv preprint arXiv:2410.02725, 2024. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M Waleed Kadous, and Ion Stoica. Routellm: Learning to route llms with preference data, 2025a. URL https://arxiv.org/abs/2406.18665. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E Gonzalez, Mohammed Ka","arxiv_match":true},{"id":"z409Nct6AS","title":"EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL","forum_url":"https://openreview.net/forum?id=z409Nct6AS","excerpt":"Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36, 2023. URL https://arxiv.org/abs/2303.17651. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M. Waleed Kadous, and Ion Stoica. Routellm: Learning to route llms with preference data. arXiv preprint arXiv:2406.18665, 2024. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong ","arxiv_match":true}],"id":"2406.18665","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":525},{"title":"Agent security bench (ASB): Formalizing and benchmarking attacks and defenses in LLM-based agents","arxiv_id":"2410.02644","year":2024,"url":"https://arxiv.org/abs/2410.02644","citing_submissions":197,"explicit_arxiv_matches":43,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"e on Learning Representations, 2023. URL https://openreview.net/forum? id=WE_vluYUL-X. Hanrong Zhang, Jingyuan Huang, Kai Mei, Yifei Yao, Zhenting Wang, Chenlu Zhan, Hongwei Wang, and Yongfeng Zhang. Agent security bench (ASB): Formalizing and benchmarking attacks and defenses in LLM-based agents. In The Thirteenth International Conference on Learning Representations, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/ hash/5750f91d8fb9d5c02bd8ad2c3b44456b-Abstract-Conference.html. ","arxiv_match":false},{"id":"L8JbHpIlSq","title":"\"You're Right, Let Me Fix It\": How LLM Agents Damage Correct Work When Falsely Accused","forum_url":"https://openreview.net/forum?id=L8JbHpIlSq","excerpt":"t safety. arXiv preprint arXiv:2604.18658, 2026. URL https://arxiv.org/abs/2604.18658. Hanrong Zhang, Jingyuan Huang, Kai Mei, Yifei Yao, Zhenting Wang, Chenlu Zhan, Hongwei Wang, and Yongfeng Zhang. Agent security bench (ASB): Formalizing and benchmarking attacks and defenses in LLM-based agents. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=V4y0CpX4hK. Jingnan Zheng, Yanzhen Luo, Jingjun Xu, Bingnan Liu, Yuxin Chen, Chenhan","arxiv_match":false},{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"es against indirect prompt injection attacks on llm agents. In NAACL (Findings), 2025. Hanrong Zhang, Jingyuan Huang, Kai Mei, Yifei Yao, Zhenting Wang, Chenlu Zhan, Hongwei Wang, and Yongfeng Zhang. Agent security bench (ASB): Formalizing and benchmarking attacks and defenses in LLM-based agents. In ICLR, 2025a. Maosen Zhang, Jianshuo Dong, Boting Lu, Wenyue Li, Xiaoping Zhang, Tianwei Zhang, Jie Zhang, and Han Qiu. The model’s tell: Measuring context-leakage attack signals with behavior gau","arxiv_match":false}],"id":"2410.02644","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":526},{"title":"Chatbot arena: An open platform for evaluating llms by human preference","arxiv_id":"2403.04132","year":2024,"url":"https://arxiv.org/abs/2403.04132","citing_submissions":197,"explicit_arxiv_matches":58,"examples":[{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":"ional Linguistics, 2024. Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael Jordan, Joseph E. Gonzalez, and Ion Stoica. Chatbot Arena: An open platform for evaluating LLMs by human preference. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 8359–8388. PMLR, 2024. Muthu Chidambaram and Rong Ge. Reassessing","arxiv_match":false},{"id":"3udVm9Jxne","title":"You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference","forum_url":"https://openreview.net/forum?id=3udVm9Jxne","excerpt":"ng Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021. Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E Gonzalez, et al. Chatbot arena: An open platform for evaluating llms by human preference. arXiv preprint arXiv:2403.04132, 2024. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthi","arxiv_match":true},{"id":"lVDgIg7kcz","title":"Locality Guided Self-Distillation","forum_url":"https://openreview.net/forum?id=lVDgIg7kcz","excerpt":"g/CorpusID:263610088. Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, DaCheng Li, Hao Zhang, Banghua Zhu, Michael I. Jordan, Joseph E. Gonzalez, and Ion Stoica. Chatbot arena: An open platform for evaluating llms by human preference. In International Conference on Machine Learning, 2024. URL https://api.semanticscholar.org/ CorpusID:268264163. Eunbi Choi, Yongrae Jo, Joel Jang, Joonwon Jang, and Minjoon Seo. Fixed input paramete","arxiv_match":false}],"id":"2403.04132","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":527},{"title":"OmniQuant: Omnidirectionally calibrated quantization for large language models","arxiv_id":"2308.13137","year":2023,"url":"https://arxiv.org/abs/2308.13137","citing_submissions":197,"explicit_arxiv_matches":47,"examples":[{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"e. Code llama: Open foundation models for code. CoRR, abs/2308.12950, 2023. Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. Anke Tang, Li Shen, Yong Luo, Han Hu, Bo Du, and Dacheng Tao. F","arxiv_match":false},{"id":"vSqsA99XH4","title":"Quantization-Aware Pre-Training with Constrained Empirical Weight Distribution","forum_url":"https://openreview.net/forum?id=vSqsA99XH4","excerpt":" 10.1007/s11263-015-0816-y. URL https://doi. org/10.1007/s11263-015-0816-y. Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=8Wuvhh0LYW. Xuan Shen, Zhenglun Kong, Changdi Yang, Zhaoyang Han, Lei Lu, Peiyan Dong, ","arxiv_match":false},{"id":"j8fLzLZ27h","title":"MirrorQuant: Learnable Householder Rotations for Low-Bit LLM Quantization","forum_url":"https://openreview.net/forum?id=j8fLzLZ27h","excerpt":"irical Methods in Natural Language Processing (EMNLP), pp. 4462–4472, 2019. Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. In Proceedings of the International Conference on Learning Representations (ICLR), 2024. Yuantian Shao, Yuanteng Chen, Peisong Wang, Jianlin Yu, Jing Lin, Yiwu Yao, Zhihui Wei, and Jian Cheng. Dartq","arxiv_match":false}],"id":"2308.13137","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":528},{"title":"Compressive transformers for long-range sequence modelling","arxiv_id":"1911.05507","year":2019,"url":"https://arxiv.org/abs/1911.05507","citing_submissions":196,"explicit_arxiv_matches":83,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"xtension of large language models. In The Twelfth International Conference on Learning Representations, 2024. Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, Chloe Hillier, and Timothy P Lillicrap. Compressive transformers for long-range sequence modelling. In The Eighth International Conference on Learning Representations, 2020. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. SQuAD: 100,000+ questions for machine comprehension of te","arxiv_match":false},{"id":"eipQbEsWLA","title":"Adaptive Consistency Graph for Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=eipQbEsWLA","excerpt":"ehavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, 2023. doi: 10.1145/3586183.3606763. URL https://doi.org/10.1145/3586183.3606763. Jack W. Rae et al. Compressive transformers for long-range sequence modelling. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum? id=SylKikSYDH. Noah Shinn et al. Reflexion: Language agents with verbal reinforcement learning. In Ad","arxiv_match":false},{"id":"1ihfTCOu91","title":"Adamas: Hadamard Sparse Attention for Efficient Long-context Inference","forum_url":"https://openreview.net/forum?id=1ihfTCOu91","excerpt":"y Quesnelle, Honglu Fan, and Enrico Shippole. YaRN: Efficient context window extension of large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=wHBfxhZu1u. Qwen Team. Qwen3 technical report, 2025. URL https://arxiv.org/abs/2505.09388. Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap. Compressive transformers for long-range sequence modelling. arXiv preprint arXiv:1911.05507, 2019. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, an","arxiv_match":true}],"id":"1911.05507","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":529},{"title":"Distillm: Towards streamlined distillation for large language models","arxiv_id":"2402.03898","year":2024,"url":"https://arxiv.org/abs/2402.03898","citing_submissions":196,"explicit_arxiv_matches":79,"examples":[{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"tos, Yaron Singer, and Amin Karbasi. Llama-3.1-foundationai-securityllm-base-8b technical report, 2025. URL https: //arxiv.org/abs/2504.21039. Jongwoo Ko, Sungnyun Kim, Tianyi Chen, and Se-Young Yun. DistiLLM: Towards streamlined distillation for large language models. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Proceedings of the 41st International Conference on Machine Learn","arxiv_match":false},{"id":"ndjcK7Ozn2","title":"Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation","forum_url":"https://openreview.net/forum?id=ndjcK7Ozn2","excerpt":"016, pp. 1317– 1327. The Association for Computational Linguistics, 2016. doi: 10.18653/V1/D16-1139. URL https://doi.org/10.18653/v1/d16-1139. Jongwoo Ko, Sungnyun Kim, Tianyi Chen, and Se-Young Yun. Distillm: Towards streamlined distillation for large language models. In Ruslan Salakhutdinov, Zico Kolter, Katherine A. Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Forty-first International Conference on Machine Learning, ICML","arxiv_match":false},{"id":"nStFNVfn07","title":"Matching While Alive: A Survival View of On-Policy Trajectory Distillation","forum_url":"https://openreview.net/forum?id=nStFNVfn07","excerpt":"on from incomplete observations. Journal of the American Statistical Association, 53(282):457-481, 1958. doi: 10.1080/01621459.1958. 10501452. Jongwoo Ko, Sungnyun Kim, Tianyi Chen, and Se-Young Yun. DistiLLM: Towards streamlined distillation for large language models. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 24872-24895. PMLR, 2024. URL https://proceedings.mlr.press/v235/k","arxiv_match":false}],"id":"2402.03898","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":530},{"title":"Entropy-Aware On-Policy Distillation of Language Models","arxiv_id":"2603.07079","year":2026,"url":"https://arxiv.org/abs/2603.07079","citing_submissions":196,"explicit_arxiv_matches":139,"examples":[{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":" dissecting on-policy self-distillation. arXiv preprint arXiv:2608.09228, 2026. Woogyeol Jin, Taywon Min, Yongjin Yang, Dennis Wei, Yi Zhou, Swanand Ravindra Kadhe, Nathalie Baracaldo, and Kimin Lee. Entropy-aware on-policy distillation of language models. In Forty-third International Conference on Machine Learning, 2026. URL https: //openreview.net/forum?id=J5i09faOOf. Simran Kaur, Narutatsu Ri, Yinghui He, Liam Fowl, and Sanjeev Arora. Rethinking on","arxiv_match":false},{"id":"IkzBak1MYU","title":"RL Starts Before RL: Rethinking Policy Initialization for Outcome-Based RL","forum_url":"https://openreview.net/forum?id=IkzBak1MYU","excerpt":", Shaosheng Cao, Zheyu Ye, Fei Zhao, Yao Hu, and Shaohui Lin. Vision-R1: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749, 2025. URL https://arxiv.org/abs/2503.06749. Woogyeol Jin, Taywon Min, Yongjin Yang, Dennis Wei, Yi Zhou, Swanand Ravindra Kadhe, Nathalie Baracaldo, and Kimin Lee. Entropy-aware on-policy distillation of language models. arXiv preprint arXiv:2603.07079, 2026. URL https://arxiv.org/abs/2603.07079. Feiyang Kang, Michael Kuchnik, Karthik Padthe, Marin Vlastelica, Ruoxi Jia, Carole-Jean Wu, and Ne","arxiv_match":true},{"id":"iHdUmEIkbu","title":"Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability","forum_url":"https://openreview.net/forum?id=iHdUmEIkbu","excerpt":"ecodebench: Holistic and contamination free evaluation of large language models for code. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. URL https://openreview.net/forum?id=chfJJYC3iL. Woogyeol Jin, Taywon Min, Yongjin Yang, Dennis Wei, Yi Zhou, Swanand Ravindra Kadhe, Nathalie Baracaldo, and Kimin Lee. Entropy-aware on-policy distillation of language models. arXiv preprint arXiv:2603.07079, 2026. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ra","arxiv_match":true}],"id":"2603.07079","metadata_source":"Cached arXiv API metadata","rank":531},{"title":"Promptbreeder: Self-referential self-improvement via prompt evolution","arxiv_id":"2309.16797","year":2023,"url":"https://arxiv.org/abs/2309.16797","citing_submissions":196,"explicit_arxiv_matches":75,"examples":[{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"hird International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id= OC2z7iSQKa. Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. Promptbreeder: Self-referential self-improvement via prompt evolution. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, volume 235 of Proceedings of Machine Learning Research, pp. 13481–13544. PMLR / OpenReview.","arxiv_match":false},{"id":"a0TQ3mSSY5","title":"NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents","forum_url":"https://openreview.net/forum?id=a0TQ3mSSY5","excerpt":"ngs.iclr.cc/paper_files/paper/2026/ hash/a05b72653ec5b473732129829ae04195-Abstract-Conference.html. Chrisantha Fernando, Dylan Sunil Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. Promptbreeder: Self-referential self-improvement via prompt evolution. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 13481–13544, 2024. URL https://proceedings. mlr.press/v235/fernan","arxiv_match":false},{"id":"0XEgr94mKk","title":"ACT: Agentic Text–Scene Co-Design and Dual Evolution for Informative Video Generation","forum_url":"https://openreview.net/forum?id=0XEgr94mKk","excerpt":" Forouzan Fallah, Maitreya Patel, Agneet Chatterjee, Vlad Morariu, Chitta Baral, and Yezhou Yang. Textinvision: Text and prompt complexity driven visual text generation benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 525–534, 2025. Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. Promptbreeder: Self-referential self-improvement via prompt evolution. arXiv preprint arXiv:2309.16797, 2023. Xuyang Guo, Jiayan Huo, Zhenmei Shi, Zhao Song, Jiahao Zhang, and Jiale Zhao. T2vtextbench: ","arxiv_match":true}],"id":"2309.16797","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":532},{"title":"DSPy: Compiling declarative language model calls into selfimproving pipelines","arxiv_id":"2310.03714","year":2023,"url":"https://arxiv.org/abs/2310.03714","citing_submissions":194,"explicit_arxiv_matches":122,"examples":[{"id":"JmKCJSrhxX","title":"FlowCompile: An Optimizing Compiler for Structured LLM Workflows","forum_url":"https://openreview.net/forum?id=JmKCJSrhxX","excerpt":"jun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code. arXiv preprint arXiv:2403.07974, 2024. Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. Dspy: Compiling declarative language model calls into self-improving pipelines. arXiv preprint arXiv:2310.03714, 2023. Hsiang-Tsung Kung, Fabrizio Luccio, and Franco P Preparata. On finding the maxima of a set o","arxiv_match":true},{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":". Version at commit c2488cfa2. Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma, Xu Wang, Qin Wang, and Guangsheng Yu. SoK: Agentic skills–beyond tool use in LLM agents. arXiv preprint arXiv:2602.20867, 2026. Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. DSPy: Compiling declarative language model calls into self-improving pipelines. arXiv preprint arXiv:2310.03714, 2023. Hanchen Li, Runyuan He, Qizheng Zhang, Changxiu Ji, Qiuyang Mang, Xiaokun Chen, Lakshya A Ag","arxiv_match":true},{"id":"07pvXM8POP","title":"Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills","forum_url":"https://openreview.net/forum?id=07pvXM8POP","excerpt":"jo. Collapse or thrive: Perils and promises of synthetic data in a selfgenerating world. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=buwLCdOHxO. Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. Dspy: Compiling declarative language model calls into self-improving pipelines. arXiv preprint arXiv:2310.03714, 2023. Jakub Grudzien Kuba, Mengting Gu, Qi Ma, Yuandong Tian, Vijai Mohan, and Jason Chen. Languag","arxiv_match":true}],"id":"2310.03714","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":533},{"title":"Function vectors in large language models","arxiv_id":"2310.15213","year":2023,"url":"https://arxiv.org/abs/2310.15213","citing_submissions":193,"explicit_arxiv_matches":31,"examples":[{"id":"SMiWjRdzKn","title":"Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors","forum_url":"https://openreview.net/forum?id=SMiWjRdzKn","excerpt":" Cao, Ziwei Chai, Y Charles, et al. Kimi k3: Open frontier intelligence. arXiv preprint arXiv:2607.24653, 2026. Eric Todd, Millicent Li, Arnab Sen Sharma, Aaron Mueller, Byron Wallace, and David Bau. Function vectors in large language models. In International conference on learning representations, volume 2024, pp. 17282–17333, 2024. Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, and Johannes G","arxiv_match":false},{"id":"iXRlVykyko","title":"Native Action-Prior Learning from Videos for World Action Models","forum_url":"https://openreview.net/forum?id=iXRlVykyko","excerpt":"bouzeid, and Ian Reid. Latent action pretraining through world modeling. arXiv preprint arXiv:2509.18428, 2025. Eric Todd, Millicent Li, Arnab Sen Sharma, Aaron Mueller, Byron Wallace, and David Bau. Function vectors in large language models. In International conference on learning representations, volume 2024, pp. 17282–17333, 2024. Shenghua Wan, Xiaohai Hu, Xunlan Zhou, Lei Yuan, Le Gan, and De-Chuan Zhan. Multi-view consistent latent ","arxiv_match":false},{"id":"nY7Q7sYhaL","title":"One-Step Steering is Not Sequential Control","forum_url":"https://openreview.net/forum?id=nY7Q7sYhaL","excerpt":"ion of transfer order. ArXiv, abs/2606.24993, 2026. URL https://api.semanticscholar.org/CorpusID: 289622206. Eric Todd, Millicent Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, and David Bau. Function vectors in large language models. ArXiv, abs/2310.15213, 2023. URL https://api. semanticscholar.org/CorpusID:264439657. Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David S. Udell, Juan J. Vazquez, Ulisse Mini, and Monte Stua","arxiv_match":false}],"id":"2310.15213","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":534},{"title":"Ministral 3","arxiv_id":"2601.08584","year":2026,"url":"https://arxiv.org/abs/2601.08584","citing_submissions":193,"explicit_arxiv_matches":184,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":" Yu Qiao. MVBench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. URL https://arxiv.org/abs/2311.17005. Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian, Victor Jouault, Abhinav Rastogi, Adrien Sadé, Alan Jeffares, Albert Jiang, Alexandre Cahill, Alexandre Gavaudan, et al. Ministral 3. arXiv preprint arXiv:2601.08584, 2026. URL https://arxiv.org/abs/2601.08584. Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, ","arxiv_match":true},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"ge processing, pp. 7421–7432, 2024c. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. Eagle-3: Scaling up inference acceleration of large language models via training-time test. Advances in Neural Information Processing Systems, 38:136737–136756, 2026. Alexander H Liu, Kartik Khandelwal, Sandeep Subramanian, Victor Jouault, Abhinav Rastogi, Adrien Sadé, Alan Jeffares, Albert Jiang, Alexandre Cahill, Alexandre Gavaudan, et al. Ministral 3. arXiv preprint arXiv:2601.08584, 2026. Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Pik","arxiv_match":true},{"id":"07pvXM8POP","title":"Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills","forum_url":"https://openreview.net/forum?id=07pvXM8POP","excerpt":" llms for logical reasoning. arXiv preprint arXiv:2502.01100, 2025. George Ling, Shanshan Zhong, and Richard Huang. Agent skills: A data-driven analysis of claude skills for extending large language model functionality. arXiv preprint arXiv:2602.08004, 2026. Alexander H Liu, Kartik Khandelwal, Sandeep Subramanian, Victor Jouault, Abhinav Rastogi, Adrien Sadé, Alan Jeffares, Albert Jiang, Alexandre Cahill, Alexandre Gavaudan, et al. Ministral 3. arXiv preprint arXiv:2601.08584, 2026a. Bo Liu, Leon Guertler, Simon Yu, Zichen Liu, Penghui Qi, Daniel Balcells, Mickel Liu, Chest","arxiv_match":true}],"id":"2601.08584","metadata_source":"Cached arXiv API metadata","rank":535},{"title":"Mixture-of-agents enhances large language model capabilities","arxiv_id":"2406.04692","year":2024,"url":"https://arxiv.org/abs/2406.04692","citing_submissions":193,"explicit_arxiv_matches":62,"examples":[{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"position. In Transactions of the Association for Computational Linguistics (TACL), 2022. https://aclanthology.org/2022.tacl-1.31/. Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Y. Zou. Mixture-of-agents enhances large language model capabilities. In International Conference on Learning Representations (ICLR), 2025. https://proceedings.iclr.cc/paper_files/paper/2025/hash/ 5434be94e82c54327bb9dcaf7fca52b6-Abstract-Conference.html. Peiyi Wang, ","arxiv_match":false},{"id":"2LN3rZeePs","title":"Latent Bridge: Asking for Help Without Words","forum_url":"https://openreview.net/forum?id=2LN3rZeePs","excerpt":"Qu, Jesse Davis, and Marie-Francine Moens. Event interaction in low-rank bottlenecks for temporal relation extraction. arXiv preprint arXiv:2609.06731, 2026b. URL https://arxiv.org/abs/2609.06731. Qwen Team. Qwen3.5: Towards native multimodal agents, February 2026. URL https://qwen. ai/blog?id=qwen3.5. Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Zou. Mixture-of-agents enhances large language model capabilities, 2024. URL https://arxiv.org/abs/2406.04692, 1(2):5. Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep selfatte","arxiv_match":true},{"id":"z409Nct6AS","title":"EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL","forum_url":"https://openreview.net/forum?id=z409Nct6AS","excerpt":"fect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception, 2026a. URL https://arxiv.org/abs/2604.12255. Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Zou. Mixture-of-agents enhances large language model capabilities. In International Conference on Learning Representations (ICLR), 2025. Yibin Wang, Yuhang Zang, Feng Han, Jiazi Bu, Yujie Zhou, Cheng Jin, and Jiaqi Wang. Unified Personalized Reward Model for Vision","arxiv_match":false}],"id":"2406.04692","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":536},{"title":"Can LLMs express their uncertainty? an empirical evaluation of confidence elicitation in LLMs","arxiv_id":"2306.13063","year":2023,"url":"https://arxiv.org/abs/2306.13063","citing_submissions":192,"explicit_arxiv_matches":52,"examples":[{"id":"r1zsoUu97b","title":"Advancing the Calibration-Compute Frontier of LLM Classifiers via Asymmetric Duos","forum_url":"https://openreview.net/forum?id=r1zsoUu97b","excerpt":"tural Language Processing: System Demonstrations, pp. 38–45. Association for Computational Linguistics, October 2020. Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. In The Twelfth International Conference on Learning Representations, 2024. Adam X. Yang, Maxime Robeyns, Xi Wang, and Laurence Aitchison. Bayesian low-rank adaptation for large language models. In T","arxiv_match":false},{"id":"QJUxAsmCAP","title":"Exploiting Uncertainty Aware Reasoning Ability of Large Language Models for CAD Drawing Completion","forum_url":"https://openreview.net/forum?id=QJUxAsmCAP","excerpt":"e on computer vision and pattern recognition, pp. 2105–2114, 2021. Rundi Wu, Chang Xiao, and Changxi Zheng. Deepcad: A deep generative network for computeraided design models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6772–6782, 2021. Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. arXiv:2306.13063, 2023. Xiang Xu, Karl DD Willis, Joseph G Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, and Ya","arxiv_match":true},{"id":"WGJJwO1uwj","title":"Dispersion Is Not Doubt: Rethinking LLM Confidence Estimation When Multiple Answers Are Valid","forum_url":"https://openreview.net/forum?id=WGJJwO1uwj","excerpt":", Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, et al. Mmlu-pro: A more robust and challenging multitask language understanding benchmark. Advances in Neural Information Processing Systems, 37: 95266–95290, 2024. Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. arXiv preprint arXiv:2306.13063, 2023. Weijie Xu, Shixian Cui, Xi Fang, Chi Xue, Stephanie Eckman, and Chandan K Reddy. Sata-bench:","arxiv_match":true}],"id":"2306.13063","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":537},{"title":"Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling","arxiv_id":"2501.17811","year":2025,"url":"https://arxiv.org/abs/2501.17811","citing_submissions":192,"explicit_arxiv_matches":188,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"bject detection in the wild. In CVPR, 2023. Jiuhai Chen, Le Xue, Zhiyang Xu, Xichen Pan, Shusheng Yang, Can Qin, An Yan, Honglu Zhou, Zeyuan Chen, Lifu Huang, et al. Blip3o-next: Next frontier of native image generation. arXiv preprint arXiv:2510.15857, 2025a. Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling. arXiv preprint arXiv:2501.17811, 2025b. Yiming Chen, Junlin Han, Tianyi Bai, Shengbang Tong, Filippos Kokkinos, and Philip Torr. Fr","arxiv_match":true},{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"print arXiv:2503.09641, 2025b. Junying Chen, Zhenyang Cai, Pengcheng Chen, Shunian Chen, Ke Ji, Xidong Wang, Yunjin Yang, and Benyou Wang. Sharegpt-4o-image: Aligning multimodal models with gpt-4o-level image generation. arXiv preprint arXiv:2506.18095, 2025c. Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling. arXiv preprint arXiv:2501.17811, 2025d. Zhenglin Cheng, Peng Sun, Jianguo Li, and Tao Lin. Twinflow: Realizing one-step generation ","arxiv_match":true},{"id":"V0dfRkM2lE","title":"One Step Generation via Condition Shifting","forum_url":"https://openreview.net/forum?id=V0dfRkM2lE","excerpt":"print arXiv:2503.09641, 2025b. Junying Chen, Zhenyang Cai, Pengcheng Chen, Shunian Chen, Ke Ji, Xidong Wang, Yunjin Yang, and Benyou Wang. Sharegpt-4o-image: Aligning multimodal models with gpt-4o-level image generation. arXiv preprint arXiv:2506.18095, 2025c. Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling. arXiv preprint arXiv:2501.17811, 2025d. Zhenglin Cheng, Peng Sun, Jianguo Li, and Tao Lin. Twinflow: Realizing one-step generation ","arxiv_match":true}],"id":"2501.17811","metadata_source":"Cached arXiv API metadata","rank":538},{"title":"Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems","arxiv_id":"2505.00212","year":2025,"url":"https://arxiv.org/abs/2505.00212","citing_submissions":192,"explicit_arxiv_matches":77,"examples":[{"id":"n7jt1Tnxzl","title":"MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?","forum_url":"https://openreview.net/forum?id=n7jt1Tnxzl","excerpt":" pp. 2995–3021, 2025. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. Textgrad: Automatic\" differentiation\" via text. arXiv preprint arXiv:2406.07496, 2024. Shaokun Zhang, Ming Yin, Jieyu Zhang, Jiale Liu, Zhiguang Han, Jingyang Zhang, Beibin Li, Chi Wang, Huazheng Wang, Yiran Chen, et al. Which agent causes task failures and when? on automated failure attribution of llm multi-agent systems. arXiv preprint arXiv:2505.00212, 2025. Zheyuan Zhang, Lin Ge, Hongjiang Li, Weicheng Zhu, Chuxu Zhang, and Yanfang Ye. Mapro: Recas","arxiv_match":true},{"id":"zCNHISpxH5","title":"CATO: Contrastive Attribution for Targeted Optimization of Multi-Component LLM Agents","forum_url":"https://openreview.net/forum?id=zCNHISpxH5","excerpt":"ng Representations, volume 2026, pp. 86069–86100, 2026c. Shaokun Zhang, Ming Yin, Jieyu Zhang, Jiale Liu, Zhiguang Han, Jingyang Zhang, Beibin Li, Chi Wang, Huazheng Wang, Yiran Chen, and Qingyun Wu. Which agent causes task failures and when? on automated failure attribution of LLM multi-agent systems. In Proceedings of the 42nd International Conference on Machine Learning, volume 267, pp. 76583–76599, 2025b. Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li","arxiv_match":false},{"id":"U9pVI6J3QY","title":"Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels","forum_url":"https://openreview.net/forum?id=U9pVI6J3QY","excerpt":"eep search, 2025b. URL https://arxiv.org/abs/2510.10581. Shaokun Zhang, Ming Yin, Jieyu Zhang, Jiale Liu, Zhiguang Han, Jingyang Zhang, Beibin Li, Chi Wang, Huazheng Wang, Yiran Chen, and Qingyun Wu. Which agent causes task failures and when? on automated failure attribution of llm multi-agent systems. In International Conference on Machine Learning (ICML), 2025c. Chenyang Zhu, Spencer Hong, Jingyu Wu, Kushal Chawla, Yuhui Tang, Youbing Yin, Nathan Wolfe, Erin Babinsky, and Daben Liu. RAFFLES: Rea","arxiv_match":false}],"id":"2505.00212","metadata_source":"Cached arXiv API metadata","rank":539},{"title":"YaRN: Efficient context window extension of large language models","arxiv_id":"2309.00071","year":2023,"url":"https://arxiv.org/abs/2309.00071","citing_submissions":192,"explicit_arxiv_matches":52,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"rence of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5336–5358, 2022. Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. YaRN: Efficient context window extension of large language models. In The Twelfth International Conference on Learning Representations, 2024. Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, Chloe Hillier, and Timothy P Lillicrap. Compressive transformers for long","arxiv_match":false},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":"M Symposium on User Interface Software and Technology, 2023. doi: 10.1145/3586183.3606763. URL https://doi.org/10.1145/3586183.3606763. Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. YaRN: Efficient context window extension of large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=wHBfxhZu1u. Qwen Team. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024.","arxiv_match":false},{"id":"7DrkJNzqyF","title":"LongLive-RAG: A General Retrieval-Augmented Framework for Long Video Generation","forum_url":"https://openreview.net/forum?id=7DrkJNzqyF","excerpt":"e. arXiv preprint arXiv:2509.25161, 2025. Yunhong Lu, Yanhong Zeng, Haobo Li, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Jiapeng Zhu, Hengyuan Cao, Zhipeng Zhang, Xing Zhu, et al. Reward forcing: Efficient streaming video generation with rewarded distribution matching distillation. arXiv preprint arXiv:2512.04678, 2025. Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. Yarn: Efficient context window extension of large language models. arXiv preprint arXiv:2309.00071, 2023. Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, ","arxiv_match":true}],"id":"2309.00071","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":540},{"title":"Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation","arxiv_id":"2602.02214","year":2026,"url":"https://arxiv.org/abs/2602.02214","citing_submissions":191,"explicit_arxiv_matches":138,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"fast distillation of pretrained diffusion models for one-step generation. In International Conference on Machine Learning, 2024. Hongzhou Zhu, Min Zhao, Guande He, Hang Su, Chongxuan Li, and Jun Zhu. Causal forcing: Autoregressive diffusion distillation done right for high-quality real-time interactive video generation. In International Conference on Machine Learning, 2026. Kai Zou, Dian Zheng, Hongbo Liu, Tiankai Hang, Bin Liu, and Nenghai Yu. Hiar: Efficient autoregressive long video generation via hierarchical d","arxiv_match":false},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":" video generation model. arXiv preprint arXiv:2603.04379, 2026. Min Zhao, Guande He, Yixiao Chen, Hongzhou Zhu, Chongxuan Li, and Jun Zhu. Riflex: A free lunch for length extrapolation in video diffusion transformers. arXiv preprint arXiv:2502.15894, 2025. Hongzhou Zhu, Min Zhao, Guande He, Hang Su, Chongxuan Li, and Jun Zhu. Causal forcing: Autoregressive diffusion distillation done right for high-quality real-time interactive video generation. arXiv preprint arXiv:2602.02214, 2026. A ","arxiv_match":true},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":"Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in neural information processing systems, 36:46595–46623, 2023. Hongzhou Zhu, Min Zhao, Guande He, Hang Su, Chongxuan Li, and Jun Zhu. Causal forcing: Autoregressive diffusion distillation done right for high-quality real-time interactive video generation. In Forty-third International Conference on Machine Learning, 2026. URL https: //openreview.net/forum?id=BYInOck3gr. Shaobin Zhuang, Kunchang Li, Xinyuan Chen, Yaohui Wang, Ziwei Liu, Yu Qiao, and Ya","arxiv_match":false}],"id":"2602.02214","metadata_source":"Cached arXiv API metadata","rank":541},{"title":"UI-TARS: Pioneering Automated GUI Interaction with Native Agents","arxiv_id":"2501.12326","year":2025,"url":"https://arxiv.org/abs/2501.12326","citing_submissions":191,"explicit_arxiv_matches":178,"examples":[{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pp. 1–22, 2023. Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, et al. Ui-tars: Pioneering automated gui interaction with native agents. arXiv preprint arXiv:2501.12326, 2025. Qwen Team. Qwen3.5: Towards native multimodal agents, February 2026. URL https://qwen. ai/bl","arxiv_match":true},{"id":"MREjTH2FlB","title":"Seeing What Changed: Transition-Guided Credit Assignment for GUI Agents","forum_url":"https://openreview.net/forum?id=MREjTH2FlB","excerpt":"e curriculum reinforcement learning. In International Conference on Learning Representations, volume 2025, pp. 79791–79821, 2025. Jiachen Qian. VisCritic: Visual state comparison as process reward for GUI agents. arXiv preprint arXiv:2606.24525, 2026. Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, et al. UI-TARS: Pioneering automated GUI interaction with native agents. arXiv preprint arXiv:2501.12326, 2025. Qwen Team. Qwen3.5-9B model card. https://huggingface.co/Qwen/Qwen3.5-9B, February 2026a. Qw","arxiv_match":true},{"id":"IMcQz8TBut","title":"DreamUI: Scaling Interactive Mobile Agent Environments with Generative UI World Model","forum_url":"https://openreview.net/forum?id=IMcQz8TBut","excerpt":"Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, et al. Ui-tars: Pioneering automated gui interaction with native agents. arXiv preprint arXiv:2501.12326, 2025. Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, and Timothy Lillicrap. Androidi","arxiv_match":true}],"id":"2501.12326","metadata_source":"Cached arXiv API metadata","rank":542},{"title":"Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of LLMs","arxiv_id":"2406.18495","year":2024,"url":"https://arxiv.org/abs/2406.18495","citing_submissions":191,"explicit_arxiv_matches":40,"examples":[{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":"Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, and Nouha Dziri. Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms. Advances in neural information processing systems, 37:8093–8131, 2024. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multit","arxiv_match":false},{"id":"y89ckFndSX","title":"HazardAuditor: From Executable Threats to Safer Computer-Use Agents","forum_url":"https://openreview.net/forum?id=y89ckFndSX","excerpt":"scovery to evidence-grounded verification. arXiv preprint arXiv:2607.01793, 2026c. Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, and Nouha Dziri. Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms. Advances in neural information processing systems, 37:8093–8131, 2024. Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, ","arxiv_match":false},{"id":"Uowq0ts0EJ","title":"AdaGuard: An Adaptive Guard Model with User-defined Policies","forum_url":"https://openreview.net/forum?id=Uowq0ts0EJ","excerpt":"uistics: Human Language Technologies (Volume 1: Long Papers), pp. 5992–6026, 2025. Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, and Nouha Dziri. Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms. Advances in neural information processing systems, 37:8093–8131, 2024. Monte Hoover, Vatsal Baherwani, Neel Jain, Khalid Saifullah, Joseph J Vincent, Chirag Jain, Melissa Rad, C Bruss, Ashwinee Pand","arxiv_match":false}],"id":"2406.18495","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":543},{"title":"Diffusion-LM improves controllable text generation","arxiv_id":"2205.14217","year":2022,"url":"https://arxiv.org/abs/2205.14217","citing_submissions":190,"explicit_arxiv_matches":30,"examples":[{"id":"oq0BXtA19v","title":"Test-Time Compositional Generalization in Diffusion Models via Prototype Discovery","forum_url":"https://openreview.net/forum?id=oq0BXtA19v","excerpt":"at learn and think like people. Behavioral and Brain Sciences, 40:e253, 2017. doi: 10.1017/S0140525X16001837. Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B. Hashimoto. DiffusionLM improves controllable text generation. In Advances in Neural Information Processing Systems, 2022. Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B. Tenenbaum. Compositional visual generation with composable diffusion models.","arxiv_match":false},{"id":"6SzEZJld4O","title":"Watermarking Continuous Diffusion Language Models","forum_url":"https://openreview.net/forum?id=6SzEZJld4O","excerpt":"Transactions on Machine Learning Research, 2024. ISSN 2835-8856. URL https://openreview.net/forum?id=FpaCL1MO2C. Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto. DiffusionLM improves controllable text generation. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (eds.), Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=3s9IrEsjLyk. Jiacheng Liang, ","arxiv_match":false},{"id":"6zSuqyB4a9","title":"What Makes a Good Decoding Path? Selecting Parallel-Decodable Trajectories for Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=6zSuqyB4a9","excerpt":" Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum? id=bS8qU2wJxu. Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto. Diffusionlm improves controllable text generation. Advances in neural information processing systems, 35: 4328–4343, 2022. Anji Liu, Oliver Broadrick, Mathias Niepert, and Guy Van den Broeck. Discrete copula diffusion. In Y. Yue, A. Garg, N. Peng, F","arxiv_match":false}],"id":"2205.14217","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":544},{"title":"WebGPT: Browser-assisted question-answering with human feedback","arxiv_id":"2112.09332","year":2021,"url":"https://arxiv.org/abs/2112.09332","citing_submissions":190,"explicit_arxiv_matches":169,"examples":[{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":", Jinyang Wu, Chengcheng Han, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao, Yueting Zhuang, and Yongliang Shen. Skill0: In-context agentic reinforcement learning for skill internalization. arXiv preprint arXiv:2604.02268, 2026. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, Mengyu Zhou, Pengyu Cheng, Dexin Wang, Xiaoxi","arxiv_match":true},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"nguage Modeling, 2025. URL https://arxiv.org/abs/2412.01928. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. WebGPT: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. URL https://arxiv.org/abs/2112.09332. Avanika Narayan, Dan Biderman, Sabri Eyuboglu, Avner May, Scott Linderman, James Zou, and Christop","arxiv_match":true},{"id":"vvAAVy99dI","title":"Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking","forum_url":"https://openreview.net/forum?id=vvAAVy99dI","excerpt":"ghoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. Mteb: Massive text embedding benchmark. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pp. 2014–2037, 2023. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. neural-bridge. Retrieval-Augmented Generation (RAG) Dataset 12000. https://huggingface. co/d","arxiv_match":true}],"id":"2112.09332","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":545},{"title":"Emergent abilities of large language models","arxiv_id":"2206.07682","year":2022,"url":"https://arxiv.org/abs/2206.07682","citing_submissions":189,"explicit_arxiv_matches":45,"examples":[{"id":"jBL8H3iA4E","title":"Short Horizons and Sparse Concepts: a Mathematical View of the Readout in the J-lens","forum_url":"https://openreview.net/forum?id=jBL8H3iA4E","excerpt":"ani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. Emergent abilities of large language models. Trans. Mach. Learn. Res., 2022, 2022. URL https://openreview.net/forum?id= yzkSU5zdwD. A DATASETS AND T RAINING C ONFIGURATION A.1 T RAINING C ORPUS The lens is fit on natural text from the WikiText","arxiv_match":false},{"id":"7Et5MDmRRV","title":"Beyond Accuracy: Re-evaluating Failures in LLM Physics Reasoning","forum_url":"https://openreview.net/forum?id=7Et5MDmRRV","excerpt":"A: A graduate-level google-proof q&a benchmark. In First Conference on Language Modeling, 2024. URL https://openreview. net/forum?id=Ti67584b98. Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo. Are emergent abilities of large language models a mirage? In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL https://openreview.net/forum?id=ITw9edRDlD. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Sh","arxiv_match":false},{"id":"DqK845IBjL","title":"Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation","forum_url":"https://openreview.net/forum?id=DqK845IBjL","excerpt":"ization by scaling up umi data, September 2025. URL https://github.com/thu-ml/RDT2. Yang Tian, Yuyin Yang, Yiman Xie, Zetao Cai, Xu Shi, Ning Gao, Hangxu Liu, Xuekun Jiang, Zherui Qiu, Feng Yuan, et al. Interndata-a1: Pioneering high-fidelity synthetic data for pretraining generalist policy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 976–985, 2026. J Wei. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zh","arxiv_match":true}],"id":"2206.07682","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":546},{"title":"Temporal difference learning for model predictive control","arxiv_id":"2203.04955","year":2022,"url":"https://arxiv.org/abs/2203.04955","citing_submissions":189,"explicit_arxiv_matches":48,"examples":[{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"iverse control tasks through world models. Nature, 640(8059):647–653, 2025. doi: 10.1038/s41586-025-08744-2. URL https://doi.org/10.1038/s41586-025-08744-2. Nicklas Hansen, Hao Su, and Xiaolong Wang. Temporal difference learning for model predictive control. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 8387–8406. PMLR, 2022. URL https: //proceedings.mlr.press/v162/ha","arxiv_match":false},{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":"obust world models for continuous control. In International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Oxh5CstDJU. Nicklas A. Hansen, Hao Su, and Xiaolong Wang. Temporal difference learning for model predictive control. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 8387–8406. PMLR, 2022. URL https://proceedings.mlr.press/v162/han","arxiv_match":false},{"id":"rtlzkXdJe9","title":"PhysiCalWorld: In-Context Physics Calibration for World Models","forum_url":"https://openreview.net/forum?id=rtlzkXdJe9","excerpt":"obot manipulation. In International Conference on Learning Representations, volume 2026, pp. 6121–6138, 2026. 2 Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. Learning latent dynamics for planning from pixels. In International conference on machine learning, pp. 2555–2565. PMLR, 2019. 2 Nicklas Hansen, Xiaolong Wang, and Hao Su. Temporal difference learning for model predictive control. arXiv preprint arXiv:2203.04955, 2022. 2 Bohan Hou, Gen Li, Jindou Jia, Tuo An, Xinying Guo, Sicong Leng, Haoran Geng, Yanjie Ze, T","arxiv_match":true}],"id":"2203.04955","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":547},{"title":"Agentboard: An analytical evaluation board of multi-turn LLM agents","arxiv_id":"2401.13178","year":2024,"url":"https://arxiv.org/abs/2401.13178","citing_submissions":188,"explicit_arxiv_matches":45,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"n Learning Representations, 2024. URL https://openreview.net/forum?id=zAdUB0aCTQ. Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, and Junxian He. AgentBoard: An analytical evaluation board of multi-turn LLM agents. In Advances in Neural Information Processing Systems, 2024. URL https://papers.nips.cc/paper_files/paper/2024/ hash/877b40688e330a0e2a3fc24084208dfa-Abstract-Datasets_and_ Benchmarks_Track.html. Mas","arxiv_match":false},{"id":"EUiwYKQQVt","title":"When External Memory Becomes Agent State: Recorded Lineage and Controlled Reuse in LLM Agents","forum_url":"https://openreview.net/forum?id=EUiwYKQQVt","excerpt":"ional Linguistics: NAACL 2025, pp. 1160–1183. Association for Computational Linguistics, 2025. doi: 10.18653/v1/2025.findings-naacl.65. Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, and Junxian He. AgentBoard: An analytical evaluation board of multi-turn LLM agents. In Advances in Neural Information Processing Systems, volume 37, pp. 74325–74362, 2024. doi: 10.52202/079017-2365. URL https://arxiv.org/abs/2401.13178. James Newsome and Dawn Song. Dynamic taint analysis for automatic detection, analysis, and signatu","arxiv_match":true},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":"–33056. PMLR, 21–27 Jul 2024. URL https://proceedings.mlr. press/v235/lu24e.html. Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, and Junxian He. AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 74325–74362. Curran Associates, Inc., 2","arxiv_match":false}],"id":"2401.13178","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":548},{"title":"Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning","arxiv_id":"2508.19828","year":2025,"url":"https://arxiv.org/abs/2508.19828","citing_submissions":188,"explicit_arxiv_matches":76,"examples":[{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":".10813, 2024. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-mem: Agentic memory for llm agents. arXiv preprint arXiv:2502.12110, 2025. Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z Pan, Hinrich Schütze, Volker Tresp, and Yunpu Ma. Memoryr1: Enhancing large language model agents to manage and utilize memories via reinforcement learning. arXiv preprint arXiv:2508.19828, 2025. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao,","arxiv_match":true},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":"v.org/abs/ 2605.21768. Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schütze, Volker Tresp, and Yunpu Ma. MemoryR1: Enhancing large language model agents to manage and utilize memories via reinforcement learning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12805–12825. Association for Computational Linguistics, 2026b. URL https://acl","arxiv_match":false},{"id":"V1zG5WErz2","title":"Progressive Agent Skill Generation via Reinforcement Learning","forum_url":"https://openreview.net/forum?id=V1zG5WErz2","excerpt":"026. Jingkai Xu and Xiangli Nie. Speci: Skill prompts based hierarchical continual imitation learning for robot manipulation. IEEE Transactions on Cognitive and Developmental Systems, 18(2):488–503, 2026. Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z Pan, et al. Memory-r1: Enhancing large language model agents to manage and utilize memories via reinforcement learning. arXiv preprint arXiv:2508.19828, 2025. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao,","arxiv_match":true}],"id":"2508.19828","metadata_source":"Cached arXiv API metadata","rank":549},{"title":"Fine-tuning language models from human preferences","arxiv_id":"1909.08593","year":2019,"url":"https://arxiv.org/abs/1909.08593","citing_submissions":187,"explicit_arxiv_matches":180,"examples":[{"id":"hVl6H6DnYO","title":"Consistency as an Inductive Bias: Mitigating Cross-View Reliability Gap in MLLMs","forum_url":"https://openreview.net/forum?id=hVl6H6DnYO","excerpt":" Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, et al. Lmms-eval: Reality check on the evaluation of large multimodal models. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 881–916, 2025b. Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019. Technical Appendices and Supplements This appendix provides reproducibility details, extende","arxiv_match":true},{"id":"60pBvPzWfY","title":"Euterpe: Efficient Multi-Level Preservation of Pretrained Performance during Preference Alignment for Protein Inverse Folding","forum_url":"https://openreview.net/forum?id=60pBvPzWfY","excerpt":"u, Changxi Chi, Hongxin Xiang, Wenjie Du, Xiaoqi Wang, and Jun Xia. Refold: Refining protein inverse folding with efficient structural matching and fusion. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, pp. 13147–13156, 2026. Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019. A ","arxiv_match":true},{"id":"7NBlFCtd2e","title":"Radioactive Feedback: Tracing Models Trained by Proprietary LLM Judges","forum_url":"https://openreview.net/forum?id=7NBlFCtd2e","excerpt":"iang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. In Advances in Neural Information Processing Systems, 2023. Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019. Appendix A Proofs For a response y on prompt x with assigned carrier j(x), let hu (x, y) = e","arxiv_match":true}],"id":"1909.08593","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":550},{"title":"Learning to (learn at test time): RNNs with expressive hidden states","arxiv_id":"2407.04620","year":2024,"url":"https://arxiv.org/abs/2407.04620","citing_submissions":187,"explicit_arxiv_matches":93,"examples":[{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":"un, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, and Moritz Hardt. Test-time training for out-of-distribution generalization, 2020. URL https://openreview.net/ forum?id=HyezmlBKwr. Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, Tatsunori Hashimoto, and Carlos Guestrin. Learning to (learn at test time): Rnns with expressive hidden states, 2025. URL https://arxiv.org/ abs/2407.04620. Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully testti","arxiv_match":true},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"stems, 38:153738–153782, 2026. Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, Tatsunori Hashimoto, and Carlos Guestrin. Learning to (learn at test time): RNNs with expressive hidden states. In Proceedings of the 42nd International Conference on Machine Learning, pp. 57503–57522, 2025. Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Re","arxiv_match":false},{"id":"GZdwmSCjJs","title":"Juno: Taming Predictive Latents for Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=GZdwmSCjJs","excerpt":"6. Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt. Test-time training with self-supervision for generalization under distribution shifts. In International conference on machine learning, pp. 9229–9248. PMLR, 2020. Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, et al. Learning to (learn at test time): Rnns with expressive hidden states. arXiv preprint arXiv:2407.04620, 2024. Basile Terver, Tsung-Yen Yang, Jean Ponce, Adrien Bardes, and Yann LeCun. What drives succes","arxiv_match":true}],"id":"2407.04620","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":551},{"title":"Offline reinforcement learning: Tutorial, review, and perspectives on open problems","arxiv_id":"2005.01643","year":2020,"url":"https://arxiv.org/abs/2005.01643","citing_submissions":187,"explicit_arxiv_matches":165,"examples":[{"id":"vTUJnLXc91","title":"Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation","forum_url":"https://openreview.net/forum?id=vTUJnLXc91","excerpt":"Carolyne Pelletier, Eric Thibodeau-Laufer, Sándor Toth, and Sam Work. Tapered off-policy REINFORCE: Stable and efficient reinforcement learning for LLMs. In The Thirtyninth Annual Conference on Neural Information Processing Systems, 2025. URL https:// openreview.net/forum?id=gFFgCWiXWI. Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. Offline reinforcement learning: Tutorial, review, and perspectives on open problems, 2020. URL https://arxiv.org/abs/2005. 01643. Gengsheng Li, Mao Zheng, Mingyang Song, Jie Sun, Zeyuan Liu, Ruiqi Liu, Qiyong Zhong, Haiyun Guo, ","arxiv_match":true},{"id":"689WPplu19","title":"FAER: Auditable Utility-Aligned Trajectory Replay for Language Model Post-Training","forum_url":"https://openreview.net/forum?id=689WPplu19","excerpt":". Offline reinforcement learning with implicit qlearning. In International Conference on Learning Representations, 2022. Aviral Kumar, Rishabh Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. In Advances in Neural Information Processing Systems, 2020. Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. Offline reinforcement learning: Tutorial, review, and perspectives on open problems. In arXiv preprint arXiv:2005.01643, 2020. Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Lei","arxiv_match":true},{"id":"Wj4LaVIEUg","title":"Calibrate Before You Learn: Coverage-Aware Rewards for Offline RL","forum_url":"https://openreview.net/forum?id=Wj4LaVIEUg","excerpt":"ZJWF8. Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative Q-learning for offline reinforcement learning. In Advances in Neural Information Processing Systems, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/0d2b2061826a5df32 21116a5085a6052-Abstract.html. Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. Offline reinforcement learning: Tutorial, review, and perspectives on open problems, 2020. URL https://arxiv.org/abs/2005 .01643. Jianxiong Li, Xianyuan Zhan, Haoran Xu, Xiangyu Zhu, Jingjing Liu, and Ya-Qin Zhang. When data geo","arxiv_match":true}],"id":"2005.01643","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":552},{"title":"The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models","arxiv_id":"2505.22617","year":2025,"url":"https://arxiv.org/abs/2505.22617","citing_submissions":187,"explicit_arxiv_matches":174,"examples":[{"id":"9XqqxYouLj","title":"Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning","forum_url":"https://openreview.net/forum?id=9XqqxYouLj","excerpt":"u Cui, Yuchen Zhang, Jiacheng Chen, Lifan Yuan, Zhi Wang, Yuxin Zuo, Haozhan Li, Yuchen Fan, Huayu Chen, Weize Chen, Zhiyuan Liu, Hao Peng, Lei Bai, Wanli Ouyang, Yu Cheng, Bowen Zhou, and Ning Ding. The entropy mechanism of reinforcement learning for reasoning language models. CoRR, abs/2505.22617, 2025. 1 Jisheng Dang, Jingze Wu, Teng Wang, Xuanhui Lin, Nannan Zhu, Hongbo Chen, Wei-Shi Zheng, Meng Wang, and Tat-Seng Chua. Reinforcing video reasoning with focused thinking","arxiv_match":false},{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":"Lifan Yuan, Zefan Wang, Hanbin Wang, Wendi Li, Bingxiang He, Yuchen Fan, Tianyu Yu, Qixin Xu, Weize Chen, et al. Process reinforcement through implicit rewards. arXiv preprint arXiv:2502.01456v1, 2025a. URL https://arxiv.org/abs/2502.01456v1. Ganqu Cui, Yuchen Zhang, Jiacheng Chen, Lifan Yuan, Zhi Wang, Yuxin Zuo, Haozhan Li, Yuchen Fan, Huayu Chen, Weize Chen, et al. The entropy mechanism of reinforcement learning for reasoning language models. arXiv preprint arXiv:2505.22617, 2025b. Boyang Deng, Songyou Peng, Kyle Genova, Gordon Wetzstein, Noah Snavely, Leonidas Guibas, an","arxiv_match":true},{"id":"BdXmcg59t4","title":"WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution","forum_url":"https://openreview.net/forum?id=BdXmcg59t4","excerpt":"n, Jeff Z. Pan, and Yunhong Wang. Terminal-World: Scaling terminal-agent environments via agent skills, 2026b. URL https://arxiv.org/abs/2605.20876. Ganqu Cui, Yuchen Zhang, Jiacheng Chen, Lifan Yuan, Zhi Wang, Yuxin Zuo, Haozhan Li, Yuchen Fan, Huayu Chen, Weize Chen, Zhiyuan Liu, Hao Peng, Lei Bai, Wanli Ouyang, Yu Cheng, Bowen Zhou, and Ning Ding. The entropy mechanism of reinforcement learning for reasoning language models, 2025. URL https://arxiv.org/abs/2505.22617. Databricks AI Research. OfficeQA Pro: Official agent harness performance results. Official benchma","arxiv_match":true}],"id":"2505.22617","metadata_source":"Cached arXiv API metadata","rank":553},{"title":"Memory OS of AI Agent","arxiv_id":"2506.06326","year":2025,"url":"https://arxiv.org/abs/2506.06326","citing_submissions":186,"explicit_arxiv_matches":34,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"d: Graph memory for LLM agents. In Forty-third International Conference on Machine Learning, 2026. URL https: //openreview.net/forum?id=xRVWftS3ES. Jiazheng Kang, Mingming Ji, Zhe Zhao, and Ting Bai. Memory OS of AI agent. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 25961–25970, 2025. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Jose","arxiv_match":false},{"id":"kZJGzT3BR2","title":"EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory","forum_url":"https://openreview.net/forum?id=kZJGzT3BR2","excerpt":"an et al. Ego4d: Around the world in 3,000 hours of egocentric video. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18995–19012, 2022. Jiazheng Kang et al. Memory os of ai agent. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 25972–25981, 2025. Yoonsang Kim et al. Speechless: Micro-utterance with personalized spatial memory-awa","arxiv_match":false},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":" and Seong Joon Oh. MEME: Multi-entity and evolving memory evaluation. arXiv preprint arXiv:2605.12477, 2026. URL https://arxiv.org/abs/2605.12477. Jiazheng Kang, Mingming Ji, Zhe Zhao, and Ting Bai. Memory OS of AI agent. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2025. URL https://aclanthology.org/2025. emnlp-main.1318/. Takes","arxiv_match":false}],"id":"2506.06326","metadata_source":"Cached arXiv API metadata","rank":554},{"title":"Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models","arxiv_id":"2501.01423","year":2025,"url":"https://arxiv.org/abs/2501.01423","citing_submissions":186,"explicit_arxiv_matches":25,"examples":[{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"k, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, and Difan Liu. VideoGigaGAN: Towards detail-rich video super-resolution. In CVPR, 2025. Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models. In CVPR, 2025. Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and William T. Freeman. Improved distribution matching distillation for fast image synthesis. I","arxiv_match":false},{"id":"o6IsUtKlgQ","title":"Learning Spatial-Pyramid Visual Representations for Autoregressive Image Generation","forum_url":"https://openreview.net/forum?id=o6IsUtKlgQ","excerpt":"in, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021. Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. Weiyi You, Jinhua Zhang, Xingyu Zhou, Wei Long, Junyu Lou, and Shuhang Gu. Advanced pixel diffusion model ","arxiv_match":false},{"id":"ncOxUOWsiS","title":"Frequency-Steered VAEs: Restoring Fine Details in Low-Resolution Latents","forum_url":"https://openreview.net/forum?id=ncOxUOWsiS","excerpt":"Xu, Xiaotao Gu, Yuxiao Dong, and Jie Tang. Cogvideox: Text-to-video diffusion models with an expert transformer, 2025. URL https://arxiv.org/abs/2408.06072. Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15703–15712, June 2025. Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang","arxiv_match":false}],"id":"2501.01423","metadata_source":"Cached arXiv API metadata","rank":555},{"title":"The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search","arxiv_id":"2504.08066","year":2025,"url":"https://arxiv.org/abs/2504.08066","citing_submissions":186,"explicit_arxiv_matches":169,"examples":[{"id":"VHnY0nzXxv","title":"Co-Evolving Skills, Memory, and Policy for Personalized Research Automation","forum_url":"https://openreview.net/forum?id=VHnY0nzXxv","excerpt":"nic health records. npj digital medicine, 6(1):135, 2023. Qiujie Xie, Qingqiu Li, Zhuohao Yu, Yuejie Zhang, Yue Zhang, and Linyi Yang. An empirical analysis of uncertainty in large language model evaluations. arXiv preprint arXiv:2502.10709, 2025. Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search. arXiv preprint arXiv:2504.08066, 2025. Minjun Zhu, Qiujie Xie, Yixuan Weng, Jian Wu, Zhen Lin, Linyi Yang, and Yue Zhang. Ai scient","arxiv_match":true},{"id":"ZIVFDKAh8p","title":"Automated Algorithm Discovery through Mathematical Reformulation: A Study in Visual-Token Reduction","forum_url":"https://openreview.net/forum?id=ZIVFDKAh8p","excerpt":"xiv.org/abs/ 2505.18705. xAI. Grok-1.5 Vision Preview. https://x.ai/blog/grok-1.5v, 2024. Introduces the RealWorldQA benchmark for real-world spatial understanding. Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha. The AI scientist-v2: Workshop-level automated scientific discovery via agentic tree search. arXiv preprint arXiv:2504.08066, 2025. doi: 10.48550/arXiv.2504.08066. URL https://arxiv.org/abs/2504.08066. Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. VisionZ","arxiv_match":true},{"id":"JN4DdAjmHA","title":"Ideas Have Genomes: A Comprehensive Benchmark for Scientific Lineage Reasoning and Lineage-Grounded Idea Generation","forum_url":"https://openreview.net/forum?id=JN4DdAjmHA","excerpt":"d workflows. arXiv preprint arXiv:2512.16969, 2025. doi: 10.48550/arXiv.2512.16969. Wanghan Xu et al. ResearchClawBench: A benchmark for end-to-end autonomous scientific research. arXiv preprint arXiv:2606.07591, 2026. doi: 10.48550/arXiv.2606.07591. Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha. The AI scientist-v2: Workshop-level automated scientific discovery via agentic tree search. arXiv preprint arXiv:2504.08066, 2025. Shunyu Yao et al. τ -bench: A benchmark for tool-agent-user interaction in real-world domain","arxiv_match":true}],"id":"2504.08066","metadata_source":"Cached arXiv API metadata","rank":556},{"title":"Transolver: A fast transformer solver for pdes on general geometries","arxiv_id":"2402.02366","year":2024,"url":"https://arxiv.org/abs/2402.02366","citing_submissions":186,"explicit_arxiv_matches":56,"examples":[{"id":"XXNQ4STUz3","title":"CEGINO: CONTINUOUS EQUIVARIANT GEOMETRY- INFORMED NEURAL OPERATORS FOR 3D PDES","forum_url":"https://openreview.net/forum?id=XXNQ4STUz3","excerpt":"d Bickel. Learning three-dimensional flow for interactive aerodynamic design. ACM Transactions on Graphics (TOG), 37(4):1–10, 2018. Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, and Mingsheng Long. Transolver: A fast transformer solver for PDEs on general geometries. In International Conference on Machine Learning, volume 235, pp. 53681–53705. PMLR, 2024. URL https://proceedings.mlr. press/v235/wu24r.html. Louis-Pascal Xhonneux, Meng Qu, and Jian Tang. Continuou","arxiv_match":false},{"id":"NIiInsYbTE","title":"FROST: LEARNING FREE-BOUNDARY EQUILIBRIA FOR SIMULATION AND INVERSE DESIGN","forum_url":"https://openreview.net/forum?id=NIiInsYbTE","excerpt":"ik, 278:269–286, 1891. S. Wang and P. Perdikaris. Deep learning of free boundary and Stefan problems. Journal of Computational Physics, 428:109914, 2021. H. Wu, H. Luo, H. Wang, J. Wang, and M. Long. Transolver: a fast transformer solver for PDEs on general geometries. In Proceedings of the International Conference on Machine Learning, pp. 53681–53705, 2024. M. Yin, N. Charon, R. Brody, L. Lu, N. Trayanova, and M. Maggioni. A scalable framework for learning the ge","arxiv_match":false},{"id":"XBWz0wdQR7","title":"FluidWeave: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives","forum_url":"https://openreview.net/forum?id=XBWz0wdQR7","excerpt":". American Mathematical Society, 2024. Holger Wendland. Scattered data approximation, volume 17. Cambridge university press, 2004. Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, and Mingsheng Long. Transolver: A fast transformer solver for pdes on general geometries. In International Conference on Machine Learning, pp. 53681–53705. PMLR, 2024. Jingrui Xing, Bin Wang, Mengyu Chu, and Baoquan Chen. Gaussian Fluids: A grid-free fluid solver based on Gaussian Spatia","arxiv_match":false}],"id":"2402.02366","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":557},{"title":"Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills","arxiv_id":"2603.25158","year":2026,"url":"https://arxiv.org/abs/2603.25158","citing_submissions":185,"explicit_arxiv_matches":175,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"026. Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, and Hao Su. Maniskill: Generalizable manipulation skill benchmark with large-scale demonstrations. arXiv preprint arXiv:2107.14483, 2021. Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, Mengyu Zhou, Pengyu Cheng, Dexin Wang, Erchao Zhao, Xiaoxi Jiang, and Guanjun Jiang. Trace2skill: Distill trajectory-local lessons into transferable agent skills. arXiv preprint arXiv:2603.25158, 2026. OpenAI. Introducing GPT-5, introducing-gpt-5/. 2025a. URL https://openai.com/index/ OpenAI. ","arxiv_match":true},{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"akano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, Mengyu Zhou, Pengyu Cheng, Dexin Wang, Xiaoxi Jiang, and Guanjun Jiang. Trace2skill: Distill trajectory-local lessons into transferable agent skills. arXiv preprint arXiv:2603.25158, 2026. Kangyun Ning, Yisong Su, Xueqiang Lv, Yuanzhe Zhang, Jian Liu, Kang Liu, and Jinan Xu. Wtuev","arxiv_match":true},{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":"ang, Jing Zhang, Jifan Yu, Xiaokang Zhang, Xiaohan Zhang, Sijia Luo, Xi Wang, and Jie Tang. SpreadsheetBench: Towards challenging real world spreadsheet manipulation. Advances in Neural Information Processing Systems, 37:94871–94908, 2024. Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, Mengyu Zhou, Pengyu Cheng, Dexin Wang, Erchao Zhao, Xiaoxi Jiang, and Guanjun Jiang. Trace2Skill: Distill trajectory-local lessons into transferable agent skills. arXiv preprint arXiv:2603.25158, 2026. Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaha","arxiv_match":true}],"id":"2603.25158","metadata_source":"Cached arXiv API metadata","rank":558},{"title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","arxiv_id":"2403.05530","year":2024,"url":"https://arxiv.org/abs/2403.05530","citing_submissions":184,"explicit_arxiv_matches":178,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":" Benyou Wang, and Xiangyu Yue. Video-r1: Reinforcing video reasoning in mllms. In NeurIPS, 2025. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. In CVPR, 2025. Gemini Team Google. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv:2403.05530, 2024. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostro","arxiv_match":true},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"rewards. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 27868–27887, 2026. Larry R. Squire. Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82(3):171–177, November 2004. doi: 10.1016/j.nlm.2004.06.005. Gemini Team. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024. URL https://arxiv.org/abs/2403.05530. Yu Wang and Xi Chen. Mirix: Multi-agent memory system for llm-based agents. arXiv preprint arXiv:2","arxiv_match":true},{"id":"M82NayqkbE","title":"Dataset Inference via Cumulative Evidence","forum_url":"https://openreview.net/forum?id=M82NayqkbE","excerpt":"n Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530, 2024. The New York Times v. Microsoft and OpenAI. Complaint, No. 1:23-cv-11195 (S.D.N.Y. filed Dec","arxiv_match":true}],"id":"2403.05530","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":559},{"title":"Wide Residual Networks","arxiv_id":"1606.01540","year":2016,"url":"https://arxiv.org/abs/1606.01540","citing_submissions":184,"explicit_arxiv_matches":89,"examples":[{"id":"4AEJkVBst5","title":"What Should Consistency Preserve? Readout-Geometric Information Transport for Semi-Supervised Regression","forum_url":"https://openreview.net/forum?id=4AEJkVBst5","excerpt":"Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multi-task learning. Advances in neural information processing systems, 33: 5824–5836, 2020. Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016. Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki. Flexmatch: Boosting semi-supervised learning with curriculu","arxiv_match":false},{"id":"ZLwbkenbR8","title":"Belief-Based Risk Reduction for Robust Reinforcement Learning under Adversarial Observation Perturbations","forum_url":"https://openreview.net/forum?id=ZLwbkenbR8","excerpt":"k, 10(11):68–71, 1997. Vahid Behzadan and Arslan Munir. Whatever does not kill deep reinforcement learning, makes it stronger. arXiv preprint arXiv:1712.09344, 2017. Roman Belaire, Arunesh Sinha, and Pradeep Varakantham. On minimizing adversarial counterfactual error in adversarial rl. arXiv preprint arXiv:2406.04724, 2024. Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016. Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay","arxiv_match":true},{"id":"u3efKiCvkc","title":"Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent","forum_url":"https://openreview.net/forum?id=u3efKiCvkc","excerpt":"tropy reinforcement learning. In International conference on machine learning (ICML), 2025. Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research, 2024. Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. OpenAI Gym. arXiv preprint arXiv:1606.01540, 2016. Tianyi Chen, Haitong Ma, Na Li, Kai Wang, and Bo Dai. One-step flow policy mirror descent. a","arxiv_match":true}],"id":"1606.01540","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":560},{"title":"AutoDAN: Generating stealthy jailbreak prompts on aligned large language models","arxiv_id":"2310.04451","year":2023,"url":"https://arxiv.org/abs/2310.04451","citing_submissions":183,"explicit_arxiv_matches":41,"examples":[{"id":"mVJzfFoRun","title":"Checking Leakage Witnesses versus Certifying Bounded Non-Leakage","forum_url":"https://openreview.net/forum?id=mVJzfFoRun","excerpt":"sh/ 50a2e625745ab078389ccd23747fc0d8-Abstract-Conference.html. Michael L. Littman, Judy Goldsmith, and Martin Mundhenk. The computational complexity of probabilistic planning. Journal of Artificial Intelligence Research, 9:1–36, 1998. Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. AutoDAN: Generating stealthy jailbreak prompts on aligned large language models. In International Conference on Learning Representations (ICLR), 2024. URL https://arxiv.org/abs/2310.04451. Samuele Marro and Michele Lombardi. Computational asymmetries in robust classification. In Proceed","arxiv_match":true},{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":"gchao Yao, Tongliang Liu, and Bo Han. Deepinception: Hypnotize large language model to be jailbreaker, 2024. URL https://arxiv.org/abs/ 2311.03191. Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. Autodan: Generating stealthy jailbreak prompts on aligned large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=7Jwpw4qKkb. Xiaogeng Liu, Peiran Li, G. Edward Suh, Yevgeniy Vorobeychik, Zhuoqing Mao,","arxiv_match":false},{"id":"IKUjfAknOl","title":"Agent Hacks Agents: Autoresearch Discovers Vulnerabilities in Production Agents","forum_url":"https://openreview.net/forum?id=IKUjfAknOl","excerpt":"oG: A diagnostic guardrail framework for AI agent safety and security. arXiv preprint arXiv:2601.18491, 2026. URL https://arxiv.org/abs/2601.18491. Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. AutoDAN: Generating stealthy jailbreak prompts on aligned large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=7Jwpw4qKkb. Xiaogeng Liu, Peiran Li, G Edward Suh, Yevgeniy Vorobeychik, Zhuoqing Mao, ","arxiv_match":false}],"id":"2310.04451","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":561},{"title":"Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory","arxiv_id":"2504.07952","year":2025,"url":"https://arxiv.org/abs/2504.07952","citing_submissions":183,"explicit_arxiv_matches":45,"examples":[{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":"nguage agents with verbal reinforcement learning. Advances in neural information processing systems, 36:8634–8652, 2023. Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. Dynamic cheatsheet: Test-time learning with adaptive memory. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7080–7106, 2026. Harsh Trivedi, Tushar Khot, Mareike Hartm","arxiv_match":false},{"id":"w88HAucqab","title":"Evolving Procedural Memory from User Traffic for Agentic Graphic Design","forum_url":"https://openreview.net/forum?id=w88HAucqab","excerpt":" https://proceedings.neurips.cc/paper_files/paper/ 2023/hash/1b44b878bb782e6954cd888628510e90-Abstract-Conference. html. Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. Dynamic cheatsheet: Test-time learning with adaptive memory. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7080–7106, Rabat, Morocco, March 2026. Association for Com","arxiv_match":false},{"id":"dHl5b1GjT0","title":"ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience","forum_url":"https://openreview.net/forum?id=dHl5b1GjT0","excerpt":"ml. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7080–7106, Rabat, Morocco, March 2026. Association for Com","arxiv_match":false}],"id":"2504.07952","metadata_source":"Cached arXiv API metadata","rank":562},{"title":"Transformers can do bayesian inference","arxiv_id":"2112.10510","year":2021,"url":"https://arxiv.org/abs/2112.10510","citing_submissions":183,"explicit_arxiv_matches":45,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":"ers Reveal Causal Feature Hierarchies in Time Series Foundation Models. arXiv preprint arXiv:2603.10071, 2026. Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. Transformers Can Do Bayesian Inference. In International Conference on Learning Representations (ICLR), 2022. Khalid Oublal, Quentin Bouniot, Qi Gan, Stephan Clémençon, and Zeynep Akata. TimeSAE: Causal Sparse Decoding for Faithful Explan","arxiv_match":false},{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":"ne Learning. 3 edition, 2025. ISBN 978-3-911578-03-5. URL https://christophm.github.io/interpretable-ml-book. Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. Transformers Can Do Bayesian Inference, 2022. Samuel Müller, Matthias Feurer, Noah Hollmann, and Frank Hutter. Pfns4bo: In-context learning for bayesian optimization. In International Conference on Machine Learning, pp. 25444–25470. PMLR","arxiv_match":false},{"id":"mZ3FfjH6Gm","title":"Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=mZ3FfjH6Gm","excerpt":"nces in Neural Information Processing Systems, volume 37, pp. 124069–124092, 2024. doi: 10.52202/079017-3941. Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. Transformers can do Bayesian inference. In International Conference on Learning Representations, 2022. Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, and Andreas Terzis. Tight a","arxiv_match":false}],"id":"2112.10510","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":563},{"title":"Llava-prumerge: Adaptive token reduction for efficient large multimodal models","arxiv_id":"2403.15388","year":2024,"url":"https://arxiv.org/abs/2403.15388","citing_submissions":182,"explicit_arxiv_matches":32,"examples":[{"id":"8bxrbbOHsv","title":"CaRAMeL: Compressed and Retrieval-Augmented Spatio-Temporal Memory for Long-Horizon Robot Navigation","forum_url":"https://openreview.net/forum?id=8bxrbbOHsv","excerpt":"lu Kayi, and Arun Reddy. Controllable hybrid captioner for improved long-form video understanding. arXiv preprint arXiv:2507.17047, 2025. Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 22857–22867. IEEE, 2025. Tin Stribor Sohn, Maximilian Dillitzer, Jason J Corso, and Eric Sax. R4: Retrieval-augmented reasoni","arxiv_match":false},{"id":"iBCBaGASs6","title":"HEAT: Hierarchical Elastic Adaptive Token Sparsification via Depth Scaling","forum_url":"https://openreview.net/forum?id=iBCBaGASs6","excerpt":"orica Patraucean, Lucas Smaira, Ankush Gupta, Adria Recasens, Larisa Markeeva, Dylan Banarse, Skanda Koppula, Mateusz Malinowski, Yi Yang, Carl Doersch, et al. Perception test: A diagnostic benchmark for multimodal video models. In Conference on Neural Information Processing Systems, pp. 42748–42761, 2023. Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. LLaVA-PruMerge: Adaptive token reduction for efficient large multimodal models. arXiv preprint arXiv:2403.15388, 2024. Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh","arxiv_match":true},{"id":"LcFUJbno2e","title":"MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs","forum_url":"https://openreview.net/forum?id=LcFUJbno2e","excerpt":"n for neural network pruning. In 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 11256–11264. IEEE, 2019. Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 22857–22867. IEEE, 2025. Jiaqi Shi, Xulong Zhang, Yuechan Li, Xiaoyang Qu, and Jianzong Wang. From inheritance to saturation:","arxiv_match":false}],"id":"2403.15388","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":564},{"title":"VinePPO: Refining credit assignment in RL training of LLMs","arxiv_id":"2410.01679","year":2024,"url":"https://arxiv.org/abs/2410.01679","citing_submissions":182,"explicit_arxiv_matches":78,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":" conference on empirical methods in natural language processing (EMNLP), pp. 6769–6781, 2020. Simran Kaur, Narutatsu Ri, Yinghui He, Liam Fowl, and Sanjeev Arora. Rethinking on-policy self-distillation for thinking models. arXiv preprint arXiv:2607.05184, 2026. Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, and Nicolas Le Roux. Vineppo: Refining credit assignment in rl training of llms. arXiv preprint arXiv:2410.01679, 2025. Taisuke Kobayashi. Proximal policy optimization with relative pearson divergence. In 2021 IE","arxiv_match":true},{"id":"MREjTH2FlB","title":"Seeing What Changed: Transition-Guided Credit Assignment for GUI Agents","forum_url":"https://openreview.net/forum?id=MREjTH2FlB","excerpt":"ulti-turn computer-use agents. arXiv preprint arXiv:2607.09773, 2026. Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron C Courville, and Nicolas Le Roux. VinePPO: Refining credit assignment in RL training of LLMs. In International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, 2025. Hanyu Lai, Xiao Liu, Yanxiao Zhao, Han Xu, Hanchen Zhang, Bohao Jing, Yanyu Ren, Shunti","arxiv_match":false},{"id":"1TfY9hAQoJ","title":"Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening","forum_url":"https://openreview.net/forum?id=1TfY9hAQoJ","excerpt":"v:2403.07974, 2024. URL https://arxiv.org/abs/2403.07974. Zhewei Kang, Aosong Feng, Sergey Levine, Dawn Song, and Xuandong Zhao. VIMPO: Valueimplicit policy optimization for LLMs. arXiv preprint arXiv:2606.20008, 2026. URL https: //arxiv.org/abs/2606.20008. Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al. VinePPO: Refining credit assignment in RL training of LLMs. In International Conference on Machine Learning, 2025. URL https://arxiv.org/abs/2410.01679. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, A","arxiv_match":true}],"id":"2410.01679","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":565},{"title":"Chameleon: Mixed-modal early-fusion foundation models","arxiv_id":"2405.09818","year":2024,"url":"https://arxiv.org/abs/2405.09818","citing_submissions":181,"explicit_arxiv_matches":169,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"on: Six lessons from babies. Artificial life, 11(1-2):13–29, 2005. Danny D Steinberg and Miho T Steinberg. Reading before speaking. Visible Language, 9(3), 1975. Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Zhengxiong Luo, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Generative multimodal models are in-context learners. In CVPR, 2024. Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. Cosmos 3 Team. Cosmos 3: Omnimodal world models for physical ai, 2026a. URL https: //arxiv.o","arxiv_match":true},{"id":"KHODv83hPB","title":"IntReal: A Real-World Dataset and Benchmark for Multimodal Interleaved Generation","forum_url":"https://openreview.net/forum?id=KHODv83hPB","excerpt":"Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, et al. Dinov3. arXiv preprint arXiv:2508.10104, 2025. Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Emu: Generative pretraining in multimodality. In International conference on learning representations, volume 2024, pp. 12352–12380, 2024. Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, I","arxiv_match":true},{"id":"IdwA2KX2G3","title":"StateWise: From Noise Awareness to State-Calibrated MLLM Conditioning for Unified Generation","forum_url":"https://openreview.net/forum?id=IdwA2KX2G3","excerpt":"ery-kontext: An unified multimodal model for image generation and editing. arXiv preprint arXiv:2509.26641, 2025. Hao Tang, Chen-Wei Xie, Xiaoyi Bao, Tingyu Weng, Pandeng Li, Yun Zheng, and Liwei Wang. Unilip: Adapting clip for unified multimodal understanding, generation and editing. In International Conference on Learning Representations, volume 2026, pp. 60440–60465, 2026. Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024. Gemini Team. Gemini 2.5 flash & gemini 2.5 flash image model card, 2025. https://storage.goo","arxiv_match":true}],"id":"2405.09818","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":566},{"title":"Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention","arxiv_id":"2502.11089","year":2025,"url":"https://arxiv.org/abs/2502.11089","citing_submissions":181,"explicit_arxiv_matches":64,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, et al. Native Sparse Attention: Hardware-aligned and natively trainable sparse attention. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 23078–23097, 2025. Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua","arxiv_match":false},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"ng mamba2 with delta rule. arXiv preprint arXiv:2412.06464, 2024c. Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, et al. Native sparse attention: Hardware-aligned and natively trainable sparse attention. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 23078–23097, 2025. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. Star:","arxiv_match":false},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"dware-Efficient Training. In ICML, 2024. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, et al. CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer. In ICLR, 2025. Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, and Chao Dong. Teaching Large Language Models to Regress Accurate Image Quality Scores Using Score Distribution. In CVPR, 2025. Jingyang Yuan et al. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention. arXiv preprint arXiv:2502.11089, 2025. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid Loss for Languag","arxiv_match":true}],"id":"2502.11089","metadata_source":"Cached arXiv API metadata","rank":567},{"title":"One step diffusion via shortcut models","arxiv_id":"2410.12557","year":2024,"url":"https://arxiv.org/abs/2410.12557","citing_submissions":181,"explicit_arxiv_matches":36,"examples":[{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"d comprehensive benchmark. arXiv preprint arXiv:2509.09680, 2025. Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, and Alain Rakotomamonjy. Unifying gans and score-based diffusion as generative particle models. Advances in Neural Information Processing Systems, 36: 59729–59760, 2023. Kevin Frans, Danijar Hafner, Sergey Levine, and Pieter Abbeel. One step diffusion via shortcut models. arXiv preprint arXiv:2410.12557, 2024. Yu Gao, Lixue Gong, Qiushan Guo, Xiaoxia Hou, Zhichao Lai, Fanshi Li, Liang Li, Xiaochen Lia","arxiv_match":true},{"id":"p6thn2PhEo","title":"Don't Throw Away the Solution: Training-Free Acceleration of Flow-Based Robot Policies","forum_url":"https://openreview.net/forum?id=p6thn2PhEo","excerpt":"e for diffusion policy. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11758–11764. IEEE, 2025. Kevin Frans, Danijar Hafner, Sergey Levine, and Pieter Abbeel. One step diffusion via shortcut models. In International Conference on Learning Representations, 2025. Zhengyang Geng, Mingyang Deng, Xingjian Bai, J. Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling. In Advances i","arxiv_match":false},{"id":"gqKba6Tu8o","title":"EagleDepth: Efficient Fine-Grained Monocular Depth Estimation via Pixel Diffusion Decoder","forum_url":"https://openreview.net/forum?id=gqKba6Tu8o","excerpt":" Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems, 27, 2014. Kevin Frans, Danijar Hafner, Sergey Levine, and Pieter Abbeel. One step diffusion via shortcut models. In International Conference on Learning Representations, volume 2025, pp. 34668– 34684, 2025. Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. ","arxiv_match":false}],"id":"2410.12557","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":568},{"title":"Absolute Zero: Reinforced Self-play Reasoning with Zero Data","arxiv_id":"2505.03335","year":2025,"url":"https://arxiv.org/abs/2505.03335","citing_submissions":180,"explicit_arxiv_matches":69,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"els: What, how, where, and how well? arXiv preprint arXiv:2503.24235, 2025b. Andrew Zhao, Yiran Wu, Tong Wu, Quentin Xu, Yang Yue, Matthieu Lin, Shenzhi Wang, Qingyun Wu, Zilong Zheng, and Gao Huang. Absolute zero: Reinforced self-play reasoning with zero data. Advances in Neural Information Processing Systems, 38:105816–105879, 2026. Ingo Ziegler, Abdullatif Köksal, Desmond Elliott, and Hinrich Schütze. Craft your dataset: Taskspecific synthetic dataset g","arxiv_match":false},{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"onference on Learning Representations, volume 2026, pp. 104223–104294, 2026. Andrew Zhao, Yiran Wu, Tong Wu, Quentin Xu, Yang Yue, Matthieu Lin, Shenzhi Wang, Qingyun Wu, Zilong Zheng, and Gao Huang. Absolute zero: Reinforced self-play reasoning with zero data. Advances in Neural Information Processing Systems, 38:105816–105879, 2026. Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fr","arxiv_match":false},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"am Math-AI. American invitational mathematics examination (aime) 2026, 2026. Andrew Zhao, Yiran Wu, Tong Wu, Quentin Xu, Yang Yue, Matthieu Lin, Shenzhi Wang, Qingyun Wu, Zilong Zheng, and Gao Huang. Absolute zero: Reinforced self-play reasoning with zero data. Advances in Neural Information Processing Systems, 38:105816–105879, 2026. Wenhao Zheng, Yixiao Chen, Weitong Zhang, Souvik Kundu, Yun Li, Zhengzhong Liu, Eric P Xing, Hongyi Wang, and Huaxiu Yao. C","arxiv_match":false}],"id":"2505.03335","metadata_source":"Cached arXiv API metadata","rank":569},{"title":"From RAG to Memory: Non-Parametric Continual Learning for Large Language Models","arxiv_id":"2502.14802","year":2025,"url":"https://arxiv.org/abs/2502.14802","citing_submissions":180,"explicit_arxiv_matches":56,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"n The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=dyJ0GWpjJB. Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su. From RAG to memory: Non-parametric continual learning for large language models. In Proceedings of the 42nd International Conference on Machine Learning, volume 267, pp. 21497–21515, 2025. Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Ch","arxiv_match":false},{"id":"PEoM2nDAcy","title":"EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation","forum_url":"https://openreview.net/forum?id=PEoM2nDAcy","excerpt":"ernal medicine. Archives of internal medicine, 165(13):1493–1499, 2005. Bernal J Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspired long-term memory for large language models. Advances in neural information processing systems, 37:59532–59569, 2024. Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su. From rag to memory: Non-parametric continual learning for large language models. arXiv preprint arXiv:2502.14802, 2025. Stefan Hegselmann, Georg von Arnim, Tillmann Rheude, Noel Kronenberg, David Sontag, Gerhard ","arxiv_match":true},{"id":"eipQbEsWLA","title":"Adaptive Consistency Graph for Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=eipQbEsWLA","excerpt":". URL https://proceedings.neurips.cc/paper_files/paper/2024/hash/ 6ddc001d07ca4f319af96a3024f6dbd1-Abstract-Conference.html. Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su. From RAG to memory: Non-parametric continual learning for large language models. In Proceedings of the 42nd International Conference on Machine Learning, volume 267, 2025. URL https: //proceedings.mlr.press/v267/gutierrez25a.html. Mengkang Hu, Tianxing Chen, Qiguang Chen, Yao Mu","arxiv_match":false}],"id":"2502.14802","metadata_source":"Cached arXiv API metadata","rank":570},{"title":"Learning meshbased simulation with graph networks","arxiv_id":"2010.03409","year":2020,"url":"https://arxiv.org/abs/2010.03409","citing_submissions":180,"explicit_arxiv_matches":45,"examples":[{"id":"XXNQ4STUz3","title":"CEGINO: CONTINUOUS EQUIVARIANT GEOMETRY- INFORMED NEURAL OPERATORS FOR 3D PDES","forum_url":"https://openreview.net/forum?id=XXNQ4STUz3","excerpt":"metric geometries under variable load conditions. Neural Networks, 198:108560, 2026. doi: 10.1016/j. neunet.2026.108560. Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W Battaglia. Learning meshbased simulation with graph networks. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=roNqYL0_XP. Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. PointNet: Deep learning on poi","arxiv_match":false},{"id":"82frKRmECA","title":"PACE: A Physics-Aware Mesh Coarsening Framework based on Error Equidistribution","forum_url":"https://openreview.net/forum?id=82frKRmECA","excerpt":"merics and Applications: CIME Summer School, Cetraro, Italy 2009, Editors: Giovanni Naldi, Giovanni Russo. Springer, 2011. Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter Battaglia. Learning mesh-based simulation with graph networks. In International Conference on Learning Representations, 2021. Rolandos Alexandros Potamias, Stylianos Ploumpis, and Stefanos Zafeiriou. Neural mesh simplification. In IEEE/CVF Conference on Compute","arxiv_match":false},{"id":"BKhScrAhtQ","title":"Phase-Aware Video Generation for Physics-Grounded Dynamics and Interactions","forum_url":"https://openreview.net/forum?id=BKhScrAhtQ","excerpt":"urban wind flows. In European Conference on Computer Vision, 2026. URL https://eccv.ecva. net/virtual/2026/poster/4800. Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W. Battaglia. Learning meshbased simulation with graph networks. In International Conference on Learning Representations, 2021. Jinsheng Quan, Qiaowei Miao, Yichao Xu, Zizhuo Lin, Ying Li, Wei Yang, Zhihui Li, and Yawei Luo. ParticleGS: Learning neural gaussian p","arxiv_match":false}],"id":"2010.03409","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":571},{"title":"Llamafactory: Unified efficient fine-tuning of 100+ language models","arxiv_id":"2403.13372","year":2024,"url":"https://arxiv.org/abs/2403.13372","citing_submissions":180,"explicit_arxiv_matches":55,"examples":[{"id":"BGtau6E80m","title":"Image Editing without Typing","forum_url":"https://openreview.net/forum?id=BGtau6E80m","excerpt":"nstruction-Driven Image Editing, 2026. URL https://arxiv.org/abs/2602.01851. Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Bangkok, Thailand, 2024. Association for Computational Linguistics. URL http://arxiv.org/abs/2403.13372. A ","arxiv_match":true},{"id":"O0MnDF6uMA","title":"Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents","forum_url":"https://openreview.net/forum?id=O0MnDF6uMA","excerpt":" Ge, Chenghua Huang, Chengxing Xie, et al. Glm-5: from vibe coding to agentic engineering. arXiv preprint arXiv:2602.15763, 2026. Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, and Zheyan Luo. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Proceedings of the 62nd annual meeting of the association for computational linguistics (ACL), pp. 400–410, 2024. Minghang Zhu, Chuyang Wei, Junhao Xu, Yilin Cheng, Zhumin Chen, and Jiyan He. Dee","arxiv_match":false},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"els in vision-language-action models. arXiv preprint arXiv:2601.03309, 2026. Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Bangkok, Thailand, 2024. Association for Computational Linguistics. URL http://arxiv.org/abs/2403.13372. Enshen Zhou, Jingkun An, Cheng Chi, Yi Han, Shanyu Rong, Chi Zhang, Pengwei Wang, Zhongyuan Wang, ","arxiv_match":true}],"id":"2403.13372","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":572},{"title":"LongBench v2: Towards deeper understanding and reasoning on realistic long-context multitasks","arxiv_id":"2412.15204","year":2024,"url":"https://arxiv.org/abs/2412.15204","citing_submissions":180,"explicit_arxiv_matches":41,"examples":[{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":" 2024. URL https: //aclanthology.org/2024.acl-long.172/. Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. LongBench v2: Towards deeper understanding and reasoning on realistic long-context multitasks. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"inguistics (Volume 1: Long Papers), pp. 3119–3137, 2024. Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. LongBench v2: Towards deeper understanding and reasoning on realistic long-context multitasks. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3639–3664, 2025. Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: Th","arxiv_match":false},{"id":"7wsro5BQsD","title":"Decentralized Multi-Agent Systems with Shared Context","forum_url":"https://openreview.net/forum?id=7wsro5BQsD","excerpt":"b. Accessed: 2026-09-25. Anthropic. Claude code. https://claude.ai/, 2026. Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, et al. Longbench v2: Towards deeper understanding and reasoning on realistic long-context multitasks. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3639–3664, 2025. Amanda Bertsch, Adithya Pratapa, Teruko Mitamura, Graham Neub","arxiv_match":false}],"id":"2412.15204","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":573},{"title":"Mastering atari with discrete world models","arxiv_id":"2010.02193","year":2020,"url":"https://arxiv.org/abs/2010.02193","citing_submissions":180,"explicit_arxiv_matches":72,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"itate policy evolution. Advances in neural information processing systems, 31, 2018a. David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018b. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019. Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193, 2020. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse domains","arxiv_match":true},{"id":"cKgE4AvBla","title":"Exploring What Matters: Value-Aware Exploration for Dreamer","forum_url":"https://openreview.net/forum?id=cKgE4AvBla","excerpt":" Andre Barreto, and Daniel Nikovski. Value-aware loss function for model-based reinforcement learning. In Artificial Intelligence and Statistics, pp. 1486–1494. PMLR, 2017. Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019. Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. Mastering Atari with discrete world models. arXiv preprint arXiv:2010.02193, 2020. Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse control","arxiv_match":true},{"id":"RN0cwgFJ8M","title":"In-Context World Modeling for Robot Control","forum_url":"https://openreview.net/forum?id=RN0cwgFJ8M","excerpt":": Learning behaviors by latent imagination. ArXiv, abs/1912.01603, 2019. URL https: //api.semanticscholar.org/CorpusID:208547755. Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, and Jimmy Ba. Mastering atari with discrete world models. ArXiv, abs/2010.02193, 2020. URL https://api. semanticscholar.org/CorpusID:222133157. Danijar Hafner, J. Pašukonis, Jimmy Ba, and Timothy P. Lillicrap. Mastering diverse domains through world model","arxiv_match":false}],"id":"2010.02193","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":574},{"title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","arxiv_id":"2502.05564","year":2025,"url":"https://arxiv.org/abs/2502.05564","citing_submissions":180,"explicit_arxiv_matches":39,"examples":[{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":"fefferle, Mustafa Tajjar, Gaël Varoquaux, and Frank Hutter. Beyond IID: How General Are Tabular Foundation Models, Really? 2026. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. In International Conference on Machine Learning, 2025. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICLv2: A better, faster, scalable, and open tabular foundation model. ","arxiv_match":false},{"id":"mZ3FfjH6Gm","title":"Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=mZ3FfjH6Gm","excerpt":" categorical features. In Advances in Neural Information Processing Systems, 2018. Lennart Purucker. Private communication, 2026. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICL: A tabular foundation model for in-context learning on large data. In International Conference on Machine Learning, 2025. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICLv2: A better, faster, scalable, and open tabular foundation model. ","arxiv_match":false},{"id":"wSHvuy1308","title":"Training Fair Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=wSHvuy1308","excerpt":", Lennart Purucker, and Frank Hutter. nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN. arXiv:2511.03634, 2025. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. In Proceedings of the 42nd International Conference on Machine Learning, volume 267, pp. 50817–50847, 2025. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICLv2: A better, fa","arxiv_match":false}],"id":"2502.05564","metadata_source":"Cached arXiv API metadata","rank":575},{"title":"Workarena: How capable are web agents at solving common knowledge work tasks?","arxiv_id":"2403.07718","year":2024,"url":"https://arxiv.org/abs/2403.07718","citing_submissions":180,"explicit_arxiv_matches":72,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":" Clopper and E. S. Pearson. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26(4):404–413, 1934. ISSN 00063444, 14643510. URL http: //www.jstor.org/stable/2331986. Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, et al. Workarena: How capable are web agents at solving common knowledge work tasks? arXiv preprint arXiv:2403.07718, 2024. Robin Dunn, Larry Wasserman, and Aaditya Ramdas. Distribution-free prediction sets for two-l","arxiv_match":true},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":"nce, 2026. URL https://arxiv.org/abs/2606.19348. Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vazquez, Nicolas Chapados, and Alexandre Lacoste. WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks? In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Proceedings of the 41st International Conference on Machine Learn","arxiv_match":false},{"id":"J6nLg2u3rl","title":"DynaPlanBench: Benchmarking Long-Horizon Agentic Planning in Dynamic Environments","forum_url":"https://openreview.net/forum?id=J6nLg2u3rl","excerpt":"raint-based planning. In EMNLP, 2025. Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, et al. Workarena: How capable are web agents at solving common knowledge work tasks? In ICML, 2024. Shiqing Fan, Xichen Ding, Liang Zhang, and Linjian Mo. Mcptoolbench++: A large scale ai agent model context protocol mcp tool use benchmark. arXiv preprint arXiv:2508.07575, 2025. Nic","arxiv_match":false}],"id":"2403.07718","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":576},{"title":"Advantage-weighted regression: Simple and scalable off-policy reinforcement learning","arxiv_id":"1910.00177","year":2019,"url":"https://arxiv.org/abs/1910.00177","citing_submissions":179,"explicit_arxiv_matches":160,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"ion Letters (RA-L), 7(4): 11205–11212, 2022. Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu. Robocasa: Large-scale simulation of everyday tasks for generalist robots. In Robotics: Science and Systems (RSS), 2024. Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine. Advantage-weighted regression: Simple and scalable off-policy reinforcement learning, 2019. URL https://arxiv.org/abs/1910.00177. Erick Rosete-Beas, Oier Mees, Gabriel Kalweit, Joschka Boedecker, and Wolfram Burgard. Latent plan","arxiv_match":true},{"id":"RIpEtbedmF","title":"LeWM++: Repairing the Planning Interface of Latent World Models","forum_url":"https://openreview.net/forum?id=RIpEtbedmF","excerpt":"nan Huang. Latent geometry beyond search: Amortizing planning in world models, 2026. Seohong Park, Kevin Frans, Benjamin Eysenbach, and Sergey Levine. OGBench: Benchmarking offline goal-conditioned RL. In International Conference on Learning Representations, 2025. Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine. Advantage-weighted regression: Simple and scalable off-policy reinforcement learning. CoRR, abs/1910.00177, 2019. URL http://arxiv.org/abs/1910.00177. Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, and Serg","arxiv_match":true},{"id":"EPREfh3h40","title":"TRACE: Trajectory Return Attribution and Contrastive Erasure for Multi-Turn Safety","forum_url":"https://openreview.net/forum?id=EPREfh3h40","excerpt":", Takashi Isobe, Baotian Hu, and Min Zhang. Uni-dpo: A unified paradigm for dynamic preference optimization of llms. In Proceedings of ICLR, 2026. Jan Peters and Stefan Schaal. Reinforcement learning by reward-weighted regression for operational space control. In Proceedings of ICML, pp. 745–750, 2007. Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine. Advantage-weighted regression: Simple and scalable off-policy reinforcement learning. arXiv preprint arXiv:1910.00177, 2019. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderso","arxiv_match":true}],"id":"1910.00177","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":577},{"title":"BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents","arxiv_id":"2504.12516","year":2025,"url":"https://arxiv.org/abs/2504.12516","citing_submissions":179,"explicit_arxiv_matches":164,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"ifang Sui. Math-shepherd: Verify and reinforce llms step-by-step without human annotations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9426–9439, 2024. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, and Amelia Glaese. Browsecomp: A simple yet challenging benchmark for browsing agents. arXiv preprint arXiv:2504.12516, 2025. Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, and Masashi Sugiyama. Rela","arxiv_match":true},{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":" Aymeric Roucher, Albert Villanova del Moral, Thomas Wolf, Leandro von Werra, and Erik Kaunismäki. ‘smolagents‘: a smol library to build great agentic systems. https://github. com/huggingface/smolagents, 2025. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, and Amelia Glaese. BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents, 2025. URL https://arxiv.org/abs/ 2504.12516. Tsung-Han Wu, Mihran Miroyan, David M. Chan, Trevor Darrell, Narges Norouzi, and Joseph E. Gonzale","arxiv_match":true},{"id":"ROD8EjYEgO","title":"One Answer to Grade Them All? Reference-Based Evaluation and Its Optimization Effects in Search Agents","forum_url":"https://openreview.net/forum?id=ROD8EjYEgO","excerpt":" and K. Müller (eds.), Advances in Neural Information Processing Systems, volume 12. MIT Press, 1999. URL https://proceedings.neurips.cc/paper_files/paper/ 1999/file/464d828b85b0bed98e80ade0a5c43b0f-Paper.pdf. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, and Amelia Glaese. Browsecomp: A simple yet challenging benchmark for browsing agents, 2025. URL https://arxiv.org/abs/2504. 12516. Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, ","arxiv_match":true}],"id":"2504.12516","metadata_source":"Cached arXiv API metadata","rank":578},{"title":"EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test","arxiv_id":"2503.01840","year":2025,"url":"https://arxiv.org/abs/2503.01840","citing_submissions":179,"explicit_arxiv_matches":58,"examples":[{"id":"e9RG7RBDSX","title":"Cadence: Regulating the Speculation Externality in LLM Serving","forum_url":"https://openreview.net/forum?id=e9RG7RBDSX","excerpt":"l Methods in Natural Language Processing, pp. 7421–7432. Association for Computational Linguistics, 2024b. doi: 10.18653/v1/2024.emnlp-main.422. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. EAGLE-3: Scaling up inference acceleration of large language models via training-time test. In Advances in Neural Information Processing Systems, volume 38, 2025. Zikun Li, Zhuofu Chen, Remi Delacourt, Gabriele Oliaro, Zeyu Wang, Qinghan Chen, Shuhuai Lin, April Yang, Zhihao Zhang, Zhuomin","arxiv_match":false},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"odels with dynamic draft trees. In Proceedings of the 2024 conference on empirical methods in natural language processing, pp. 7421–7432, 2024c. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. Eagle-3: Scaling up inference acceleration of large language models via training-time test. Advances in Neural Information Processing Systems, 38:136737–136756, 2026. Alexander H Liu, Kartik Khandelwal, Sandeep Subramanian, Victor Jouault, Abhinav Rastogi, Adrien Sadé, Alan Jeffares, Albe","arxiv_match":false},{"id":"BpPvJxQxg5","title":"FiT: from market language to market decisions","forum_url":"https://openreview.net/forum?id=BpPvJxQxg5","excerpt":"imir I. Levenshtein. On bounds for packings in n-dimensional Euclidean space. Soviet Mathematics Doklady, 20:417–421, 1979. Siyang Li, Yize Chen, Zijie Zhu, et al. TimeRFT: Stimulating generalizable time series forecasting for TSFMs via reinforcement finetuning. arXiv preprint arXiv:2605.00015, 2026. Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. EAGLE-3: Scaling up inference acceleration of large language models via training- time test. arXiv preprint arXiv:2503.01840, 2025. Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Co","arxiv_match":true}],"id":"2503.01840","metadata_source":"Cached arXiv API metadata","rank":579},{"title":"LIMA: Less is more for alignment","arxiv_id":"2305.11206","year":2023,"url":"https://arxiv.org/abs/2305.11206","citing_submissions":179,"explicit_arxiv_matches":30,"examples":[{"id":"CFVCzCNC1l","title":"Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=CFVCzCNC1l","excerpt":"132, 2025. Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. LIMA: Less is more for alignment. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 55006–55021. Curran Associates, Inc., 2023. doi: 10.5","arxiv_match":false},{"id":"vvAAVy99dI","title":"Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking","forum_url":"https://openreview.net/forum?id=vvAAVy99dI","excerpt":"ssociation for Computational Linguistics: ACL 2024, pp. 12834–12859, 2024. Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. Lima: Less is more for alignment. Advances in Neural Information Processing Systems, 36:55006–55021, 2023. A C OMPLEXITY A NALYSIS A.1 T IME C OMPLEXITY Table 7: Time complexity of reranking K passages. Enc- and dec-based rerankers ","arxiv_match":false},{"id":"dvyMwimtP5","title":"Beyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training","forum_url":"https://openreview.net/forum?id=dvyMwimtP5","excerpt":"ns, 2023b. Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. Lima: Less is more for alignment. In Advances in Neural Information Processing Systems, 2023. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et ","arxiv_match":false}],"id":"2305.11206","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":580},{"title":"Reasoning with Latent Thoughts: On the Power of Looped Transformers","arxiv_id":"2502.17416","year":2025,"url":"https://arxiv.org/abs/2502.17416","citing_submissions":179,"explicit_arxiv_matches":47,"examples":[{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":"23314. Zirui Ren and Ziming Liu. Are your reasoning models reasoning or guessing? a mechanistic analysis of hierarchical reasoning models. arXiv preprint arXiv:2601.10679, 2026. URL https: //arxiv.org/abs/2601.10679. Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, and Sashank J. Reddi. Reasoning with latent thoughts: On the power of looped transformers. In International Conference on Learning Representations (ICLR), 2025. URL https://arxiv.org/abs/2502.17416. Amin Sghaier, Ali Parviz, and Alexia Jolicoeur-Martineau. Probabilistic tiny recursive model. arXi","arxiv_match":true},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"ings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 8732–8740, 2020. doi: 10.1609/aaai.v34i05.6399. Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, and Sashank J Reddi. Reasoning with latent thoughts: On the power of looped transformers. In International Conference on Learning Representations, volume 2025, pp. 14855–14881, 2025. Imanol Schlag, Kazuki Irie, and Jürgen Schmidhuber. Linear transformers are secretly fast weight program","arxiv_match":false},{"id":"m61N49mzzc","title":"Improving Test-Time Scaling with Adaptive Looped Transformers","forum_url":"https://openreview.net/forum?id=m61N49mzzc","excerpt":"arXiv preprint arXiv:2311.12022, 2023. Zirui Ren and Ziming Liu. Are Your Reasoning Models Reasoning or Guessing? A Mechanistic Analysis of Hierarchical Reasoning Models. arXiv preprint arXiv:2601.10679, 2026. URL https://arxiv.org/abs/2601.10679. Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, and Sashank J. Reddi. Reasoning with Latent Thoughts: On the Power of Looped Transformers. arXiv preprint arXiv:2502.17416, 2025. URL https://arxiv.org/abs/2502.17416. Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Tran, Yi Tay, and Donald M","arxiv_match":true}],"id":"2502.17416","metadata_source":"Cached arXiv API metadata","rank":581},{"title":"SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model","arxiv_id":"2502.02737","year":2025,"url":"https://arxiv.org/abs/2502.02737","citing_submissions":179,"explicit_arxiv_matches":165,"examples":[{"id":"cDlj67IBe4","title":"Learning Length Priors for Canvas Allocation in Masked Diffusion Language Models","forum_url":"https://openreview.net/forum?id=cDlj67IBe4","excerpt":"Lozhkov, Elie Bakouch, Gabriel Martín Blázquez, Guilherme Penedo, Lewis Tunstall, Andrés Marafioti, Hynek Kydlíček, Agustín Piqueres Lajarín, Vaibhav Srivastav, Joshua Lochner, Caleb Fahlgren, Xuan-Son Nguyen, Clémentine Fourrier, Ben Burtenshaw, Hugo Larcher, Haojun Zhao, Cyril Zakka, Mathieu Morlon, Colin Raffel, Leandro von Werra, and Thomas Wolf. SmolLM2: When Smol Goes Big – Data-Centric Training of a Small Language Model, 2025. URL https://arxiv.org/abs/2502.02737. Wail Bouhedja, Amr Mohamed, and Guokan Shang. Carve: Verified expansion for variable-length generat","arxiv_match":true},{"id":"Fhj95cY1wD","title":"Decoupling Token Roles in Autoregressive Pretraining","forum_url":"https://openreview.net/forum?id=Fhj95cY1wD","excerpt":" L Leavitt, and Mansheej Paul. Perplexed by perplexity: Perplexity-based data pruning with small reference models. In International Conference on Learning Representations, 2025. Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Gabriel Martín Blázquez, Guilherme Penedo, Lewis Tunstall, Andrés Marafioti, Hynek Kydlíček, Agustín Piqueres Lajarín, Vaibhav Srivastav, et al. SmolLM2: When smol goes big – data-centric training of a small language model. arXiv preprint arXiv:2502.02737, 2025. Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for sequenc","arxiv_match":true},{"id":"uYjAgwpnQm","title":"RelFed: Relative-Representation Federation of Heterogeneous Small Language Models for Multi-Site AIoT Sensing","forum_url":"https://openreview.net/forum?id=uYjAgwpnQm","excerpt":"Yao, and Yaliang Li. Federated fine-tuning of large language models under heterogeneous tasks and client resources. In Advances in Neural Information Processing Systems, 2024. Loubna Ben Allal et al. SmolLM2: When Smol goes big – data-centric training of a small language model, 2025. Irene Cannistraci, Luca Moschella, Marco Fumero, Valentino Maiorca, and Emanuele Rodolà. From bricks to bridges: Product of invariances to enhance latent space communication. In International ","arxiv_match":false}],"id":"2502.02737","metadata_source":"Cached arXiv API metadata","rank":582},{"title":"D4RL: Datasets for deep data-driven reinforcement learning","arxiv_id":"2004.07219","year":2020,"url":"https://arxiv.org/abs/2004.07219","citing_submissions":178,"explicit_arxiv_matches":144,"examples":[{"id":"6FEw1esZ1j","title":"Kuramoto Decision Diffusion Models","forum_url":"https://openreview.net/forum?id=6FEw1esZ1j","excerpt":"l of Mathematical Biology, 22(1):1–9, 1985. Cited on page 2. Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson. Implicit behavioral cloning. In Conference on robot learning, pp. 158–168. PMLR, 2022. Cited on pages 8 and 37. Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219, 2020. Cited on pages 7, 8, 36, and 37. Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, a","arxiv_match":true},{"id":"dMaE5Aa6Fi","title":"OSCC: Certified Observation-Safe Coupling Optimization for Gradient-Noise Control in Imperfect-Information Learning","forum_url":"https://openreview.net/forum?id=dMaE5Aa6Fi","excerpt":" new variance reduction technique for agent evaluation in imperfect information games. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018. Lili Chen et al. Decision transformer: Reinforcement learning via sequence modeling. In Advances in Neural Information Processing Systems, 2021. Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning. In arXiv preprint arXiv:2004.07219, 2020. Paul Glasserman. Monte Carlo Methods in Financial Engineering. Springer, 2004. Johannes Hein","arxiv_match":true},{"id":"KvWRJQwcme","title":"AUDIT-FIRST VAPO: RISK-CERTIFIED SELECTIVE UPDATES UNDER IMPERFECT VERIFICATION","forum_url":"https://openreview.net/forum?id=KvWRJQwcme","excerpt":"k-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning. In International Conference on Learning Representations, 2021. Yonatan Geifman and Ran El-Yaniv. Selectivenet: A deep neural network with an integrated reject option. In International Conference on ","arxiv_match":false}],"id":"2004.07219","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":583},{"title":"Megatron-LM: Training multi-billion parameter language models using model parallelism","arxiv_id":"1909.08053","year":2019,"url":"https://arxiv.org/abs/1909.08053","citing_submissions":178,"explicit_arxiv_matches":165,"examples":[{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":"anguage models. arXiv preprint arXiv:2402.03300, 2024. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. Hybridflow: A flexible and efficient rlhf framework. arXiv preprint arXiv: 2409.19256, 2024. Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-lm: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053, 2019. Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, SH Cai, Yuan Cao, Y Charles, HS Che, Cheng C","arxiv_match":true},{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"ts improve transformer, 2020. N. Shazeer and M. Stern. Adafactor: Adaptive learning rates with sublinear memory cost, 2018. M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro. Megatron-lm: Training multi-billion parameter language models using model parallelism. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–12. ACM, 2021. M. Song, K. Ahn, and C. Yun. Does sgd really happen in tiny su","arxiv_match":false},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":"earning, pp. 18332–18346. PMLR, 2022. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017. Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-LM: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053, 2019. StepFun, Bin Wang, Bojun Wang, Changyi Wan, Guanzhe Huang, Hanpeng Hu, Haonan Jia, Hao Nie, ","arxiv_match":true}],"id":"1909.08053","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":584},{"title":"SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model","arxiv_id":"2501.15830","year":2025,"url":"https://arxiv.org/abs/2501.15830","citing_submissions":178,"explicit_arxiv_matches":115,"examples":[{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"e 32, 2018. Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. FAST: Efficient action tokenization for vision-language-action models. arXiv preprint arXiv:2501.09747, 2025. Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang, JiaYuan Gu, Bin Zhao, Dong Wang, et al. SpatialVLA: Exploring spatial representations for visual-languageaction model. arXiv preprint arXiv:2501.15830, 2025. Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh","arxiv_match":true},{"id":"6MjQdxXZoS","title":"VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=6MjQdxXZoS","excerpt":", Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al. Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 6892–6903. IEEE, 2024. Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang, JiaYuan Gu, Bin Zhao, Dong Wang, et al. Spatialvla: Exploring spatial representations for visuallanguage-action model. arXiv preprint arXiv:2501.15830, 2025. Mohit Shridhar, Lucas Manuelli, and Dieter Fox. Perceiver-actor: A multi-task transformer fo","arxiv_match":true},{"id":"Lc2iGieCPZ","title":"Language-Forcing: Semantic Alignment for Whole-Body World Action Model","forum_url":"https://openreview.net/forum?id=Lc2iGieCPZ","excerpt":"tachowicz, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, and Ury Zhilinsky. π0.5 : a vision-language-action model with open-world generalization, 2025. URL https://arxiv. org/abs/2504.16054. Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang, JiaYuan Gu, Bin Zhao, Dong Wang, and Xuelong Li. SpatialVLA: Exploring spatial representations for visual-language-action model, 2025. URL https://arxiv.org/abs/2501.15830. Luis Sentis and Oussama Khatib. A whole-body control framework for humanoids operating in human en","arxiv_match":true}],"id":"2501.15830","metadata_source":"Cached arXiv API metadata","rank":585},{"title":"The wmdp benchmark: Measuring and reducing malicious use with unlearning","arxiv_id":"2403.03218","year":2024,"url":"https://arxiv.org/abs/2403.03218","citing_submissions":178,"explicit_arxiv_matches":89,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"tis, Steven Basart, Stephen Fitz, Ponnurangam Kumaraguru, Kallol Krishna Karmakar, Uday Tupakula, Vijay Varadharajan, Yan Shoshitaishvili, Jimmy Ba, Kevin M. Esvelt, Alexandr Wang, and Dan Hendrycks. The WMDP benchmark: Measuring and reducing malicious use with unlearning. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview. net/forum?id=xlr6AUDuJz. Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for ge","arxiv_match":false},{"id":"bdZAOqTSho","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","forum_url":"https://openreview.net/forum?id=bdZAOqTSho","excerpt":"arning with LLM beliefs. In ICLR, 2026. Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D. Li, Ann-Kathrin Dombrowski, Shashwat Goel, Gabriel Mukobi, et al. The WMDP benchmark: Measuring and reducing malicious use with unlearning. In ICML, 2024. Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, and Haibing Guan. Adversarial example does good: Preventing painting imitation from diffu","arxiv_match":false},{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":"rom a language model. In NeurIPS, 2023. Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D. Li, Ann-Kathrin Dombrowski, Shashwat Goel, Gabriel Mukobi, et al. The WMDP benchmark: Measuring and reducing malicious use with unlearning. In ICML, 2024. Chris Yuhao Liu, Yaxuan Wang, Jeffrey Flanigan, and Yang Liu. Large language model unlearning via embedding-corrupted prompts. In NeurIPS, 2024a. Sijia Liu, Yuanshun Yao, Jinghan Jia,","arxiv_match":false}],"id":"2403.03218","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":586},{"title":"Titans: Learning to Memorize at Test Time","arxiv_id":"2501.00663","year":2024,"url":"https://arxiv.org/abs/2501.00663","citing_submissions":178,"explicit_arxiv_matches":62,"examples":[{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":" understanding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3119–3137, 2024. Ali Behrouz, Peilin Zhong, and Vahab Mirrokni. Titans: Learning to memorize at test time. Advances in Neural Information Processing Systems, 38:113506–113543, 2026. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. PIQA: Reasoning about physical commonsense in nat","arxiv_match":false},{"id":"efYt84hV53","title":"Gap-Aware Predictive Memory for Test Time Training","forum_url":"https://openreview.net/forum?id=efYt84hV53","excerpt":"v.org/abs/ 2412.15204. Rachit Bansal, Aston Zhang, Rishabh Tiwari, Lovish Madaan, Sai Surya Duvvuri, Devvrit Khatri, David Brandfonbrener, David Alvarez-Melis, Prajjwal Bhargava, Mihir Sanjay Kale, and Samy Jelassi. Let’s (not) just put things in context: Test-time training for long-context llms, 2025. URL https://arxiv.org/abs/2512.13898. Ali Behrouz, Peilin Zhong, and Vahab Mirrokni. Titans: Learning to memorize at test time, 2024. URL https://arxiv.org/abs/2501.00663. Ali Behrouz, Zeman Li, Praneeth Kacham, Majid Daliri, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, ","arxiv_match":true},{"id":"N6ehRHGZfr","title":"STAMP: Learning Cross-Session Memory in Native Recurrent States","forum_url":"https://openreview.net/forum?id=N6ehRHGZfr","excerpt":", Vahab Mirrokni, and Aaron Courville. Proteus: Incremental memory activation for long-context sequence modeling. arXiv preprint arXiv:2608.16844, 2026. Ali Behrouz, Peilin Zhong, and Vahab Mirrokni. Titans: Learning to memorize at test time. In Advances in Neural Information Processing Systems, volume 38, pp. 113506–113543, 2025. Aydar Bulatov, Yury Kuratov, and Mikhail Burtsev. Recurrent memory transformer. In Advances in Neural Inform","arxiv_match":false}],"id":"2501.00663","metadata_source":"Cached arXiv API metadata","rank":587},{"title":"Training language models to self-correct via reinforcement learning","arxiv_id":"2409.12917","year":2024,"url":"https://arxiv.org/abs/2409.12917","citing_submissions":178,"explicit_arxiv_matches":54,"examples":[{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, and Aleksandra Faust. Training language models to self-correct via reinforcement learning. In International Conference on Learning Representations (ICLR), 2025. https://proceedings.iclr.cc/paper_files/paper/2025/hash/ 871ac99fdc5282d0301934d23945ebaa-Abstract-Conference.html. Jung Hwan Le","arxiv_match":false},{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"system card. arXiv preprint arXiv:2410.21276, 2024. Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, et al. Training language models to self-correct via reinforcement learning. In International Conference on Learning Representations, volume 2025, pp. 54523–54549, 2025. Manling Li, Shiyu Zhao, Qineng Wang, Kangrui Wang, Yu Zhou, Sanjana Srivastava, Cem Gokmen, Tony Lee, Li ","arxiv_match":false},{"id":"UeO5A1vhy1","title":"Correcting Is Not Reusing: What a Failed Attempt Contributes Beyond the Verdict","forum_url":"https://openreview.net/forum?id=UeO5A1vhy1","excerpt":"r own mistakes? A critical survey of self-correction of LLMs. Transactions of the Association for Computational Linguistics, 12:1417–1440, 2024. arXiv:2406.01297. Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, et al. Training language models to selfcorrect via reinforcement learning. In International Conference on Learning Representations (ICLR), 2025. arXiv:2409.12917. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, H","arxiv_match":true}],"id":"2409.12917","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":588},{"title":"Real-Time Execution of Action Chunking Flow Policies","arxiv_id":"2506.07339","year":2025,"url":"https://arxiv.org/abs/2506.07339","citing_submissions":177,"explicit_arxiv_matches":54,"examples":[{"id":"ylrbHy6YvK","title":"KI-VLA: Kinematics-Informed Action Decoding for Flow-Matching Robot Policies","forum_url":"https://openreview.net/forum?id=ylrbHy6YvK","excerpt":". π0 : A vision-language-action flow model for general robot control. In Robotics: Science and Systems XXI, 2025b. doi: 10.15607/RSS.2025. XXI.010. Kevin Black, Manuel Y. Galliker, and Sergey Levine. Real-time execution of action chunking flow policies. In Advances in Neural Information Processing Systems, volume 38, 2025c. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avin","arxiv_match":false},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"usai, Manuel Y Galliker, et al. Pi0.5: a visionlanguage-action model with open-world generalization. In 9th Annual Conference on Robot Learning, 2025. Kevin Black, Manuel Galliker, and Sergey Levine. Real-time execution of action chunking flow policies. Advances in Neural Information Processing Systems, 38:33383–33407, 2026. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Ha","arxiv_match":false},{"id":"f7uO3rAyIZ","title":"STAR: Practical Backdoor Attacks Against Vision-Language-Action Models via Semantic Triggers","forum_url":"https://openreview.net/forum?id=f7uO3rAyIZ","excerpt":"lack, Noah Brown, James Darpinian, et al. π0.5 : A vision-language-action model with open-world generalization. arXiv preprint arXiv:2504.16054, 2025. Kevin Black, Manuel Galliker, and Sergey Levine. Real-time execution of action chunking flow policies. Advances in Neural Information Processing Systems, 38:33383–33407, 2026. Anthony Brohan, Noah Brown, Justice Carbajal, et al. RT-1: Robotics transformer for real-world control at scale. In Proceedin","arxiv_match":false}],"id":"2506.07339","metadata_source":"Cached arXiv API metadata","rank":589},{"title":"ScienceAgentBench: Toward rigorous assessment of language agents for data-driven scientific discovery","arxiv_id":"2410.05080","year":2024,"url":"https://arxiv.org/abs/2410.05080","citing_submissions":177,"explicit_arxiv_matches":41,"examples":[{"id":"P9bJmoIx5X","title":"Controlling False Discoveries via Auditing Scientific Agents","forum_url":"https://openreview.net/forum?id=P9bJmoIx5X","excerpt":", Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, Vishal Dey, Mingyi Xue, Frazier N. Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, and Huan Sun. ScienceAgentBench: Toward rigorous assessment of language agents for data-driven scientific discovery. In International Conference on Learning Representations (ICLR), 2025. URL https: //openreview.net/forum?id=6z4YKr0GK6. EunYi Chung and Joseph P. Romano. Exact and asymptotically robust permutation t","arxiv_match":false},{"id":"6EWMGy11eE","title":"CreditAgentBench: Measuring Agentic Credit-Model Repair under Production Constraints","forum_url":"https://openreview.net/forum?id=6EWMGy11eE","excerpt":", Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, Vishal Dey, Mingyi Xue, Frazier N. Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, and Huan Sun. Scienceagentbench: Toward rigorous assessment of language agents for data-driven scientific discovery. In The Thirteenth International Conference on Learning Representations, 2025. Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big","arxiv_match":false},{"id":"z9LtbKknbT","title":"Beyond Retrieval and Fine-tuning: Learning from Scientific Execution Experience for Self-Improving Agents","forum_url":"https://openreview.net/forum?id=z9LtbKknbT","excerpt":", Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, Vishal Dey, Mingyi Xue, Frazier N. Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, and Huan Sun. ScienceAgentBench: Toward rigorous assessment of language agents for data-driven scientific discovery. In International Conference on Learning Representations, 2025. Xingyi Cheng, Bo Chen, Pan Li, Jing Gong, Jie Tang, and Le Song. Training compute-optimal protein language models. In Advances in Neura","arxiv_match":false}],"id":"2410.05080","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":590},{"title":"VACE: All-in-One Video Creation and Editing","arxiv_id":"2503.07598","year":2025,"url":"https://arxiv.org/abs/2503.07598","citing_submissions":177,"explicit_arxiv_matches":30,"examples":[{"id":"gm7CWhtpxy","title":"Towards Coherent Scene Reconstruction in Video Object Removal","forum_url":"https://openreview.net/forum?id=gm7CWhtpxy","excerpt":"on-driven benchmark for video object removal. arXiv preprint arXiv:2609.16878, 2026. URL https://arxiv.org/abs/2609.16878. Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu. VACE: Allin-one video creation and editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 17191–17202, 2025. URL https://ali-vilab.github.io/ VACE-Page/. Jaeyeon Kang, Seoung Wug Oh, and Seon Joo Kim. Error c","arxiv_match":false},{"id":"fx6TAsHANs","title":"Edit in Sync: Unified End-to-End Audio-Visual Editing for Human-Centric Videos","forum_url":"https://openreview.net/forum?id=fx6TAsHANs","excerpt":"herent audio-visual editing via conditional audio generation following video edits. arXiv preprint arXiv:2512.07209, 2025. Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu. VACE: All-in-one video creation and editing. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 17191–17202. IEEE, 2025. Hoiyeong Jin, Hyojin Jang, Jeongho Kim, Junha Hyung, Kinam Kim, Dongjin Kim, Huijin Choi, Hyeonji Ki","arxiv_match":false},{"id":"aocYTIRnhk","title":"GeoVerse: World-Consistent Novel View Synthesis in Geometric Latent Space","forum_url":"https://openreview.net/forum?id=aocYTIRnhk","excerpt":"splat: Feed-forward 3d gaussian splatting from unconstrained views. ACM Transactions on Graphics (TOG), 44(6):1–16, 2025a. Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu. Vace: All-in-one video creation and editing. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 17191–17202. IEEE, 2025b. Haian Jin, Hanwen Jiang, Hao Tan, Kai Zhang, Sai Bi, Tianyuan Zhang, Fujun Luan, Noah Snavely, and ","arxiv_match":false}],"id":"2503.07598","metadata_source":"Cached arXiv API metadata","rank":591},{"title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","arxiv_id":null,"year":2015,"url":null,"citing_submissions":175,"explicit_arxiv_matches":0,"examples":[{"id":"jjf3FKJp3k","title":"MetaBrainPro: A Graph Learning Approach for Enterprise Metadata Lineage Retrieval","forum_url":"https://openreview.net/forum?id=jjf3FKJp3k","excerpt":"arXiv:1502.01852, 2015. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2016. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Thomas N Kipf and Max Welling. Semi-supervise","arxiv_match":false},{"id":"ToTuhzZPcv","title":"How Does Loss Landscape Geometry Transfer Across Scales?","forum_url":"https://openreview.net/forum?id=ToTuhzZPcv","excerpt":"wig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In The Eleventh International Conference on Learning Representations, 2023. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pp. 448–456, 2015. Akira Ito, Masanori Yamada, Daiki Chijiwa, and Atsutoshi Kumagai. Do we rea","arxiv_match":false},{"id":"vI823T4gSD","title":"Learning to Compare Neural Representations from Parameterized Computation Graphs","forum_url":"https://openreview.net/forum?id=vI823T4gSD","excerpt":"man, William Rucklidge, et al. Comparing images using the hausdorff distance. IEEE Transactions on pattern analysis and machine intelligence, 15(9): 850–863, 1993. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448–456. pmlr, 2015. Ioannis Kalogeropoulos, Giorgos Bouritsas, and Yannis Panagakis. Scale equivariant graph metanetworks. In The Thirty-eighth ","arxiv_match":false}],"id":"bda4b6132f70f1b4","metadata_source":"Curated bibliography catalog / publisher metadata","rank":592},{"title":"CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks","arxiv_id":"2112.03227","year":2021,"url":"https://arxiv.org/abs/2112.03227","citing_submissions":175,"explicit_arxiv_matches":39,"examples":[{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"t2: Exploring the scaling limit of umi data towards zero-shot cross-embodiment generalization. arXiv preprint arXiv:2602.03310, 2026. Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard. Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters, 7(3):7327–7334, 2022. Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, and Hao Su. Maniskill: Generalizable mani","arxiv_match":false},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":" reasoning benchmark. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6924–6934, October 2025. Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard. Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters (RA-L), 7(3):7327–7334, 2022. NVIDIA, Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi ”Jim” Fan, Yu Fang, Dieter Fox, Fengyuan","arxiv_match":false},{"id":"vwDnaAnR3T","title":"When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models","forum_url":"https://openreview.net/forum?id=vwDnaAnR3T","excerpt":"n: A data generation system for scalable robot learning using human demonstrations. In 7th Annual Conference on Robot Learning, 2023. Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard. Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters, 7(3):7327–7334, 2022. Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163, 20","arxiv_match":false}],"id":"2112.03227","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":593},{"title":"Fine-grained visual classification of aircraft","arxiv_id":"1306.5151","year":2013,"url":"https://arxiv.org/abs/1306.5151","citing_submissions":175,"explicit_arxiv_matches":149,"examples":[{"id":"aVu1Zo8kCI","title":"Beyond Holistic Representations: Dual Distribution Modeling for Test-Time Adaptation of Vision–Language Models","forum_url":"https://openreview.net/forum?id=aVu1Zo8kCI","excerpt":"ter-efficient modification. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 14394–14410, 2024. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In Proceedings of the 36th Advances in Neural Information Processing Systems (NeurIPS’23), 2023. Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large numb","arxiv_match":true},{"id":"HVg2GtOyJV","title":"When [CLS] Falls Short: Object-Aware Feature Displacement for Robust Generalized Category Discovery","forum_url":"https://openreview.net/forum?id=HVg2GtOyJV","excerpt":"and Pattern Recognition Conference, pp. 9891–9900, 2025. Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. Advances in neural information processing systems, 33:11525–11538, 2020. Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow. Realist","arxiv_match":true},{"id":"tlahLcI2zN","title":"Beyond Category Assignments: Learning Sample-Prototype Dynamics for Generalized Category Discovery","forum_url":"https://openreview.net/forum?id=tlahLcI2zN","excerpt":"Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In ICLR, 2023. Yuanpei Liu and Kai Han. DebGCD: Debiased learning with distribution guidance for generalized category discovery. In ICLR, 2025. Yuanpei Liu, Zhenqi He, and Kai Han. Hyperbolic category discovery. In CVPR, 2025c. Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, P","arxiv_match":true}],"id":"1306.5151","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":594},{"title":"Generalizing Verifiable Instruction Following","arxiv_id":"2507.02833","year":2025,"url":"https://arxiv.org/abs/2507.02833","citing_submissions":175,"explicit_arxiv_matches":50,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"LR, 2025. URL https://proceedings.mlr.press/v267/patil25a.ht ml. Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, and Hannaneh Hajishirzi. Generalizing verifiable instruction following. In Advances in Neural Information Processing Systems, volume 38. Curran Associates, Inc., 2025. doi: 10.52202/085713-1645. URL https://proceedings.neurips.cc/paper_fil es/paper/2025/hash/46499a0622e","arxiv_match":false},{"id":"a0TQ3mSSY5","title":"NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents","forum_url":"https://openreview.net/forum?id=a0TQ3mSSY5","excerpt":"lp-main.494. 494/. URL https://aclanthology.org/2023.emnlp-main. Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, and Hannaneh Hajishirzi. Generalizing verifiable instruction following. In Advances in Neural Information Processing Systems, 2025. URL https://proceedings.neurips.cc/paper_files/paper/2025/ hash/46499a0622ecf568b72d17b61e45dbd5-Abstract-Datasets_and_ Benchmarks_Track.h","arxiv_match":false},{"id":"JmF5x6QI3i","title":"Evolving Support Priorities in Empathetic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=JmF5x6QI3i","excerpt":"3: Emotional intelligence benchmark, 2025. Benchmark repository. Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, and Hannaneh Hajishirzi. Generalizing verifiable instruction following. In NeurIPS, 2025. Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yuxin Chen, Cilin Yan, Jiayin Cai, Xiaolong Jiang, Yao Hu, Yoko Yamakata, and Tat-Seng Chua. Preference-aware rubric learning for personalized ","arxiv_match":false}],"id":"2507.02833","metadata_source":"Cached arXiv API metadata","rank":595},{"title":"RAPTOR: Recursive abstractive processing for tree-organized retrieval","arxiv_id":"2401.18059","year":2024,"url":"https://arxiv.org/abs/2401.18059","citing_submissions":175,"explicit_arxiv_matches":30,"examples":[{"id":"26oXJe49qk","title":"MemGym: a Long-Horizon Memory Environment for LLM Agents","forum_url":"https://openreview.net/forum?id=26oXJe49qk","excerpt":"Zaragoza. The probabilistic relevance framework: BM25 and beyond, volume 4. Now Publishers Inc, 2009. Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D. Manning. RAPTOR: Recursive abstractive processing for tree-organized retrieval. In International Conference on Learning Representations (ICLR), 2024. Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scial","arxiv_match":false},{"id":"1V3066nDJF","title":"Contract Memory Compiler: Resolve, Then Traverse","forum_url":"https://openreview.net/forum?id=1V3066nDJF","excerpt":"tion from policy execution. arXiv preprint arXiv:2606.01435, 2026. URL https://arxiv.org/abs/2606.01435. Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher Manning. RAPTOR: Recursive abstractive processing for tree-organized retrieval. In International Conference on Learning Representations, 2024. URL https://proceedings.iclr.cc/paper_files/paper/2024/hash/ 8a2acd174940dbca361a6398a4f9df91-Abstract-Conference.html. Yiting Shen, Ku","arxiv_match":false},{"id":"WqaXwcWgJT","title":"StrataDistill: Structure-Aware Distillation for Long-Document Retrieval","forum_url":"https://openreview.net/forum?id=WqaXwcWgJT","excerpt":"pter of the Association for Computational Linguistics: Human Language Technologies, pp. 3715–3734, 2022. Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher Manning. Raptor: Recursive abstractive processing for tree-organized retrieval. In International Conference on Learning Representations, volume 2024, pp. 32628–32649, 2024. Shruti Singh and Mayank Singh. Cosaemb: Contrastive section-aware aspect embeddings for scientific articl","arxiv_match":false}],"id":"2401.18059","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":596},{"title":"Accelerating scientific discovery with Co-Scientist","arxiv_id":"2502.18864","year":2025,"url":"https://arxiv.org/abs/2502.18864","citing_submissions":174,"explicit_arxiv_matches":51,"examples":[{"id":"P9bJmoIx5X","title":"Controlling False Discoveries via Auditing Scientific Agents","forum_url":"https://openreview.net/forum?id=P9bJmoIx5X","excerpt":"Preprint: arXiv:2505.13400, “Robin: A multi-agent system for automating scientific discovery”. Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Petar Sirkovic, Artiom Myaskovsky, Grzegorz Glowaty, Felix Weissenberger, Alessio Orlandi, Dan Popovici, et al. Accelerating scientific discovery with Co-Scientist. Nature, 655(8122):487–496, 2026. doi: 10.1038/s41586-026-10644-y. URL https://www.nature.com/articles/ s41586-026-10644-y. Preprint: arXiv:2502.18864, “Towards an AI co-scientist”. Ken Gu, Ruoxi Shang, Ruien Jiang, Keying Kuang, Richard-John Lin, Do","arxiv_match":true},{"id":"fG6gMiEzqt","title":"From Measured Biology to Analysis Decisions: How Far Does Post-Trained Knowledge Travel?","forum_url":"https://openreview.net/forum?id=fG6gMiEzqt","excerpt":"eprint arXiv:2406.12793, 2024. Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Petar Sirkovic, Artiom Myaskovsky, Grzegorz Glowaty, Felix Weissenberger, Alessio Orlandi, Dan Popovici, et al. Accelerating scientific discovery with co-scientist. Nature, pp. 1–3, 2026. Emre Hayir, Lorin Crawford, and Alex X Lu. Morphohelm: A comprehensive benchmark for evaluating representations for microscopy-based morphology assays. arXiv preprint arXiv:26","arxiv_match":false},{"id":"gll8jlEkbo","title":"Surprising Success, Repeated Failure: Entropy-Guided Credit Assignment for Exploration in LLM Reasoning","forum_url":"https://openreview.net/forum?id=gll8jlEkbo","excerpt":", pp. 594–606. Springer, 2025. Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Petar Sirkovic, Artiom Myaskovsky, Grzegorz Glowaty, Felix Weissenberger, Alessio Orlandi, Dan Popovici, et al. Accelerating scientific discovery with co-scientist. Nature, 655(8122):487–496, 2026. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. Deepseek-r1 incentivizes reasoning in l","arxiv_match":false}],"id":"2502.18864","metadata_source":"Cached arXiv API metadata","rank":597},{"title":"LTX-2: Efficient Joint Audio-Visual Foundation Model","arxiv_id":"2601.03233","year":2026,"url":"https://arxiv.org/abs/2601.03233","citing_submissions":174,"explicit_arxiv_matches":165,"examples":[{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":"iprut, Yaki Bitterman, Andrew Kvochko, Avishai Berkowitz, Daniel Shalem, Daphna Lifschitz, Dudu Moshe, Eitan Porat, Eitan Richardson, Guy Shiran, Itay Chachy, Jonathan Chetboun, Michael Finkelson, Michael Kupchick, Nir Zabari, Nitzan Guetta, Noa Kotler, Ofir Bibi, Ori Gordon, Poriya Panet, Roi Benita, Shahar Armon, Victor Kulikov, Yaron Inger, Yonatan Shiftan, Zeev Melumian, and Zeev Farbman. Ltx-2: Efficient joint audio-visual foundation model. arXiv preprint arXiv:2601.03233, 2025. Seokil Ham, Sangmin Woo, Jin-Young Kim, Hyojun Go, Byeongjun Park, and Changick Kim. Diffusi","arxiv_match":true},{"id":"h9oDIdD6ok","title":"Cine-Long: Consistent Long-Horizon Multi-Shot Audio-Visual Generation","forum_url":"https://openreview.net/forum?id=h9oDIdD6ok","excerpt":"23383, 2026. URL https://arxiv.org/abs/2608. 23383. Ridouane Ghermi, Xi Wang, Vicky Kalogeiton, and Ivan Laptev. Long story short: Story-level video understanding from 20k short films. International Journal of Computer Vision, 134:396, 2026. doi: 10.1007/s11263-026-02940-x. Yoav HaCohen, Benny Brazowski, Nisan Chiprut, Yaki Bitterman, et al. LTX-2: Efficient joint audio-visual foundation model. arXiv preprint arXiv:2601.03233, 2026. URL https: //arxiv.org/abs/2601.03233. Roberto Henschel, Levon Khachatryan, Daniil Hayrapetyan, Hayk Poghosyan, Vahram Tadevosyan, Zhangy","arxiv_match":true},{"id":"fx6TAsHANs","title":"Edit in Sync: Unified End-to-End Audio-Visual Editing for Human-Centric Videos","forum_url":"https://openreview.net/forum?id=fx6TAsHANs","excerpt":" Qichao Sun, Liyang Chen, Bingchuan Li, Pengze Zhang, Jiawei Liu, Songtao Zhao, Qian He, and Xiangwang Hou. DreamID-Omni: Unified framework for controllable human-centric audio-video generation. arXiv preprint arXiv:2602.12160, 2026. Yoav HaCohen, Benny Brazowski, Nisan Chiprut, Yaki Bitterman, Andrew Kvochko, Avishai Berkowitz, Daniel Shalem, Daphna Lifschitz, Dudu Moshe, Eitan Porat, et al. LTX-2: Efficient joint audio-visual foundation model. arXiv preprint arXiv:2601.03233, 2026. Xu He, Haoxian Zhang, Hejia Chen, Changyuan Zheng, Liyang Chen, Songlin Tang, Jiehui Huang, ","arxiv_match":true}],"id":"2601.03233","metadata_source":"Cached arXiv API metadata","rank":598},{"title":"Olmoe: Open mixture-of-experts language models","arxiv_id":"2409.02060","year":2024,"url":"https://arxiv.org/abs/2409.02060","citing_submissions":174,"explicit_arxiv_matches":70,"examples":[{"id":"vSjw3bWmyF","title":"Who Contributes? Counterfactual Attribution in Sparse Mixture-of-Experts","forum_url":"https://openreview.net/forum?id=vSjw3bWmyF","excerpt":"hane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah A. Smith, Pang Wei Koh, Amanpreet Singh, and Hannaneh Hajishirzi. OLMoE: Open mixture-of-experts language models. In International Conference on Learning Representations, 2025. Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck, Man Luo, Sungduk Yu, Chendi Xue, and Vasudev Lal. Probing semantic routing in large ","arxiv_match":false},{"id":"J6s4pH2a2M","title":"LLM2llm: Parameter-Efficient Adaptation via Transferred Experts from MoE LLMs","forum_url":"https://openreview.net/forum?id=J6s4pH2a2M","excerpt":"ing, 2019. URL https://arxiv.org/abs/1906.10771. Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, et al. Olmoe: Open mixture-of-experts language models. In International Conference on Learning Representations, volume 2025, pp. 62061–62121, 2025. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. Medmcqa: A large-scale multi-subject mult","arxiv_match":false},{"id":"IURFuR16tB","title":"Cobalt: Leveraging Expert Co-activation for Efficient Distributed MoE Training","forum_url":"https://openreview.net/forum?id=IURFuR16tB","excerpt":" Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah A. Smith, Pang Wei Koh, Amanpreet Singh, and Hannah Hajishirzi. OLMoE: Open Mixture-of-Experts Language Models. In International Conference on Learning Representations, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/hash/ 9b224ace8963c9385ad5e2b5c9039b97-Abstract-Conference.html. Xiaonan Nie, Xu","arxiv_match":false}],"id":"2409.02060","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":599},{"title":"Kimi Linear: An Expressive, Efficient Attention Architecture","arxiv_id":"2510.26692","year":2025,"url":"https://arxiv.org/abs/2510.26692","citing_submissions":173,"explicit_arxiv_matches":159,"examples":[{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":" Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. Mixtral of experts. arXiv preprint arXiv:2401.04088, 2024a. Chenyu Jiang, Ye Tian, Zhen Jia, Shuai Zheng, Chuan Wu, and Yida Wang. Lancet: Accelerating mixture-of-experts training via whole graph computation-communication overlapping. Proceedings of Machine Learning and Systems, 6:74–86, 2024b. Kimi Team. Kimi Linear: An expressive, efficient attention architecture, 2025. URL https: //arxiv.org/abs/2510.26692. Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthi","arxiv_match":true},{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"g, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive Benchmark Suite for Video Generative Models. In CVPR, 2024. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. In ICML, 2020. Kimi Team. Kimi Linear: An Expressive, Efficient Attention Architecture. arXiv preprint arXiv:2510.26692, 2025. Kimi Team. Kimi K3: Open Frontier Intelligence. Moonshot AI technical blog, https://www.kimi","arxiv_match":true},{"id":"OAX2QPoTJg","title":"HyLin: Modality-Aware Hybrid Attention for Efficient Long-Context Vision–Language Models","forum_url":"https://openreview.net/forum?id=OAX2QPoTJg","excerpt":"n Su, Jiezhong Qiu, Bo Pang, Junjie Yan, Zhejun Jiang, Weixiao Huang, Bohong Yin, Jiacheng You, Chu Wei, Zhengtao Wang, Chao Hong, Yutian Chen, Guanduo Chen, Yucheng Wang, Huabin Zheng, Feng Wang, Yibo Liu, Mengnan Dong, Zheng Zhang, Siyuan Pan, Wenhao Wu, Yuhao Wu, Longyu Guan, Jiawen Tao, Guohong Fu, Xinran Xu, Yuzhi Wang, Guokun Lai, Yuxin Wu, Xinyu Zhou, Zhilin Yang, and Yulun Du. Kimi linear: An expressive, efficient attention architecture. arXiv preprint arXiv:2510.26692, 2025. Dezhan Tu, Danylo Vashchilenko, Yuzhe Lu, and Panpan Xu. VL-Cache: Sparsity and ModalityAwar","arxiv_match":true}],"id":"2510.26692","metadata_source":"Cached arXiv API metadata","rank":600},{"title":"Massive activations in large language models","arxiv_id":"2402.17762","year":2024,"url":"https://arxiv.org/abs/2402.17762","citing_submissions":173,"explicit_arxiv_matches":82,"examples":[{"id":"vwvDGT3GUw","title":"When Anchors Commit, Coherence Breaks","forum_url":"https://openreview.net/forum?id=vwvDGT3GUw","excerpt":"01.02076, 2026a. Yingte Shu, Yuchuan Tian, Chao Xu, Yunhe Wang, and Hanting Chen. Deferred commitment decoding for diffusion language models with confidence-aware sliding windows. arXiv preprint arXiv:2601.02076, 2026b. Haocheng Sun, Cynthia Xin Wen, and Edward Hong Wang. Why mask diffusion does not work. arXiv preprint arXiv:2510.03289, 2025. Mingjie Sun, Xinlei Chen, J. Zico Kolter, and Zhuang Liu. Massive activations in large language models. arXiv preprint arXiv:2402.17762, 2024. Alexander Swerdlow, Mihir Prabhudesai, Siddharth Gandhi, Deepak Pathak, and Katerina Fragkia","arxiv_match":true},{"id":"Qma5CYaxtO","title":"Beyond Attribute-Centric Fairness: Modeling and Mitigating Unified Bias Representation in Omni-modal Large Language Models","forum_url":"https://openreview.net/forum?id=Qma5CYaxtO","excerpt":"ning text-to-image diffusion models for fairness. In International Conference on Learning Representations, volume 2024, pp. 25458–25500, 2024. Mingjie Sun, Xinlei Chen, J Zico Kolter, and Zhuang Liu. Massive activations in large language models. In First Conference on Language Modeling, 2024. URL https://openreview. net/forum?id=F7aAhfitX6. Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed Chi","arxiv_match":false},{"id":"LbGqNEnzW9","title":"LastOPD: Taming Collapse in Latent On-Policy Distillation","forum_url":"https://openreview.net/forum?id=LbGqNEnzW9","excerpt":"ahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: Hints for thin deep nets, 2015. URL https://arxiv.org/abs/ 1412.6550. Mingjie Sun, Xinlei Chen, J. Zico Kolter, and Zhuang Liu. Massive activations in large language models. In First Conference on Language Modeling, 2024. Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. Patient knowledge distillation for bert model compression. In Proceedings of the 2019 conference on emp","arxiv_match":false}],"id":"2402.17762","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":601},{"title":"PaperBench: Evaluating AI's Ability to Replicate AI Research","arxiv_id":"2504.01848","year":2025,"url":"https://arxiv.org/abs/2504.01848","citing_submissions":173,"explicit_arxiv_matches":104,"examples":[{"id":"mqy5nGDnra","title":"SIGHT: Compositional Skill Routing for Instance-Specific Rubric Generation across Heterogeneous Multimodal Tasks","forum_url":"https://openreview.net/forum?id=mqy5nGDnra","excerpt":" Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-LM: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053, 2019. Giulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, Jun Shern Chan, Leon Maksin, Rachel Dias, Evan Mays, Benjamin Kinsella, Wyatt Thompson, Johannes Heidecke, Amelia Glaese, and Tejal Patwardhan. PaperBench: Evaluating AI’s ability to replicate AI research. arXiv preprint arXiv:2504.01848, 2025. Juanxi Tian, Fengyuan Liu, Jiaming Han, Yilei Jiang, Yongliang Wu, Yesheng Liu, Haodong Li, ","arxiv_match":true},{"id":"Iq8XfoL1Iu","title":"CEO Arena: Evaluating Long-Horizon Multi-Agent Decision-Making in Competitive Markets","forum_url":"https://openreview.net/forum?id=Iq8XfoL1Iu","excerpt":"iulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, Jun Shern Chan, Leon Maksin, Rachel Dias, Evan Mays, Benjamin Kinsella, Wyatt Thompson, Johannes Heidecke, Amelia Glaese, and Tejal Patwardhan. PaperBench: Evaluating AI’s Ability to Replicate AI Research. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 56843–56873. PMLR, 2025. URL https://proceedings. mlr.press/v267/","arxiv_match":false},{"id":"BrrM1HlZaC","title":"ReproBridge: Building Reproducibility into ML Research Artifacts","forum_url":"https://openreview.net/forum?id=BrrM1HlZaC","excerpt":"iulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, Jun Shern Chan, Leon Maksin, Rachel Dias, Evan Mays, Benjamin Kinsella, Wyatt Thompson, Johannes Heidecke, Amelia Glaese, and Tejal Patwardhan. PaperBench: Evaluating AI’s ability to replicate AI research. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 56843–56873. PMLR, 2025. URL https://proceedings. mlr.press/v267/","arxiv_match":false}],"id":"2504.01848","metadata_source":"Cached arXiv API metadata","rank":602},{"title":"Show-o: One single transformer to unify multimodal understanding and generation","arxiv_id":"2408.12528","year":2024,"url":"https://arxiv.org/abs/2408.12528","citing_submissions":173,"explicit_arxiv_matches":69,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"sion language models with rgpo. In NeurIPS, 2025. Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. In ICLR, 2025. Yi Xin, Qi Qin, Siqi Luo, Kaiwen Zhu, Juncheng Yan, Yan Tai, Jiayi Lei, Yuewen Cao, Keqi Wang, Yibin Wang, et al. Lumina-dimoo: An omni diffusion large language model for multi-modal ","arxiv_match":false},{"id":"cbEkVPsGSJ","title":"DriveBeyond: Rethinking Group Advantage for Driving Policy Optimization","forum_url":"https://openreview.net/forum?id=cbEkVPsGSJ","excerpt":"ment learning. Machine Learning, 8:229–256, 1992. Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. In ICLR, volume 2025, pp. 28240–28264, 2025. Kaixin Xiong, Xiangyu Guo, Fang Li, Sixu Yan, Gangwei Xu, Lijun Zhou, Long Chen, Haiyang Sun, Bing Wang, Kun Ma, et al. ReCogDrive: A reinforced cognitiv","arxiv_match":false},{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"yu Chen, Haotian Tang, Dacheng Li, Yunhao Fang, Ligeng Zhu, Enze Xie, Hongxu Yin, Li Yi, et al. Vila-u: a unified foundation model integrating visual understanding and generation. arXiv preprint arXiv:2409.04429, 2024. Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528, 2024. Yilun Xu, Weili Nie, and Arash Vahdat. One-step diffusion models with f -divergence distribu","arxiv_match":true}],"id":"2408.12528","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":603},{"title":"Teaching large language models to self-debug","arxiv_id":"2304.05128","year":2023,"url":"https://arxiv.org/abs/2304.05128","citing_submissions":173,"explicit_arxiv_matches":47,"examples":[{"id":"9GDStxR3kP","title":"Inter-Trajectory Dependence Limits Answer Coverage in Local dLLM Repair","forum_url":"https://openreview.net/forum?id=9GDStxR3kP","excerpt":"on. Rfg: Test-time scaling for diffusion large language model reasoning with reward-free guidance. arXiv preprint arXiv:2509.25604, 2025b. Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug. In The Twelfth International Conference on Learning Representations, 2024. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you ha","arxiv_match":false},{"id":"d9ccCJn91k","title":"Learning the Loop, Not Just the Page: Execution-Grounded Self-Improvement for Web Generation","forum_url":"https://openreview.net/forum?id=d9ccCJn91k","excerpt":" Liu, and Shaolei Zhang. Webgrader: Training llms for web development with self-evolving programmatic grader. CoRR, abs/2608.06474, 2026. Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug. In ICLR. OpenReview.net, 2024. GLM-5 Team. GLM-5: From vibe coding to agentic engineering, 2026. URL https://arxiv. org/abs/2602.15763. Google DeepMind. Gemini 3.1 Pro Model Card. Technical report, ","arxiv_match":false},{"id":"CkuA2Se9vX","title":"MODEPrompt: Taxonomy-Guided Pareto Evolution for Prompt Optimization","forum_url":"https://openreview.net/forum?id=CkuA2Se9vX","excerpt":"ver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Forty-first International Conference on Machine Learning, ICML, pp. 6503–6518, 2024a. Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug. In The Twelfth International Conference on Learning Representations, ICLR, 2024b. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm","arxiv_match":false}],"id":"2304.05128","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":604},{"title":"Concrete problems in AI safety","arxiv_id":"1606.06565","year":2016,"url":"https://arxiv.org/abs/1606.06565","citing_submissions":172,"explicit_arxiv_matches":162,"examples":[{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":" Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mane. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565, 2016. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Ann","arxiv_match":true},{"id":"rT6sTWto1q","title":"VidForensics-M1: AI-Generated Video Detection and Forensics via Verifiable Meta-Detection Reinforcement Learning","forum_url":"https://openreview.net/forum?id=rT6sTWto1q","excerpt":" Alibaba Cloud. Wan 2.7: Image-to-video api. https://www.alibabacloud.com/help/ en/model-studio/image-to-video-general-api-reference, 2026. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. Concrete problems in ai safety, 2016. URL https://arxiv.org/abs/1606.06565. Jianfa Bai, Man Lin, Gang Cao, and Zijie Lou. Ai-generated video detection via spatial-temporal an","arxiv_match":true},{"id":"eXRKslhHmT","title":"WARD: Watching Actions via Latent-Space Reachability for Runtime Backdoor Defense in VLA Models","forum_url":"https://openreview.net/forum?id=eXRKslhHmT","excerpt":"ificial Intelligence, 2018. doi: 10.1609/AAAI.V32I1.11797. URL https://ojs.aaai. org/index.php/AAAI/article/view/11797. Aaron D. Ames, Xiangru Xu, Jessy W. Grizzle, and Paulo Tabuada. Control barrier function based quadratic programs for safety critical systems. IEEE Transactions on Automatic Control, 62(8): 3861–3876, 2017. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565, 2016. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fus","arxiv_match":true}],"id":"1606.06565","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":605},{"title":"Gated linear attention transformers with hardware-efficient training","arxiv_id":"2312.06635","year":2023,"url":"https://arxiv.org/abs/2312.06635","citing_submissions":172,"explicit_arxiv_matches":73,"examples":[{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"Enze Xie, Yingcong Chen, Yao Lu, Song Han, and Yukang Chen. LongLive: Real-Time Interactive Long Video Generation. In ICLR, 2026. Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, and Yoon Kim. Gated Linear Attention Transformers with Hardware-Efficient Training. In ICML, 2024. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, et al. CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer. In ICLR, 2025. Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, and Ch","arxiv_match":false},{"id":"OAX2QPoTJg","title":"HyLin: Modality-Aware Hybrid Attention for Efficient Long-Context Vision–Language Models","forum_url":"https://openreview.net/forum?id=OAX2QPoTJg","excerpt":"ttention via Semantic-Aware Permutation. In Advances in Neural Information Processing Systems, volume 38, pp. 96965–96991, 2025a. Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, and Yoon Kim. Gated Linear Attention Transformers with Hardware-Efficient Training. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 56501–56523. PMLR, 2024. Songlin Yang, Jan Kautz, and Ali Hatamiz","arxiv_match":false},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"t implementations of linear attention mechanism. GitHub repository, 2024. URL https://github.com/ fla-org/flash-linear-attention. Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, and Yoon Kim. Gated linear attention transformers with hardware-efficient training. In Proceedings of the 41st International Conference on Machine Learning, pp. 56501–56523, 2024a. Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, and Yoon Kim. Parallelizing linear transformers wit","arxiv_match":false}],"id":"2312.06635","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":606},{"title":"Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation","arxiv_id":"2602.12125","year":2026,"url":"https://arxiv.org/abs/2602.12125","citing_submissions":172,"explicit_arxiv_matches":159,"examples":[{"id":"AVKgs60Glj","title":"Flux-OPD: On-Policy Distillation with Evolving Contexts","forum_url":"https://openreview.net/forum?id=AVKgs60Glj","excerpt":", Chengen Huang, Chenxu Lv, et al. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025a. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. Self-distilled rlvr. arXiv preprint arXiv:2604.03128, 2026a. Wenkai Yang, Weijie Liu, Ruobing Xie, Kai Yang, Saiyong Yang, and Yankai Lin. Learning beyond teacher: Generalized on-policy distillation with reward extrapolation. arXiv preprint arXiv:2602.12125, 2026b. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, ","arxiv_match":true},{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":"eference guided fine-tuning, 2026. URL https://arxiv.org/abs/2603.01223. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. Self-distilled rlvr, 2026a. URL https://arxiv.org/ abs/2604.03128. Wenkai Yang, Weijie Liu, Ruobing Xie, Kai Yang, Saiyong Yang, and Yankai Lin. Learning beyond teacher: Generalized on-policy distillation with reward extrapolation, 2026b. URL https: //arxiv.org/abs/2602.12125. Tianzhu Ye, Li Dong, Xun Wu, Shaohan Huang, and Furu Wei. On-policy context distillation for langu","arxiv_match":true},{"id":"QKJ906InBo","title":"Teaching What to Look For: Privileged Perception Questions for Agentic On-Policy Distillation in Video Captioning","forum_url":"https://openreview.net/forum?id=QKJ906InBo","excerpt":"Unified reinforcement learning with verifiable rewards for dense image and video captioning. CoRR, abs/2606.09393, 2026a. Wenkai Yang, Weijie Liu, Ruobing Xie, Kai Yang, Saiyong Yang, and Yankai Lin. Learning beyond teacher: Generalized on-policy distillation with reward extrapolation. CoRR, abs/2602.12125, 2026b. Linli Yao, Yuancheng Wei, Yaojie Zhang, Lei Li, Xinlong Chen, Feifan Song, Ziyue Wang, Kun Ouyang, Yuanxin Liu, Lingpeng Kong, Qi Liu, Pengfei Wan, Kun Gai, Yuanxing Zha","arxiv_match":false}],"id":"2602.12125","metadata_source":"Cached arXiv API metadata","rank":607},{"title":"Learning interactive real-world simulators","arxiv_id":"2310.06114","year":2023,"url":"https://arxiv.org/abs/2310.06114","citing_submissions":172,"explicit_arxiv_matches":76,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"an Lin, Kang He, Yukang Feng, Xiaofeng Mao, Yuanyang Yin, Yongtao Ge, and Kaipeng Zhang. WorldMark: A unified benchmark suite for interactive video world models. arXiv preprint arXiv:2604.21686, 2026b. URL https://arxiv.org/abs/2604.21686. Sherry Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Leslie Kaelbling, Dale Schuurmans, and Pieter Abbeel. Learning Interactive Real-World Simulators. arXiv preprint arXiv:2310.06114, 2023. URL https://arxiv.org/abs/2310.06114. Yu Yang, Xuemeng Yang, Licheng Wen, Lingdong Kong, Xiaobin Hu, Dongyue Lu, Wei Chow, Xiyan Huang, ","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"ight in world-action models, 2026. URL https://arxiv.org/abs/2608.05903. Sherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson, Leslie Kaelbling, Dale Schuurmans, and Pieter Abbeel. Learning interactive real-world simulators. In International Conference on Learning Representations, 2024. Yi Yang, Zhihong Liu, Siqi Kou, Yiyang Chen, Yanzhe Hu, Jianbo Zhou, Boyuan Zhao, Zhijie Wei, Xiao Xia, Xueqi Li, Pengfei Liu, and Zhij","arxiv_match":false},{"id":"rtlzkXdJe9","title":"PhysiCalWorld: In-Context Physics Calibration for World Models","forum_url":"https://openreview.net/forum?id=rtlzkXdJe9","excerpt":"(CVPR), pp. 10793–10804. IEEE, 2025. 3 Qiyao Xue, Xiangyu Yin, Boyuan Yang, and Wei Gao. Phyt2v: Llm-guided iterative self-refinement for physics-grounded text-to-video generation. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18826–18836. IEEE, 2025. 3 Sherry Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Leslie Kaelbling, Dale Schuurmans, and Pieter Abbeel. Learning interactive real-world simulators. arXiv preprint arXiv:2310.06114, 2023. 1 Yuncong Yang, Zhengtao Han, Furkan Ozyurt, Zeyuan Yang, Han Yang, Junyi Cao, Haoyu Zhen, Y","arxiv_match":true}],"id":"2310.06114","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":608},{"title":"RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots","arxiv_id":"2406.02523","year":2024,"url":"https://arxiv.org/abs/2406.02523","citing_submissions":172,"explicit_arxiv_matches":104,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"ed data. IEEE Robotics and Automation Letters (RA-L), 7(4): 11205–11212, 2022. Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu. Robocasa: Large-scale simulation of everyday tasks for generalist robots. In Robotics: Science and Systems (RSS), 2024. Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine. Advantage-weighted regression: Simple and scalable off-policy reinforcement learning, 2019. ","arxiv_match":false},{"id":"9FaUYSuhHT","title":"SmokeBot: A Perception-to-Manipulation Framework and Simulation Platform for Robotic Manipulation in Smoke Scenes","forum_url":"https://openreview.net/forum?id=9FaUYSuhHT","excerpt":"g, volume 205 of Proceedings of Machine Learning Research, pages 80–93, 2023b. Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu. RoboCasa: Large-scale simulation of everyday tasks for generalist robots. In Proceedings of Robotics: Science and Systems, 2024. Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri, and Yuke Zhu. RoboCasa365: A largescale simulation framework for training and benchmark","arxiv_match":false},{"id":"Bd1yTJg9o3","title":"Iterative Self-Improvement with World-Expectation Optimization for World-Action Model","forum_url":"https://openreview.net/forum?id=Bd1yTJg9o3","excerpt":"reprint arXiv:2508.05635, 2025. Suvir Mirchandani, Suneel Belkhale, Joey Hejna, Evelyn Choi, Md Sazzad Islam, and Dorsa Sadigh. So you think you can scale up autonomous robot data collection?, 2024. URL https://arxiv.org/abs/2411.01813. Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu. Robocasa: Large-scale simulation of everyday tasks for generalist robots, 2024. URL https://arxiv.org/abs/2406.02523. NVIDIA GEAR Team. GR00T N1.5: An improved open foundation model for generalist humanoid robots. NV","arxiv_match":true}],"id":"2406.02523","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":609},{"title":"Towards stable test-time adaptation in dynamic wild world","arxiv_id":"2302.12400","year":2023,"url":"https://arxiv.org/abs/2302.12400","citing_submissions":172,"explicit_arxiv_matches":41,"examples":[{"id":"0LJ106OAjs","title":"Forecasting Before the Outcome: Catalogue Gated Prequential Cognitive Performance under Participant and Domain Shift","forum_url":"https://openreview.net/forum?id=0LJ106OAjs","excerpt":"03.0010. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan. Efficient test-time model adaptation without forgetting. In International conference on machine learning, pp. 16888–16905. PMLR, 2022. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan. Towards stable test-time adaptation in dynamic wild world. arXiv preprint arXiv:2302.12400, 2023. URL https://arxiv.org/abs/2302.12400. Shalini Pandey and George Karypis. A self-attentive model for knowledge tracing. arXiv preprint ar","arxiv_match":true},{"id":"GJfdHOgKGE","title":"Skip, Adapt, or Route? Certified Deployment of Test-Time Adaptive Rerankers under Limited Labels","forum_url":"https://openreview.net/forum?id=GJfdHOgKGE","excerpt":"06.01862. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan. Efficient test-time model adaptation without forgetting. arXiv preprint arXiv:2204.02610, 2022. URL https://arxiv.org/abs/2204.02610. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan. Towards stable test-time adaptation in dynamic wild world. arXiv preprint arXiv:2302.12400, 2023. URL https://arxiv.org/abs/2302.12400. Joseph J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System: Exp","arxiv_match":true},{"id":"AfJJPX455e","title":"Optimal Training-Time Scaling in Gradual Adaptation","forum_url":"https://openreview.net/forum?id=AfJJPX455e","excerpt":"Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 16888–16905. PMLR, 2022. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan. Towards stable test-time adaptation in dynamic wild world. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=g2YraF75Tj. German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter. C","arxiv_match":false}],"id":"2302.12400","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":610},{"title":"Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets","arxiv_id":"2504.02792","year":2025,"url":"https://arxiv.org/abs/2504.02792","citing_submissions":172,"explicit_arxiv_matches":89,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"o Castañeda, Fengyuan Hu, You Liang Tan, Letian Fu, Trevor Darrell, Furong Huang, Yuke Zhu, Danfei Xu, and Linxi Fan. Egoscale: Scaling dexterous manipulation with diverse egocentric human data, 2026. URL https://arxiv.org/abs/2602.16710. Chuning Zhu, Raymond Yu, Siyuan Feng, Benjamin Burchfiel, Paarth Shah, and Abhishek Gupta. Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets, 2025. URL https://arxiv.org/abs/2504.02792. Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Ste","arxiv_match":true},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"and Systems, 2026. doi: 10.15607/RSS.2026.XXII.015. URL https://www.roboticsproceedings.org/rss22/p015.html. Chuning Zhu, Raymond Yu, Siyuan Feng, Benjamin Burchfiel, Paarth Shah, and Abhishek Gupta. Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets. In Proceedings of Robotics: Science and Systems, 2025a. doi: 10.15607/RSS.2025.XXI.015. URL https://www.roboticsproceedings.org/rss21/p015.html. Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam ","arxiv_match":false},{"id":"xE1Rd5Luma","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","forum_url":"https://openreview.net/forum?id=xE1Rd5Luma","excerpt":" preprint arXiv:2403.09631, 2024. Ruijie Zheng, Jing Wang, Scott Reed, Johan Bjorck, Yu Fang, Fengyuan Hu, Joel Jang, Kaushil Kundalia, Zongyu Lin, Loic Magne, et al. Flare: Robot learning with implicit world modeling. arXiv preprint arXiv:2505.15659, 2025. Chuning Zhu, Raymond Yu, Siyuan Feng, Benjamin Burchfiel, Paarth Shah, and Abhishek Gupta. Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets. arXiv preprint arXiv:2504.02792, 2025a. Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam Cheang, and Tao Kong. Irasim: A finegr","arxiv_match":true}],"id":"2504.02792","metadata_source":"Cached arXiv API metadata","rank":611},{"title":"Length-controlled AlpacaEval: A simple way to debias automatic evaluators","arxiv_id":"2404.04475","year":2024,"url":"https://arxiv.org/abs/2404.04475","citing_submissions":171,"explicit_arxiv_matches":133,"examples":[{"id":"9Ou6wrydAL","title":"Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following","forum_url":"https://openreview.net/forum?id=9Ou6wrydAL","excerpt":" obfuscation. arXiv preprint arXiv:2503.11926, 2025. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. Journal of machine learning research, 25(70):1–53, 2024. Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B Hashimoto. Length-controlled alpacaeval: A simple way to debias automatic evaluators. arXiv preprint arXiv:2404.04475, 2024. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Z","arxiv_match":true},{"id":"JN4DdAjmHA","title":"Ideas Have Genomes: A Comprehensive Benchmark for Scientific Lineage Reasoning and Lineage-Grounded Idea Generation","forum_url":"https://openreview.net/forum?id=JN4DdAjmHA","excerpt":"Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, 2019. doi: 10.18653/v1/N19-1423. Alexey Dosovitskiy et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B. Hashimoto. Length-controlled AlpacaEval: A simple way to debias automatic evaluators. arXiv preprint arXiv:2404.04475, 2024. Arpad E. Elo. The Rating of Chessplayers, Past and Present. Arco, 1978. Santo Fortunato, Car","arxiv_match":true},{"id":"hbEJ10jEL7","title":"Gap-Adaptive Per-Objective Regularization against Differential Overoptimization in Multi-Objective Alignment","forum_url":"https://openreview.net/forum?id=hbEJ10jEL7","excerpt":"learning from human preferences, 2017. URL https://arxiv.org/abs/ 1706.03741. Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. Safe rlhf: Safe reinforcement learning from human feedback, 2023. URL https:// arxiv.org/abs/2310.12773. Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B. Hashimoto. Length-controlled alpacaeval: A simple way to debias automatic evaluators, 2024. URL https://arxiv.org/ abs/2404.04475. Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D’Amour, DJ Dvijotham, Adam Fi","arxiv_match":true}],"id":"2404.04475","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":612},{"title":"Phi-3 technical report: A highly capable language model locally on your phone","arxiv_id":"2404.14219","year":2024,"url":"https://arxiv.org/abs/2404.14219","citing_submissions":171,"explicit_arxiv_matches":150,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":" Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, et al. Phi-3 technical report: A highly capable language model locally on your phone, 2024. URL https://arxiv.org/ abs/2404.14219. Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael Mahoney, and Joseph Gonz","arxiv_match":true},{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"te, Michael Wyatt, Can Xu, Jiahang Xu, Weijian Xu, Sonali Yadav, Fan Yang, Ziyi Yang, Donghan Yu, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yunan Zhang, and Xiren Zhou. Phi-3 technical report: A highly capable language model locally on your phone. Technical Report MSR-TR-2024-12, Microsoft, August 2024. URL https://www.microsoft.com/en-us/research/publication/ phi-3-technical-report-a-highly-capable-language-model-locally-on-your-phone/. Rish","arxiv_match":false},{"id":"Khg0qee5LV","title":"OmniPersona: Persona-Embodied Agents for Multimodal Conversation Interaction","forum_url":"https://openreview.net/forum?id=Khg0qee5LV","excerpt":"ang Wang, Xin Wang, Yu Wang, Rachel Ward, Wen Wen, Philipp Witte, Haiping Wu, Xiaoxia Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, Weijian Xu, Jilong Xue, Sonali Yadav, Fan Yang, Jianwei Yang, Yifan Yang, Ziyi Yang, Donghan Yu, Lu Yuan, Chenruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, and Xiren Zhou. Phi-3 technical report: A highly capable language model locally on your phone, 2024. URL https://arxiv.org/abs/2404.14219. Vasu Agrawal, Akinniyi Akinyemi, Kathryn Alvero, Morteza Behrooz, Julia Buffalini, Fabio Maria Car","arxiv_match":true}],"id":"2404.14219","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":613},{"title":"Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models","arxiv_id":"2312.06585","year":2023,"url":"https://arxiv.org/abs/2312.06585","citing_submissions":170,"explicit_arxiv_matches":96,"examples":[{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"eej Mahajan, Laura A Culp, Lechao Xiao, Maxwell Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, and Noah Fiedel. Beyond human data: Scaling self-training for problem-solving with language models. Transactions on Machine Learning Research, 2024. ISSN 2835-8856. URL https://openreview.net/forum? id=lNAyUngGFK. Expert Certification. P. K. Srijith, Shrey Satapara, and Sarath Chandar. Continual l","arxiv_match":false},{"id":"UrDgYAs0MP","title":"When Good Verifiers Go Bad: Silent Negative Transfer in Verifier-Guided VLM Training","forum_url":"https://openreview.net/forum?id=UrDgYAs0MP","excerpt":"Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, and Noah Fiedel. Beyond human data: Scaling self-training for problem-solving with language models. Transactions on Machine Learning Research, 2024. Rickard Stureborg, Dimitris Alikaniotis, and Yoshi Suhara. Large language models are inconsistent and biased evaluators. arXiv preprint arXiv:2405.01","arxiv_match":false},{"id":"dHl5b1GjT0","title":"ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience","forum_url":"https://openreview.net/forum?id=dHl5b1GjT0","excerpt":"Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, and Noah Fiedel. Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Transactions on Machine Learning Research, 2024. Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, and Moritz Hardt. Test-Time Training with Self-Supervision for Generalization under ","arxiv_match":false}],"id":"2312.06585","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":614},{"title":"DanceGRPO: Unleashing GRPO on Visual Generation","arxiv_id":"2505.07818","year":2025,"url":"https://arxiv.org/abs/2505.07818","citing_submissions":170,"explicit_arxiv_matches":162,"examples":[{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"v:2306.09341, 2023. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36:15903–15935, 2023. Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al. Dancegrpo: Unleashing grpo on visual generation. arXiv preprint arXiv:2505.07818, 2025. Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin C","arxiv_match":true},{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":". In Advances in Neural Information Processing Systems, volume 38, 2025. Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al. Dancegrpo: Unleashing grpo on visual generation. arXiv, 2025. Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with ","arxiv_match":false},{"id":"tSpuyBaWWf","title":"Displacement Matching: One Objective for Reward Learning and Distillation in Few-Step Generators","forum_url":"https://openreview.net/forum?id=tSpuyBaWWf","excerpt":"ge synthesis, 2023. URL https://arxiv.org/abs/2306.09341. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: learning and evaluating human preferences for text-to-image generation. In NeurIPS, pp. 15903–15935, 2023. Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al. Dancegrpo: Unleashing grpo on visual generation. arXiv preprint arXiv:2505.07818, 2025. Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, and","arxiv_match":true}],"id":"2505.07818","metadata_source":"Cached arXiv API metadata","rank":615},{"title":"Latent consistency models: Synthesizing high-resolution images with few-step inference","arxiv_id":"2310.04378","year":2023,"url":"https://arxiv.org/abs/2310.04378","citing_submissions":170,"explicit_arxiv_matches":143,"examples":[{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":", Qiuyu Wang, Ka Leong Cheng, Jiapeng Zhu, Hengyuan Cao, Zhipeng Zhang, Xing Zhu, et al. Reward forcing: Efficient streaming video generation with rewarded distribution matching distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 34385–34397, 2026. Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378, 2023. Yue Ma, Kunyu Feng, Zhongyuan Hu, Xinyu Wang, Yucheng Wang, Mingzhe Zheng, Xuanhua He, Cheny","arxiv_match":true},{"id":"AivYEuElOk","title":"sCM-RNA: Preference-Aligned Consistency Models for RNA Inverse Folding","forum_url":"https://openreview.net/forum?id=AivYEuElOk","excerpt":"hristoph Flamm, Peter F Stadler, and Ivo L Hofacker. Viennarna package 2.0. Algorithms for molecular biology, 6 (1):26, 2011. Cheng Lu and Yang Song. Simplifying, stabilizing and scaling continuous-time consistency models. In The Thirteenth International Conference on Learning Representations, 2025. Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378, 2023. Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic mod","arxiv_match":true},{"id":"7x4AAlYBDM","title":"Stabilizing Multi-Step Diffusion Distillation with Student Perturbation Consistency","forum_url":"https://openreview.net/forum?id=7x4AAlYBDM","excerpt":"mework for general image editing. arXiv preprint arXiv:2504.17761, 2025. Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. ICLR, 2023. Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378, 2023. Yihong Luo, Tianyang Hu, Jiacheng Sun, Yujun Cai, and Jing Tang. Learning few-step diffusion","arxiv_match":true}],"id":"2310.04378","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":616},{"title":"Safety alignment should be made more than just a few tokens deep","arxiv_id":"2406.05946","year":2024,"url":"https://arxiv.org/abs/2406.05946","citing_submissions":170,"explicit_arxiv_matches":45,"examples":[{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":"nguage models. In Proceedings of the 9th Widening NLP Workshop, pp. 268–282, 2025. Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, and Peter Henderson. Safety alignment should be made more than just a few tokens deep. In International Conference on Learning Representations, volume 2025, pp. 54911–54941, 2025. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julia","arxiv_match":false},{"id":"ygrx8bN3MO","title":"Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning","forum_url":"https://openreview.net/forum?id=ygrx8bN3MO","excerpt":"LP (TrustNLP 2025), pp. 335–350, 2025. Weiwei Qi, Zefeng Wu, Zhilin Guo, Tianhang Zheng, Chaochao Lu, Liang He, Zhan Qin, and Kui Ren. Darwin: Evolving jailbreak adversary and guardrail for llm safety evaluation and protection. arXiv preprint arXiv:2607.19829, 2026. Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, and Peter Henderson. Safety alignment should be made more than just a few tokens deep. arXiv preprint arXiv:2406.05946, 2024a. Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagie","arxiv_match":true},{"id":"GGw37srpoS","title":"Safe Representation for Combined Image-Text Generation in Multi-Turn Conversations","forum_url":"https://openreview.net/forum?id=GGw37srpoS","excerpt":"cobo, Laurence Lindsey, Rutika Moharir, Gunnar Lund, Yang Xu, Navid Shiee, Jeffrey Bigham, et al. Vlsu: Mapping the limits of joint multimodal understanding for ai safety. In International Conference on Learning Representations, volume 2026, pp. 100354–100397, 2026. Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, and Peter Henderson. Safety alignment should be made more than just a few tokens deep. arXiv preprint arXiv:2406.05946, 2024. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Che","arxiv_match":true}],"id":"2406.05946","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":617},{"title":"VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models","arxiv_id":"2411.13503","year":2024,"url":"https://arxiv.org/abs/2411.13503","citing_submissions":170,"explicit_arxiv_matches":30,"examples":[{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":"ion and Pattern Recognition (CVPR), pp. 21807–21818. IEEE, 2024. Ziqi Huang, Fan Zhang, Xiaojie Xu, Yinan He, Jiashuo Yu, Ziyue Dong, Qianli Ma, Nattapol Chanpaisit, Chenyang Si, Yuming Jiang, et al. Vbench++: Comprehensive and versatile benchmark suite for video generative models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025c. Yicheng Ji, Zhizhou Zhong, Jun Zhang, Qin Yang, XiTai Jin, Ying Qin, Wenhan Luo, Shuiyang Mao, Wei Liu, and Huan Li. Forcing-kv","arxiv_match":false},{"id":"mwyLZ7hFam","title":"AffectVBench: Benchmarking Affective Faithfulness in Long-Form Text-to-Video Generation","forum_url":"https://openreview.net/forum?id=mwyLZ7hFam","excerpt":" Computer Vision and Pattern Recognition, pp. 21807–21818, 2024. Ziqi Huang, Fan Zhang, Xiaojie Xu, Yinan He, Jiashuo Yu, Ziyue Dong, Qianli Ma, Nattapol Chanpaisit, Chenyang Si, Yuming Jiang, et al. Vbench++: Comprehensive and versatile benchmark suite for video generative models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025. Xingxun Jiang, Yuan Zong, Wenming Zheng, Chuangao Tang, Wanchuang Xia, Cheng Lu, and Jiateng Liu. Dfew: A large-scale database f","arxiv_match":false},{"id":"ZFCw6a3QKH","title":"Not All Privilege Is Distillable: Calibrating Teacher Corrections for Few-Step Video Generation","forum_url":"https://openreview.net/forum?id=ZFCw6a3QKH","excerpt":"ion and Pattern Recognition (CVPR), pp. 21807–21818. IEEE, 2024. Ziqi Huang, Fan Zhang, Xiaojie Xu, Yinan He, Jiashuo Yu, Ziyue Dong, Qianli Ma, Nattapol Chanpaisit, Chenyang Si, Yuming Jiang, et al. Vbench++: Comprehensive and versatile benchmark suite for video generative models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025. Yuki Ichihara, Naoto Iwase, Mohammad Atif Quamar, and Junpei Komiyama. Privileged solutions or context-induced teacher behavior?","arxiv_match":false}],"id":"2411.13503","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":618},{"title":"WorldVLA: Towards Autoregressive Action World Model","arxiv_id":"2506.21539","year":2025,"url":"https://arxiv.org/abs/2506.21539","citing_submissions":170,"explicit_arxiv_matches":153,"examples":[{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"v preprint arXiv:2505.06111, 2025. Junhao Cai, Zetao Cai, Jiafei Cao, Yilun Chen, Zeyu He, Lei Jiang, Hang Li, Hengjie Li, Yang Li, Yufei Liu, et al. Internvla-a1: Unifying understanding, generation and action for robotic manipulation. arXiv preprint arXiv:2601.02456, 2026. Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, et al. Worldvla: Towards autoregressive action world model. arXiv preprint arXiv:2506.21539, 2025. Chilam Cheang, Sijin Chen, Zhongren Cui, Yingdong Hu, Liqun Huang, Tao Kong, Hang Li, Yifeng","arxiv_match":true},{"id":"xE1Rd5Luma","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","forum_url":"https://openreview.net/forum?id=xE1Rd5Luma","excerpt":"Ting Hu, Shoubhik Debnath, Ronghang Hu, Didac Suris Coll-Vinent, Chaitanya Ryali, Kalyan Vasudev Alwala, Haitham Khedr, Andrew Huang, et al. Sam 3: Segment anything with concepts. In International conference on learning representations, volume 2026, pp. 138846–138923, 2026. Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, et al. Worldvla: Towards autoregressive action world model. arXiv preprint arXiv:2506.21539, 2025. Jiayi Chen, Wenxuan Song, Pengxiang Ding, Ziyang Zhou, Han Zhao, Barrett Tang, Donglin Wang,","arxiv_match":true},{"id":"REE0TB9Fde","title":"PINT: A Unified Model with Shared Predictive Intent for Robot Manipulation","forum_url":"https://openreview.net/forum?id=REE0TB9Fde","excerpt":"aokang Yang, Ru Ying, Ran Zheng, and Yao Mu. AHA-WAM: Asynchronous horizon-adaptive world-action modeling with observation-guided context routing. arXiv preprint arXiv:2606.09811, 2026. URL https://arxiv.org/abs/2606.09811. Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, et al. WorldVLA: Towards autoregressive action world model. arXiv preprint arXiv:2506.21539, 2025. URL https://arxiv.org/abs/2506.21539. Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jia","arxiv_match":true}],"id":"2506.21539","metadata_source":"Cached arXiv API metadata","rank":619},{"title":"Evaluating real-world robot manipulation policies in simulation","arxiv_id":"2405.05941","year":2024,"url":"https://arxiv.org/abs/2405.05941","citing_submissions":169,"explicit_arxiv_matches":77,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":" Li, Kyle Hsu, Jiayuan Gu, Oier Mees, Karl Pertsch, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, and Ted Xiao. Evaluating real-world robot manipulation policies in simulation. In 8th Annual Conference on Robot Learning, 2024. URL https://openreview.net/forum?id=LZh48DTg71. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for ","arxiv_match":false},{"id":"fKq8qGzBwD","title":"Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning","forum_url":"https://openreview.net/forum?id=fKq8qGzBwD","excerpt":"ical modeling using the t distribution. Journal of the American Statistical Association, 84(408):881–896, 1989. ISSN 01621459, 1537274X. URL http://www.jstor.org/stable/2290063. Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, and Ted Xiao. Evaluating real-world robot manipulation policies in simulation. arXiv preprint arXiv:2405.05941, 2024. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching","arxiv_match":true},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"onference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 19730–19742. PMLR, 23–29 Jul 2023a. URL https://proceedings.mlr.press/v202/li23q.html. Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, and Ted Xiao. Evaluating real-world robot manipulation policies in simulation. arXiv preprint arXiv:2405.05941, 2024. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating obje","arxiv_match":true}],"id":"2405.05941","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":620},{"title":"LTX-Video: Realtime Video Latent Diffusion","arxiv_id":"2501.00103","year":2024,"url":"https://arxiv.org/abs/2501.00103","citing_submissions":169,"explicit_arxiv_matches":160,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"amework for evaluating text-to-image alignment. In NeurIPS, 2023. Zipeng Guo, Lichen Ma, Yu He, Xiaolong Fu, Jingling Fu, Junshi Huang, and Yan Li. PixelU: A U-shaped transformer for efficient end-to-end pixel diffusion. arXiv preprint arXiv:2606.27760, 2026. Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, et al. Ltx-video: Realtime video latent diffusion. arXiv preprint arXiv:2501.00103, 2024. Yu He, Lichen Ma, Zipeng Guo, Xinyuan Shan, Jingling Fu, Dong Chen, Junshi Huang, and Yan Li","arxiv_match":true},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"IPS, 2014. URL https://proceedings.neurips.cc/paper_files/paper/2014/ hash/f033ed80deb0234979a61f95710dbe25-Abstract.html. Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, Poriya Panet, Sapir Weissbuch, Victor Kulikov, Yaki Bitterman, Zeev Melumian, and Ofir Bibi. Ltx-video: Realtime video latent diffusion. arXiv preprint arXiv:2501.00103, 2024. URL https://arxiv.org/abs/2501.00103. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans tr","arxiv_match":true},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"ning. Transactions on Machine Learning Research, 2023. Junyao Gao, Jiaxing Li, Wenran Liu, Yanhong Zeng, Fei Shen, Kai Chen, Yanan Sun, and Cairong Zhao. Charactershot: Controllable and consistent 4d character animation. arXiv preprint arXiv:2508.07409, 2025. Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, et al. Ltx-video: Realtime video latent diffusion. arXiv preprint arXiv:2501.00103, 2024. Xianglong He, Chunli Peng, Zexiang Liu, Boyang Wang, Yifan Zhang, Qi Cui, Fei Kang, Biao Jia","arxiv_match":true}],"id":"2501.00103","metadata_source":"Cached arXiv API metadata","rank":621},{"title":"Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?","arxiv_id":"2403.14624","year":2024,"url":"https://arxiv.org/abs/2403.14624","citing_submissions":169,"explicit_arxiv_matches":34,"examples":[{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":" Sun, Runze Liu, Che Jiang, Yuchen Fan, Kai Tian, Guoli Jia, Pengfei Li, et al. A survey of reinforcement learning for large reasoning models. arXiv preprint arXiv:2509.08827, 2025a. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, et al. MathVerse: Does your multi-modal LLM truly see the diagrams in visual math problems? arXiv preprint arXiv:2403.14624, 2024. URL https: //arxiv.org/abs/2403.14624. Yi-Fan Zhang, Huanyu Zhang, Haochen Tian, Chaoyou Fu, Shuangqing Zhang, Junfei Wu, Feng Li, Kun Wa","arxiv_match":true},{"id":"UcFbcNhyQO","title":"PhysAlign: A Benchmark for Evidence-Grounded Role Alignment in Multimodal Physics Reasoning","forum_url":"https://openreview.net/forum?id=UcFbcNhyQO","excerpt":" and what to do about it?, 2023. URL https://arxiv.org/abs/2210.01936. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pp. 169–186. Springer, 2024. Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, and Jun Li","arxiv_match":false},{"id":"aIio1AlTq2","title":"Latent Noise Mask Eliminates Visual Redundancy of Multimodal Large Language Models","forum_url":"https://openreview.net/forum?id=aIio1AlTq2","excerpt":"ial of vision-language models. arXiv preprint arXiv:2511.11007, 2025b. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pp. 169–186. Springer, 2024. Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in langua","arxiv_match":false}],"id":"2403.14624","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":622},{"title":"TextGrad: Automatic “differentiation” via text","arxiv_id":"2406.07496","year":2024,"url":"https://arxiv.org/abs/2406.07496","citing_submissions":169,"explicit_arxiv_matches":146,"examples":[{"id":"ROquSvIhjs","title":"Inverse Garment Design with Visual Agents","forum_url":"https://openreview.net/forum?id=ROquSvIhjs","excerpt":"the Association for Computational Linguistics, 2024. Shaofeng Yin, Jiaxin Ge, Zora Zhiruo Wang, Chenyang Wang, Xiuyu Li, Michael J. Black, Trevor Darrell, Angjoo Kanazawa, and Haiwen Feng. Vision-as-inverse-graphics agent via interleaved multimodal reasoning. arXiv preprint arXiv:2601.11109, 2026. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. Textgrad: Automatic ”differentiation” via text. arXiv preprint arXiv:2406.07496, 2024. Cheng Zhang, Yuanhao Wang, Francisco Vicente Carrasco, Chenglei Wu, Jinlong Yang, Thabo Beel","arxiv_match":true},{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"Huan Sun, Yu Su, and Wenhu Chen. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9556–9567, June 2024. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. TextGrad: Automatic Differentiation via Text. arXiv preprint arXiv:2406.07496, 2024. URL https://arxiv.org/abs/2406.07496. Eric Zelikman, Eliana Lorch, Lester Mackey, and Adam Tauman Kalai. Self-Taught Optimizer (STOP): R","arxiv_match":true},{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":"ents. Advances in Neural Information Processing Systems, 38:17577–17604, 2026. Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei Gao, Qi Dai, Bei Liu, et al. SkillOpt: Executive strategy for self-evolving agent skills. arXiv preprint arXiv:2605.23904, 2026. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. TextGrad: Automatic “differentiation” via text. arXiv preprint arXiv:2406.07496, 2024. Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, J","arxiv_match":true}],"id":"2406.07496","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":623},{"title":"A survey of large language models","arxiv_id":"2303.18223","year":2023,"url":"https://arxiv.org/abs/2303.18223","citing_submissions":168,"explicit_arxiv_matches":71,"examples":[{"id":"Y15G7fdvPv","title":"S$^2$Aligner: Pair-Efficient Transferable Pre-Training for Sparse Text-Attributed Graphs","forum_url":"https://openreview.net/forum?id=Y15G7fdvPv","excerpt":"al language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp. 3982–3992, 2019. Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, and Chao Huang. A survey of large language models for graphs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 6616–6626, 2024. Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. Distribu","arxiv_match":false},{"id":"tifLEoFKw2","title":"Black-Box Membership Inference Attack for Vision-Language Models Using Self-Memory Probing","forum_url":"https://openreview.net/forum?id=tifLEoFKw2","excerpt":"nce, 46(8):5625–5644, 2024b. Kai Zhang, Rong Zhou, Eashan Adhikarla, Zhiling Yan, Yixin Liu, Jun Yu, Zhengliang Liu, Xun Chen, Brian D Davison, Hui Ren, et al. A generalist vision–language foundation model for diverse biomedical tasks. Nature medicine, 30(11):3129–3141, 2024c. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 1(2):1–124, 2023. Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: ","arxiv_match":true},{"id":"KvMIeBWgq8","title":"Ewen: A Covariate-Aware Foundation Model for EEG Understanding","forum_url":"https://openreview.net/forum?id=KvMIeBWgq8","excerpt":"earning Representations, 2020. Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Animashree Anandkumar, and Yuandong Tian. GaLore: Memory-efficient LLM training by gradient low-rank projection. In Proceedings of the 41st International Conference on Machine Learning, 2024. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 1(2):1–124, 2023. Junhao Zheng, Xidi Cai, Shengjie Qiu, and Qianli Ma. Spurious forgetting in cont","arxiv_match":true}],"id":"2303.18223","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":624},{"title":"ELLA: Equip diffusion models with LLM for enhanced semantic alignment","arxiv_id":"2403.05135","year":2024,"url":"https://arxiv.org/abs/2403.05135","citing_submissions":168,"explicit_arxiv_matches":150,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"lassifier-free diffusion guidance. In NeurIPS Workshop, 2021. Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020. Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. Simple diffusion: End-to-end diffusion for high resolution images. In ICML, pp. 13213–13232, 2023. Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with llm for enhanced semantic alignment. arXiv preprint arXiv:2403.05135, 2024. Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, and Tim Salimans. ","arxiv_match":true},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"rocessing, pp. 7514–7528, 2021. Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, and John A Bateman. Ai2d-rst: A multimodal corpus of 1000 primary school science diagrams. Language Resources and Evaluation, 55:661–688, 2021. Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with llm for enhanced semantic alignment. arXiv preprint arXiv:2403.05135, 2024. Yucheng Hu, Jianke Zhang, Yuanfei Luo, Yanjiang Guo, Xiaoyu Chen, Xinshu Sun, Kun Feng, Qing","arxiv_match":true},{"id":"7x4AAlYBDM","title":"Stabilizing Multi-Step Diffusion Distillation with Student Perturbation Consistency","forum_url":"https://openreview.net/forum?id=7x4AAlYBDM","excerpt":", and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In ICLR, 2022. URL https://openreview.net/forum?id=nZeVKeeFYf9. Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with llm for enhanced semantic alignment, 2024. Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, and Stefano Ermon. Consistency trajectory models: Learning probability f","arxiv_match":false}],"id":"2403.05135","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":625},{"title":"MathArena: Evaluating LLMs on Uncontaminated Math Competitions","arxiv_id":"2505.23281","year":2025,"url":"https://arxiv.org/abs/2505.23281","citing_submissions":168,"explicit_arxiv_matches":49,"examples":[{"id":"V7gMQjOqcP","title":"Learning from Single-Feedback Deployment Experience","forum_url":"https://openreview.net/forum?id=V7gMQjOqcP","excerpt":"Fei Bai, Huatong Song, Shuang Sun, Daixuan Cheng, Yike Yang, Chuan Hao, Renyuan Li, Feng Chang, Yuan Wei, Ran Tao, Bryan Dai, Jian Yang, Wayne Xin Zhao, and Ji-Rong Wen. ClawGym: A scalable framework for building effective Claw agents, 2026. URL https://arxiv.org/ abs/2604.26904. Mislav Balunović, Jasper Dekoninck, Ivo Petrov, Nikola Jovanović, and Martin Vechev. MathArena: Evaluating LLMs on uncontaminated math competitions, 2025. URL https://arxiv.org/ abs/2505.23281. Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celin","arxiv_match":true},{"id":"mczw113vfG","title":"On-Policy Parameter Update Direction Underlies Generalization in LLM Post-Training","forum_url":"https://openreview.net/forum?id=mczw113vfG","excerpt":" and Edoardo M Ponti. Scaling sparse fine-tuning to large language models. arXiv preprint arXiv:2401.16405, 2024. Mislav Balunovic, Jasper Dekoninck, Ivo Petrov, Nikola Jovanović, and Martin Vechev. Matharena: Evaluating llms on uncontaminated math competitions. Advances in Neural Information Processing Systems, 38, 2026. Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about physical commonsense in natural language. In Proceedi","arxiv_match":false},{"id":"Mm5OLOPLT0","title":"Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization","forum_url":"https://openreview.net/forum?id=Mm5OLOPLT0","excerpt":"e Association for Computational Linguistics: ACL 2022, pp. 700–710, 2022. doi: 10.18653/v1/2022.findings-acl.58. Mislav Balunović, Jasper Dekoninck, Ivo Petrov, Nikola Jovanović, and Martin Vechev. MathArena: Evaluating LLMs on uncontaminated math competitions. https://matharena.ai/, 2025. Nicolas Boizard, Kevin El Haddad, Céline Hudelot, and Pierre Colombo. Towards cross-tokenizer distillation: the universal logit distillation loss for llms. Transactions ","arxiv_match":false}],"id":"2505.23281","metadata_source":"Cached arXiv API metadata","rank":626},{"title":"AgentPoison: Red-teaming LLM agents via poisoning memory or knowledge bases","arxiv_id":"2407.12784","year":2024,"url":"https://arxiv.org/abs/2407.12784","citing_submissions":167,"explicit_arxiv_matches":33,"examples":[{"id":"4qddTfOQac","title":"TRACE-MAS: Provenance Guided Selective Recovery for Multi-agent Incidents","forum_url":"https://openreview.net/forum?id=4qddTfOQac","excerpt":"de, and Akshay Nambi. Exposing weak links in multi-agent systems under adversarial prompting, 2025. URL https://arxiv.org/abs/2511.10949. Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, and Bo Li. Agentpoison: Red-teaming LLM agents via poisoning memory or knowledge bases. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry ","arxiv_match":false},{"id":"EUiwYKQQVt","title":"When External Memory Becomes Agent State: Recorded Lineage and Controlled Reuse in LLM Agents","forum_url":"https://openreview.net/forum?id=EUiwYKQQVt","excerpt":"entao Fu, Jiajun Zhou, Shanqing Yu, and Qi Xuan. MemSecBench: Tracking agent memory poisoning from persistence to consequence and repair, 2026. URL https://arxiv.org/abs/2607.27080. Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, and Bo Li. AgentPoison: Red-teaming LLM agents via poisoning memory or knowledge bases. In Advances in Neural Information Processing Systems, volume 37, pp. 130185–130213, 2024. doi: 10.52202/079017-4136. URL https://arxiv.org/abs/2407.12784. Pritam Dash, Tongyu Ge, Aditi Jain, Tanmay Shah, and Zhiwei Shang. From untrusted input to trusted","arxiv_match":true},{"id":"FCnWjmOlAg","title":"PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust","forum_url":"https://openreview.net/forum?id=FCnWjmOlAg","excerpt":"thik Narasimhan. τ 2 -bench: Evaluating conversational agents in a dual-control environment, 2025. URL https://arxiv.org/abs/ 2506.07982. Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, and Bo Li. AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases. arXiv, 2024. URL https://doi. org/10.48550/arXiv.2407.12784. Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable lon","arxiv_match":false}],"id":"2407.12784","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":627},{"title":"OpenThoughts: Data Recipes for Reasoning Models","arxiv_id":"2506.04178","year":2025,"url":"https://arxiv.org/abs/2506.04178","citing_submissions":167,"explicit_arxiv_matches":101,"examples":[{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":"ya Grover, Kai-Wei Chang, Vaishaal Shankar, Aaron Gokaslan, Mike A Merrill, Tatsunori Hashimoto, Yejin Choi, Jenia Jitsev, Reinhard Heckel, Maheswaran Sathiamoorthy, Alex Dimakis, and Ludwig Schmidt. Openthoughts: Data recipes for reasoning models. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=7xjoTuaNmN. Zihao Han, Tiangang Zhang, Huaibin Wang, and Yilun Sun. Adaptive teacher","arxiv_match":false},{"id":"KbAV5KY9YW","title":"Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue","forum_url":"https://openreview.net/forum?id=KbAV5KY9YW","excerpt":"ivek Ramanujan, Jon Saad-Falcon, Jeffrey Li, Achal Dave, Alon Albalak, Kushal Arora, Blake Wulfe, Chinmay Hegde, Greg Durrett, Sewoong Oh, Mohit Bansal, Saadia Gabriel, Aditya Grover, Kai-Wei Chang, Vaishaal Shankar, Aaron Gokaslan, Mike A. Merrill, Tatsunori Hashimoto, Yejin Choi, Jenia Jitsev, Reinhard Heckel, Maheswaran Sathiamoorthy, Alexandros G. Dimakis, and Ludwig Schmidt. Openthoughts: Data recipes for reasoning models, 2025. URL https://arxiv.org/abs/2506.04178. Téo Guichoux, Théodor Lemerle, Shivam Mehta, Jonas Beskow, Gustav Eje Henter, Laure Soulier, Cat","arxiv_match":true},{"id":"dvyMwimtP5","title":"Beyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training","forum_url":"https://openreview.net/forum?id=dvyMwimtP5","excerpt":"Nezhurina, Jean Mercat, Trung Vu, Zayne Sprague, Ashima Suvarna, Benjamin Feuer, Liangyu Chen, Zaid Khan, Eric Frankel, Sachin Grover, Caroline Choi, Niklas Muennighoff, Shiye Su, Wanjia Zhao, et al. Openthoughts: Data recipes for reasoning models, 2025. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, and Maosong Sun. Olympiadbench: A cha","arxiv_match":false}],"id":"2506.04178","metadata_source":"Cached arXiv API metadata","rank":628},{"title":"ProcessBench: Identifying process errors in mathematical reasoning","arxiv_id":"2412.06559","year":2024,"url":"https://arxiv.org/abs/2412.06559","citing_submissions":167,"explicit_arxiv_matches":52,"examples":[{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"ning Representations (ICLR), 2026b. https://openreview.net/forum?id=IdF6JqXWzx. Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. ProcessBench: Identifying process errors in mathematical reasoning. In Annual Meeting of the Association for Computational Linguistics (ACL), 2025. https://aclanthology.org/2025.acl-long.50/. Han Zhou, Xingchen Wan, Ruoxi Sun, Hamid Palangi, Shariq Iqbal, Ivan Vulic","arxiv_match":false},{"id":"K39aqWW934","title":"When Does LLM Reasoning Compositionality Break and Recover?","forum_url":"https://openreview.net/forum?id=K39aqWW934","excerpt":"guage model hallucinations can snowball. arXiv preprint arXiv:2305.13534, 2023. Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Processbench: Identifying process errors in mathematical reasoning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1009–1024, 2025a. Congming Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kang","arxiv_match":false},{"id":"V32KUCuNhr","title":"Do LLMs Know Where Constraints Bite? Towards Fine-Grained Reward Signals for Instruction Following","forum_url":"https://openreview.net/forum?id=V32KUCuNhr","excerpt":"bs/2602.01791. Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, Jingren Zhou, and Junyang Lin. Group sequence policy optimization, 2025a. URL https://arxiv.org/abs/2507.18071. Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Processbench: Identifying process errors in mathematical reasoning, 2025b. URL https://arxiv.org/abs/2412.06559. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and","arxiv_match":true}],"id":"2412.06559","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":629},{"title":"Scaling Latent Reasoning via Looped Language Models","arxiv_id":"2510.25741","year":2025,"url":"https://arxiv.org/abs/2510.25741","citing_submissions":167,"explicit_arxiv_matches":161,"examples":[{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":". Rui-Jie Zhu, Zixuan Wang, Kai Hua, Tianyu Zhang, Ziniu Li, Haoran Que, Boyi Wei, Zixin Wen, Fan Yin, He Xing, Lu Li, Jiajun Shi, Kaijing Ma, Shanda Li, Taylor Kergan, Andrew Smith, Xingwei Qu, Mude Hui, Bohong Wu, Qiyang Min, Hongzhi Huang, Xun Zhou, Wei Ye, Jiaheng Liu, Jian Yang, Yunfeng Shi, Chenghua Lin, Enduo Zhao, Tianle Cai, Ge Zhang, Wenhao Huang, Yoshua Bengio, and Jason Eshraghian. Scaling latent reasoning via looped language models. arXiv preprint arXiv:2510.25741, 2025. A T RAINING I MPLEMENTATION D ETAILS A.1 M ODEL A RCHITECTURE Our models build on a common Q","arxiv_match":true},{"id":"w9AtxUbfFB","title":"Loop-Distill: Refining Information Flow in Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=w9AtxUbfFB","excerpt":"utational Linguistics (Volume 1: Long Papers), pp. 11425–11460, 2026. Rui-Jie Zhu, Zixuan Wang, Kai Hua, Tianyu Zhang, Ziniu Li, Haoran Que, Boyi Wei, Zixin Wen, Fan Yin, He Xing, et al. Scaling latent reasoning via looped language models. arXiv preprint arXiv:2510.25741, 2025b. Rui-Jie Zhu, Zixuan Wang, Kai Hua, Tianyu Zhang, Ziniu Li, Haoran Que, Boyi Wei, Zixin Wen, Fan Yin, He Xing, et al. Scaling latent reasoning via looped language models. arXiv preprint arXiv:2510.25741, 2025c. Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur","arxiv_match":true},{"id":"m61N49mzzc","title":"Improving Test-Time Scaling with Adaptive Looped Transformers","forum_url":"https://openreview.net/forum?id=m61N49mzzc","excerpt":"Ziniu Li, Haoran Que, Boyi Wei, Zixin Wen, Fan Yin, He Xing, Lu Li, Jiajun Shi, Kaijing Ma, Shanda Li, Taylor Kergan, Andrew Smith, Xingwei Qu, Mude Hui, Bohong Wu, Qiyang Min, Hongzhi Huang, Xun Zhou, Wei Ye, Jiaheng Liu, Jian Yang, Yunfeng Shi, Chenghua Lin, Enduo Zhao, Tianle Cai, Ge Zhang, Wenhao Huang, Yoshua Bengio, and Jason Eshraghian. Scaling Latent Reasoning via Looped Language Models. arXiv preprint arXiv:2510.25741, 2025. URL https://arxiv.org/abs/2510.25741. A ","arxiv_match":true}],"id":"2510.25741","metadata_source":"Cached arXiv API metadata","rank":630},{"title":"SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing","arxiv_id":"1807.03819","year":2018,"url":"https://arxiv.org/abs/1807.03819","citing_submissions":167,"explicit_arxiv_matches":122,"examples":[{"id":"sFUbqvhx3m","title":"SyntaxBPE: Learning Which Code Boundaries to Cross","forum_url":"https://openreview.net/forum?id=sFUbqvhx3m","excerpt":"al Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 66–75, 2018. doi: 10.18653/v1/P18-1007. URL https://aclanthology.org/P18-1007/. Taku Kudo and John Richardson. SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 66–71, 2018. doi: 10.18653/v1/D18-2012. URL https://aclanthology.org/D18-2012/. ","arxiv_match":false},{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":" URL https://arxiv.org/abs/2605.25230. Róbert Csordás, Christopher D. Manning, and Christopher Potts. Do language models use their depth efficiently? In Advances in Neural Information Processing Systems (NeurIPS), 2025. URL https://arxiv.org/abs/2505.13898. Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser. Universal transformers. In International Conference on Learning Representations (ICLR), 2019. URL https://arxiv.org/abs/1807.03819. Andreas Efstathiou and Aishwarya Balwani. Recursive reasoning as attractor landscape search: Mecha","arxiv_match":true},{"id":"rEbx1TSx9H","title":"Pruned CTC for Memory-Efficient Large-Vocabulary ASR Training","forum_url":"https://openreview.net/forum?id=rEbx1TSx9H","excerpt":"iyong Guo, Wei Kang, Long Lin, Mingshuang Luo, Zengwei Yao, and Daniel Povey. Pruned RNN-T for fast, memory-efficient ASR training. In Proc. Interspeech, Incheon, 2022. Taku Kudo and John Richardson. SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In Proc. EMNLP: System Demonstrations, Brussels, 2018. Rao Ma, Tongzhou Chen, Kartik Audhkhasi, and Bhuvana Ramabhadran. LegoSLM: Connecting LLM with speech encoder using CTC posteriors. In Findings","arxiv_match":false}],"id":"1807.03819","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":631},{"title":"Finetuned language models are zero-shot learners","arxiv_id":"2109.01652","year":2021,"url":"https://arxiv.org/abs/2109.01652","citing_submissions":166,"explicit_arxiv_matches":82,"examples":[{"id":"TA0thWBRuQ","title":"ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence","forum_url":"https://openreview.net/forum?id=TA0thWBRuQ","excerpt":" IEEE/CVF International Conference on Computer Vision, pp. 24170–24181, 2025. Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. Finetuned language models are zero-shot learners. In International Conference on Learning Representations, 2022. Diankun Wu, Fangfu Liu, Yi-Hsin Hung, and Yueqi Duan. Spatial-MLLM: Boosting MLLM capabilities in visual-based spatial intelligence. In","arxiv_match":false},{"id":"6N8dYXmsNk","title":"Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=6N8dYXmsNk","excerpt":"ng multimodal mathematical reasoning with MATH-vision dataset. In NeurIPS, 2024a. Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. Finetuned language models are zero-shot learners. In ICLR, 2022a. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. Chain of thought prompting elicits reasoning in large language mo","arxiv_match":false},{"id":"GGw37srpoS","title":"Safe Representation for Combined Image-Text Generation in Multi-Turn Conversations","forum_url":"https://openreview.net/forum?id=GGw37srpoS","excerpt":"r Computational Linguistics: NAACL 2025, pp. 3563–3605, 2025c. Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024c. Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652, 2021. Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda ","arxiv_match":true}],"id":"2109.01652","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":632},{"title":"FitNets: Hints for thin deep nets","arxiv_id":"1412.6550","year":2014,"url":"https://arxiv.org/abs/1412.6550","citing_submissions":166,"explicit_arxiv_matches":82,"examples":[{"id":"QiYD616GNr","title":"Fast-ZSAD: Patch-wise Adaptive Inference for Efficient Zero-Shot Anomaly Detection","forum_url":"https://openreview.net/forum?id=QiYD616GNr","excerpt":"ual models from natural language supervision, 2021. URL https://arxiv.org/abs/2103.00020. Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh. Dynamicvit: Efficient vision transformers with dynamic token sparsification, 2021. URL https://arxiv. org/abs/2106.02034. Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: Hints for thin deep nets, 2015. URL https://arxiv.org/abs/ 1412.6550. Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vas","arxiv_match":true},{"id":"MpNPoDTf3B","title":"Semantic Fields Are Interfaces: Consumer-Typed Functional Migration for Representation Autoencoders","forum_url":"https://openreview.net/forum?id=MpNPoDTf3B","excerpt":" and Björn Ommer. Highresolution image synthesis with latent diffusion models. In CVPR, 2022. Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: Hints for thin deep nets. In ICLR, 2015. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. Im","arxiv_match":false},{"id":"J6s4pH2a2M","title":"LLM2llm: Parameter-Efficient Adaptation via Transferred Experts from MoE LLMs","forum_url":"https://openreview.net/forum?id=J6s4pH2a2M","excerpt":"erence on Health, Inference, and Learning, volume 174 of Proceedings of Machine Learning Research, pp. 248–260. PMLR, 07–08 Apr 2022. URL https://proceedings.mlr.press/ v174/pal22a.html. Qwen Team. Qwen3.5: Towards native multimodal agents, February 2026. URL https://qwen. ai/blog?id=qwen3.5. Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: hints for thin deep nets. arxiv preprint. arXiv preprint arXiv:1412.6550, 2014. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An advers","arxiv_match":true}],"id":"1412.6550","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":633},{"title":"MiMo-V2-Flash Technical Report","arxiv_id":"2601.02780","year":2026,"url":"https://arxiv.org/abs/2601.02780","citing_submissions":166,"explicit_arxiv_matches":156,"examples":[{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":", Shengfan Wang, Shicheng Li, Shuhao Gu, Shuhuai Ren, Sirui Deng, Tao Guo, Tianyang Lu, Weiji Zhuang, Weikang Zhang, Weimin Xiong, Wenshan Huang, Wenyu Yang, Xin Zhang, Xing Yong, Xu Wang, Xueyang Xie, Yilin Jiang, Yixin Yang, Yongzhe He, Yu Tu, Yuanliang Dong, Yuchen Liu, Yue Ma, Yue Yu, Yuxing Xiang, Zhaojun Huang, Zhenru Lin, Zhipeng Xu, Zhiyang Chen, Zhonghua Deng, Zihan Zhang, and Zihao Yue. Mimo-v2-flash technical report, 2026. URL https://arxiv.org/abs/2601.02780. DeepSeek-AI, Anyi Xu, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, ","arxiv_match":true},{"id":"4Dg0z4hCoY","title":"Scaling Properties of Capability Transfer in Same-Family On-Policy Distillation","forum_url":"https://openreview.net/forum?id=4Dg0z4hCoY","excerpt":"o Liu, Muling Wu, et al. Reward hacking in the era of large models: Mechanisms, emergent misalignment, challenges. arXiv preprint arXiv:2604.13602, 2026b. Lilian Weng. Scaling laws, carefully. lilianweng.github.io, June 2026. URL https://lilianweng. github.io/posts/2026-06-24-scaling-laws/. Bangjun Xiao, Bingquan Xia, Bo Yang, Bofei Gao, Bowen Shen, Chen Zhang, Chenhong He, Chiheng Lou, Fuli Luo, Gang Wang, et al. Mimo-v2-flash technical report. arXiv preprint arXiv:2601.02780, 2026. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao,","arxiv_match":true},{"id":"l6TjLrpsyc","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=l6TjLrpsyc","excerpt":" Shengfan Wang, Shicheng Li, Shuhao Gu, Shuhuai Ren, Sirui Deng, Tao Guo, Tianyang Lu, Weiji Zhuang, Weikang Zhang, Weimin Xiong, Wenshan Huang, Wenyu Yang, Xin Zhang, Xing Yong, Xu Wang, Xueyang Xie, Yilin Jiang, Yixin Yang, Yongzhe He, Yu Tu, Yuanliang Dong, Yuchen Liu, Yue Ma, Yue Yu, Yuxing Xiang, Zhaojun Huang, Zhenru Lin, Zhipeng Xu, Zhiyang Chen, Zhonghua Deng, Zihan Zhang, and Zihao Yue. Mimo-v2-flash technical report, 2026a. URL https://arxiv.org/abs/2601.02780. Kimi Team, Yifan Bai, Yiping Bao, Y Charles, Cheng Chen, Guanduo Chen, Haiting Chen, Huarong Chen,","arxiv_match":true}],"id":"2601.02780","metadata_source":"Cached arXiv API metadata","rank":634},{"title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","arxiv_id":"1910.01108","year":2019,"url":"https://arxiv.org/abs/1910.01108","citing_submissions":165,"explicit_arxiv_matches":137,"examples":[{"id":"wslxAq80Kb","title":"Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models","forum_url":"https://openreview.net/forum?id=wslxAq80Kb","excerpt":"s. Springer, 2nd edition, 2004. doi: 10.1007/978-1-4757-4145-2. Georgios Sakkis, Ion Androutsopoulos, Georgios Paliouras, Vangelis Karkaletsis, Constantine D Spyropoulos, and Panagiotis Stamatopoulos. A memory-based approach to anti-spam filtering for mailing lists. Information retrieval, 6(1):49–73, 2003. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019. Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianw","arxiv_match":true},{"id":"GJfdHOgKGE","title":"Skip, Adapt, or Route? Certified Deployment of Test-Time Adaptive Rerankers under Limited Labels","forum_url":"https://openreview.net/forum?id=GJfdHOgKGE","excerpt":" SIGIR and TOIS, 2006–2015. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’16, pp. 5–14, New York, NY, USA, 2016. Association for Computing Machinery. doi: 10.1145/2911451.2911492. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019. URL https://arxiv.org/abs/1910.01108. Anna Shtok, Oren Kurland, David Carmel, Fiana Raiber, and Gilad Markovits. Predicting query perfor","arxiv_match":true},{"id":"WLxq1AWPHL","title":"IG-OPD: Information-Geometric Control for On-Policy Distillation","forum_url":"https://openreview.net/forum?id=WLxq1AWPHL","excerpt":"agavatula, and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106, 2021. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: Smaller, faster, cheaper and lighter. In NeurIPS Workshop on Energy Efficient Deep Learning, 2019. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath","arxiv_match":false}],"id":"1910.01108","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":635},{"title":"GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models","arxiv_id":"2508.06471","year":2025,"url":"https://arxiv.org/abs/2508.06471","citing_submissions":165,"explicit_arxiv_matches":152,"examples":[{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"g, Yuhang Li, Yuhao Wu, Yujiang Li, Yunan Liu, Yunqing Wang, Yuntao Li, Yuxuan Zhang, Zezhen Liu, Zhen Yang, Zhengda Zhou, Zhongpei Qiao, Zhuoer Feng, Zhuorui Liu, Zichen Zhang, Zihan Wang, Zijun Yao, Zikang Wang, Ziqiang Liu, Ziwei Chai, Zixuan Li, Zuodong Zhao, Wenguang Chen, Jidong Zhai, Bin Xu, Minlie Huang, Hongning Wang, Juanzi Li, Yuxiao Dong, and Jie Tang. Glm-4.5: Agentic, reasoning, and coding (arc) foundation models, 2025. URL https://arxiv.org/abs/2508.06471. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan","arxiv_match":true},{"id":"FVJBqG73i0","title":"AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents","forum_url":"https://openreview.net/forum?id=FVJBqG73i0","excerpt":", Maria Lomeli, Lucas Hosseini, and Hervé Jégou. The faiss library. IEEE Trans. Big Data, 12(2):346–361, 2026. doi: 10.1109/TBDATA.2025.3618474. URL https: //doi.org/10.1109/TBDATA.2025.3618474. GLM. GLM-4.5: agentic, reasoning, and coding (ARC) foundation models. CoRR, abs/2508.06471, 2025. doi: 10.48550/ARXIV.2508.06471. URL https://doi.org/10.48550/arXiv.2508. 06471. Tilmann Gneiting and Adrian E. Raftery. Strictly proper scoring rules, prediction, and est","arxiv_match":false},{"id":"vMGUOWgGQJ","title":"SparseEngine: Sparse-First Inference Engine","forum_url":"https://openreview.net/forum?id=vMGUOWgGQJ","excerpt":"dao Liu, Rui Lu, Shulin Cao, Xiaohan Zhang, Xuancheng Huang, Yao Wei, Yean Cheng, Yifang An, Yilin Niu, Yuan Wen, Yu Bai, Zhengxiao Du, Zi-Han Wang, Zilin Zhu, Bohan Zhang, Bosi Wen, Bowen Wu, et al. Glm-4.5: Agentic, reasoning, and coding (arc) foundation models. ArXiv, abs/2508.06471, 2025. URL https://api.semanticscholar.org/CorpusID:280561359. Rongzhi Zhang, Kuan Wang, Liyuan Liu, Shuohang Wang, Hao Cheng, Chao Zhang, and Yelong Shen. Lorc: Low-rank compr","arxiv_match":false}],"id":"2508.06471","metadata_source":"Cached arXiv API metadata","rank":636},{"title":"Grokking: Generalization beyond overfitting on small algorithmic datasets","arxiv_id":"2201.02177","year":2022,"url":"https://arxiv.org/abs/2201.02177","citing_submissions":165,"explicit_arxiv_matches":143,"examples":[{"id":"kzNMJ3elVQ","title":"The Limits of Algorithm Acquisition in Sequence Models","forum_url":"https://openreview.net/forum?id=kzNMJ3elVQ","excerpt":"ess measures for grokking via mechanistic interpretability. In International Conference on Learning Representations (ICLR), 2023. Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. Building machines that learn and think like people. Behavioral and Brain Sciences, 40, 2017. Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, and Vedant Misra. Grokking: Generalization beyond overfitting on small algorithmic datasets. arXiv preprint arXiv:2201.02177, 2022. Yiyou Sun, Yuhan Cao, Pohao Huang, Haoyue Bai, Hannaneh Hajishirzi, Nouha Dziri, and Dawn So","arxiv_match":true},{"id":"zTMq2gKF01","title":"How Much Attribution Mass Lands on Tokens That Cannot Matter? A Faithfulness Benchmark for Transformers","forum_url":"https://openreview.net/forum?id=zTMq2gKF01","excerpt":"n, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. In-context learning and induction heads. Transformer Circuits Thread, 2022. https://transformer-circuits.pub/ 2022/in-context-learning-and-induction-heads/index.html. Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, and Vedant Misra. Grokking: Generalization beyond overfitting on small algorithmic datasets. arXiv preprint arXiv:2201.02177, 2022. Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh,","arxiv_match":true},{"id":"oHdG0the8F","title":"CoverWeave: Coverage Percolation Governs Compositional Depth Extrapolation under Verifiable Rewards","forum_url":"https://openreview.net/forum?id=oHdG0the8F","excerpt":". J. Newman. Networks: An Introduction. Oxford University Press, 2010. Maya Okawa, Ekdeep Singh Lubana, Robert P. Dick, and Hidenori Tanaka. Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task. In Advances in Neural Information Processing Systems, 2023. Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, and Vedant Misra. Grokking: Generalization beyond overfitting on small algorithmic datasets. arXiv preprint arXiv:2201.02177, 2022. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. Measuring ","arxiv_match":true}],"id":"2201.02177","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":637},{"title":"Hybrid llm: Cost-efficient and quality-aware query routing","arxiv_id":"2404.14618","year":2024,"url":"https://arxiv.org/abs/2404.14618","citing_submissions":165,"explicit_arxiv_matches":41,"examples":[{"id":"2te1UDMg3T","title":"MedRouter: Demystifying Knowledge Differences Across Medical LLMs for Routing-Based Reasoning","forum_url":"https://openreview.net/forum?id=2te1UDMg3T","excerpt":"ersatile reward models. arXiv preprint arXiv:2405.16587, 2024. Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Rühle, Laks V. S. Lakshmanan, and Ahmed Hassan Awadallah. Hybrid LLM: Cost-efficient and qualityaware query routing. In The Twelfth International Conference on Learning Representations, 2024. URL https://proceedings.iclr.cc/paper_files/paper/2024/hash/ b47d93c99fa22ac0b377578af0a1f63a-Abstract-Conference.html. Zia","arxiv_match":false},{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"Advances in Neural Information Processing Systems, 38:57654–57689, 2026. Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Rühle, Laks Lakshmanan, and Ahmed H Awadallah. Hybrid llm: Cost-efficient and quality-aware query routing. In International Conference on Learning Representations, volume 2024, pp. 41348–41366, 2024. Zhichen Dong, Zhanhui Zhou, Zhixuan Liu, Chao Yang, and Chaochao Lu. Emergent response planning in LLMs. ","arxiv_match":false},{"id":"AnxK3Tb5VS","title":"To Ask or Not to Ask: Backdooring the Learned Consultation Policy of Collaborative LLM Agents","forum_url":"https://openreview.net/forum?id=AnxK3Tb5VS","excerpt":"ve math word problems. arXiv preprint arXiv:2110.14168, 2021. Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Rühle, Laks V. S. Lakshmanan, and Ahmed Hassan Awadallah. Hybrid LLM: Cost-efficient and quality-aware query routing. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=02f3mUtqnM. Shubham Gandhi, Yiqing Xie, Atharva Naik, Ruichen Zhu, and Carolyn Rose. St","arxiv_match":false}],"id":"2404.14618","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":638},{"title":"TTRL: Test-Time Reinforcement Learning","arxiv_id":"2504.16084","year":2025,"url":"https://arxiv.org/abs/2504.16084","citing_submissions":165,"explicit_arxiv_matches":58,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"2 2025. Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, Biqing Qi, Youbang Sun, Zhiyuan Ma, Lifan Yuan, Ning Ding, and Bowen Zhou. TTRL: Test-time reinforcement learning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. Adam Zweiger, Jyothish Pari, Han Guo, Yoon Kim, and Pulkit Agrawal. Self-adapting language models. In The Thirty","arxiv_match":false},{"id":"j0zCGq1kOr","title":"Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models","forum_url":"https://openreview.net/forum?id=j0zCGq1kOr","excerpt":".15043. Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, Biqing Qi, Youbang Sun, Zhiyuan Ma, Lifan Yuan, Ning Ding, and Bowen Zhou. TTRL: Test-time reinforcement learning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum? id=VuVhgEiu20. A Table 3 summarizes the main notation used throughout the pape","arxiv_match":false},{"id":"dHl5b1GjT0","title":"ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience","forum_url":"https://openreview.net/forum?id=dHl5b1GjT0","excerpt":"/64784. Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, Biqing Qi, Youbang Sun, Zhiyuan Ma, Lifan Yuan, Ning Ding, and Bowen Zhou. TTRL: Test-Time Reinforcement Learning. In Advances in Neural Information Processing Systems, volume 38, pp. 131459–131483. Curran Associates, Inc., 2025. doi: 10.52202/085713-4376. URL https://proceedings.neurips.cc/paper_files/paper/202","arxiv_match":false}],"id":"2504.16084","metadata_source":"Cached arXiv API metadata","rank":639},{"title":"UniVLA: Learning to Act Anywhere with Task-centric Latent Actions","arxiv_id":"2505.06111","year":2025,"url":"https://arxiv.org/abs/2505.06111","citing_submissions":165,"explicit_arxiv_matches":134,"examples":[{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"24164, 2024. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. RT-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022. Qingwen Bu, Yanting Yang, Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, and Hongyang Li. UniVLA: Learning to act anywhere with task-centric latent actions. arXiv preprint arXiv:2505.06111, 2025. Jiahong Chen, Jing Wang, Long Chen, Chuwei Cai, and Jinghui Lu. NanoVLA: Routing decoupled v","arxiv_match":true},{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"colas Heess, Lucy Gonzalez, et al. Genie: Generative interactive environments. In ICML, 2024. Qingwen Bu, Yanting Yang, Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, and Hongyang Li. UniVLA: Learning to act anywhere with task-centric latent actions. In RSS, 2025. Xizhou Bu, Jiexi Lyu, Fulei Sun, Ruichen Yang, Zhiqiang Ma, and Wei Li. LAOF: Robust latent action learning with optical flow constraints. In CVPR, 2026. Joao Carreira and Andrew Zisse","arxiv_match":false},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"2026. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control at scale. Robotics: Science and Systems XIX, 2023. Qingwen Bu, Yanting Yang, Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, and Hongyang Li. Univla: Learning to act anywhere with task-centric latent actions. arXiv preprint arXiv:2505.06111, 2025. Junhao Cai, Zetao Cai, Jiafei Cao, Yilun Chen, Zeyu He, Lei Jiang, Hang Li, Hengjie Li, Yang","arxiv_match":true}],"id":"2505.06111","metadata_source":"Cached arXiv API metadata","rank":640},{"title":"Stereo magnification: Learning view synthesis using multiplane images","arxiv_id":"1805.09817","year":2018,"url":"https://arxiv.org/abs/1805.09817","citing_submissions":164,"explicit_arxiv_matches":85,"examples":[{"id":"9kxTLJwbxB","title":"TraceWorld: Trajectory-Indexed Memory for Spatially Consistent Video World Model","forum_url":"https://openreview.net/forum?id=9kxTLJwbxB","excerpt":"024b. Jensen Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. In 2025 IEEE/CVF International Conference on Computer Vision, pp. 12405–12414. IEEE, 2025a. Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817, 2018. Yang Zhou, Yifan Wang, Jianjun Zhou, Wenzheng Chang, Haoyu Guo, Zizun Li, Kaijing Ma, Xinyue","arxiv_match":true},{"id":"ukP6owZeZe","title":"SphereBridge: Single-Panorama 3D Gaussian Reconstruction for High-Resolution Real-Time Multi-Sphere Rendering","forum_url":"https://openreview.net/forum?id=ukP6owZeZe","excerpt":"Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. TOG, 2018. A This section gives the full pair-selection protocol referenced in the Datasets paragraph of Section 4.1. The main text uses a single-panorama protocol: each method receives one source E","arxiv_match":false},{"id":"aocYTIRnhk","title":"GeoVerse: World-Consistent Novel View Synthesis in Geometric Latent Space","forum_url":"https://openreview.net/forum?id=aocYTIRnhk","excerpt":"tric. In CVPR, pp. 586–595, 2018. Yang Zhou, Yifan Wang, Jianjun Zhou, Wenzheng Chang, Haoyu Guo, Zizun Li, Kaijing Ma, Xinyue Li, Yating Wang, Haoyi Zhu, et al. Omniworld: A multi-domain and multi-modal dataset for 4d world modeling. arXiv preprint arXiv:2509.12201, 2025. Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM TOG, 37, 2018. URL https://arxiv.org/abs/1805.09817. This supplementary material provides additional technical details and experimental results to comp","arxiv_match":true}],"id":"1805.09817","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":641},{"title":"An empirical study of catastrophic forgetting in large language models during continual fine-tuning.","arxiv_id":"2308.08747","year":2023,"url":"https://arxiv.org/abs/2308.08747","citing_submissions":163,"explicit_arxiv_matches":60,"examples":[{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"las D. Lane, and Mengwei Xu. Small language models: Survey, measurements, and insights, 2025. URL https://arxiv.org/abs/2409.15790. Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. An empirical study of catastrophic forgetting in large language models during continual fine-tuning, 2025. Michael McCloskey and Neal J. Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. volume 24 of Psychology of Learning and Motivation, pp. 109–165. Acad","arxiv_match":false},{"id":"kM6wd2rhiZ","title":"Towards Multi-CT Perception and Understanding with a Radiology Tool-Augmented Multimodal Large Language Model","forum_url":"https://openreview.net/forum?id=kM6wd2rhiZ","excerpt":"sis. In The Fourteenth International Conference on Learning Representations, 2026. URL https: //openreview.net/forum?id=nrZI64gTvC. Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. An empirical study of catastrophic forgetting in large language models during continual fine-tuning. IEEE Transactions on Audio, Speech and Language Processing, 2025. Heber MacMahon, David P Naidich, Jin Mo Goo, Kyung Soo Lee, Ann NC Leung, John R Mayo, Atul C Mehta, Yoshiharu Ohno, Charles A Powel","arxiv_match":false},{"id":"TA0thWBRuQ","title":"ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence","forum_url":"https://openreview.net/forum?id=TA0thWBRuQ","excerpt":" International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 22631–22648. PMLR, 2023. Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. An empirical study of catastrophic forgetting in large language models during continual fine-tuning. IEEE Transactions on Audio, Speech and Language Processing, 33:3776–3786, 2025. Xiaojian Ma, Silong Yong, Zilong Zheng, Qing Li, Yitao Liang, Song-Chun Zhu, and Siyuan Huang. SQA3D: Situated questio","arxiv_match":false}],"id":"2308.08747","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":642},{"title":"Discovering language model behaviors with model-written evaluations","arxiv_id":"2212.09251","year":2022,"url":"https://arxiv.org/abs/2212.09251","citing_submissions":163,"explicit_arxiv_matches":41,"examples":[{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":"/openai.com/index/sycophancy-in-gpt-4o/. Ethan Perez, Sam Ringer, Kamilē Lukošiūtē, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, et al. Discovering language model behaviors with model-written evaluations. In Findings of ACL, 2023. OpenAI. Expanding on what we missed with sycophancy. OpenAI, May 2025a. URL https: //openai.com/index/expanding-on-sycophancy/. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Alm","arxiv_match":false},{"id":"FrxiIrkLSN","title":"The Anatomy of Conversational Consistency: Linear Readouts and Control Under Pressure","forum_url":"https://openreview.net/forum?id=FrxiIrkLSN","excerpt":"m Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, and Jared Kaplan. Discovering language model behaviors with model-written evaluations. In Findings of the Association for Computational Linguistics: ACL 2023, pp. 13387–13434. Association for Computational Linguistics, 2023. doi: 10.18653/v1/2023.findings-acl.847. Qibing Ren, Hao Li, ","arxiv_match":false},{"id":"P8ts9TSQ5K","title":"Correct Trace Conclusions, Wrong Answers: Unfaithful Capitulation Under Pressure","forum_url":"https://openreview.net/forum?id=P8ts9TSQ5K","excerpt":"ics: EMNLP 2024, pp. 15012–15032, 2024. Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, et al. Discovering language model behaviors with model-written evaluations. In Findings of the Association for Computational Linguistics: ACL 2023, pp. 13387–13434, 2023. Leonardo Ranaldi and Giulia Pucci. When large language models contradict humans? Large language models’","arxiv_match":false}],"id":"2212.09251","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":643},{"title":"WildChat: 1M ChatGPT Interaction Logs in the Wild","arxiv_id":"2405.01470","year":2024,"url":"https://arxiv.org/abs/2405.01470","citing_submissions":163,"explicit_arxiv_matches":42,"examples":[{"id":"KbAV5KY9YW","title":"Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue","forum_url":"https://openreview.net/forum?id=KbAV5KY9YW","excerpt":" In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13761– 13771, October 2025b. Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. Wildchat: 1m chatgpt interaction logs in the wild. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. URL https://openreview.net/forum?id=Bl8u7ZRlbM. Siyi Zhou, Yiqu","arxiv_match":false},{"id":"M752QnEbXC","title":"CoPE: Co-Evolving Policy and Experience via Context Distillation for Open-Ended Generation","forum_url":"https://openreview.net/forum?id=M752QnEbXC","excerpt":" In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/ forum?id=Jpxfof0EaS. Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. Wildchat: 1m chatGPT interaction logs in the wild. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Bl8u7ZRlbM. A P ROMPT T EMPLATES We use prompt templates from EL (Ye et al., 2026a) and","arxiv_match":false},{"id":"SMiWjRdzKn","title":"Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors","forum_url":"https://openreview.net/forum?id=SMiWjRdzKn","excerpt":"ai Wang, Yang Yue, Shiji Song, and Gao Huang. Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?, 2025. URL https://arxiv.org/abs/2504.13837. Z.ai. Glm-5.3: Frontier coding with emergent cyber capabilities, august 2026. URL https://z.ai/blog/ glm-5.3. Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. Wildchat: 1m chatgpt interaction logs in the wild, 2024. URL https://arxiv.org/abs/2405.01470. Yifei Zhou, Song Jiang, Yuandong Tian, Jason Weston, Sergey Levine, Sainbayar Sukhbaatar, and Xian","arxiv_match":true}],"id":"2405.01470","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":644},{"title":"Evolution of heuristics: Towards efficient automatic algorithm design using large language model","arxiv_id":"2401.02051","year":2024,"url":"https://arxiv.org/abs/2401.02051","citing_submissions":162,"explicit_arxiv_matches":42,"examples":[{"id":"1Kxk30PvqF","title":"From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery","forum_url":"https://openreview.net/forum?id=1Kxk30PvqF","excerpt":"an, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step. In International Conference on Learning Representations, volume 2024, pp. 39578–39601, 2024. Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang. Evolution of heuristics: Towards efficient automatic algorithm design using large language model. arXiv preprint arXiv:2401.02051, 2024. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Al","arxiv_match":true},{"id":"ZIVFDKAh8p","title":"Automated Algorithm Discovery through Mathematical Reformulation: A Study in Visual-Token Reduction","forum_url":"https://openreview.net/forum?id=ZIVFDKAh8p","excerpt":"eprint arXiv:2604.05550, 2026. doi: 10.48550/arXiv.2604.05550. URL https://arxiv.org/abs/2604.05550. Fei Liu, Tong Xialiang, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang. Evolution of heuristics: Towards efficient automatic algorithm design using large language model. In Proceedings of the 41st International Conference on Machine Learning, 2024a. Gang Liu, Yihan Zhu, Jie Chen, and Meng Jiang. Scientific algorithm discovery by augmenting AlphaEvolve with deep rese","arxiv_match":false},{"id":"LfIpbuIKLb","title":"Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Memory Requirements","forum_url":"https://openreview.net/forum?id=LfIpbuIKLb","excerpt":"Li, Peng Zhou, Rui Meng, Meet P Vadera, Lihong Li, and Yang Li. Turn-ppo: Turn-level advantage estimation with ppo for improved multi-turn rl in agentic llms. In Findings of the Association for Computational Linguistics: EACL 2026, pp. 6227–6243, 2026. Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang. Evolution of heuristics: Towards efficient automatic algorithm design using large language model. arXiv preprint arXiv:2401.02051, 2024. WenTao Liu, Siyu Song, Hao Hao, and Aimin Zhou. Ea4llm: A gradient-free approach to large la","arxiv_match":true}],"id":"2401.02051","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":645},{"title":"HealthBench: Evaluating Large Language Models Towards Improved Human Health","arxiv_id":"2505.08775","year":2025,"url":"https://arxiv.org/abs/2505.08775","citing_submissions":162,"explicit_arxiv_matches":138,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"nguage models: Learning from selfgenerated mistakes. In International Conference on Learning Representations, volume 2024, pp. 21246–21263, 2024. Rahul K. Arora, Jason Wei, Rebecca Soskin Hicks, Preston Bowman, Joaquin Quiñonero-Candela, Foivos Tsimpourlas, Michael Sharman, Meghan Shah, Andrea Vallone, Alex Beutel, Johannes Heidecke, and Karan Singhal. HealthBench: Evaluating large language models towards improved human health, 2025. URL https://arxiv.org/abs/2505.08775. Anisha Gunjal, Anthony Wang, Elaine Lau, Vaskar Nath, Yunzhong He, Bing Liu, and Sean Hendryx. Rub","arxiv_match":true},{"id":"O0MnDF6uMA","title":"Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents","forum_url":"https://openreview.net/forum?id=O0MnDF6uMA","excerpt":"nes Brandstetter, and Sepp Hochreiter. RUDDER: Return decomposition for delayed rewards. In Advances in Neural Information Processing Systems (NeurIPS), volume 32. Curran Associates, Inc., 2019. Rahul K. Arora, Jason Wei, Rebecca Soskin Hicks, Preston Bowman, Joaquin Quiñonero-Candela, Foivos Tsimpourlas, Michael Sharman, Meghan Shah, Andrea Vallone, Alex Beutel, et al. HealthBench: Evaluating large language models towards improved human health. arXiv preprint arXiv:2505.08775, 2025. Ilze Amanda Auzina, Joschka Strüber, Sergio Hernández-Gutiérrez, Shashwat Goel, Ameya Prabhu","arxiv_match":true},{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"rcement Learning. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum? id=RQm2KQTM5r. Rahul K. Arora, Jason Wei, Rebecca Soskin Hicks, Preston Bowman, Joaquin Quiñonero-Candela, Foivos Tsimpourlas, Michael Sharman, Meghan Shah, Andrea Vallone, Alex Beutel, Johannes Heidecke, and Karan Singhal. Healthbench: Evaluating large language models towards improved human health, 2025. URL https://arxiv.org/abs/2505.08775. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie","arxiv_match":true}],"id":"2505.08775","metadata_source":"Cached arXiv API metadata","rank":646},{"title":"Least-to-most prompting enables complex reasoning in large language models","arxiv_id":"2205.10625","year":2022,"url":"https://arxiv.org/abs/2205.10625","citing_submissions":162,"explicit_arxiv_matches":65,"examples":[{"id":"6Zkm41r219","title":"REx: Small Language Model Agents with Recursive Execution and Subtree Compression","forum_url":"https://openreview.net/forum?id=6Zkm41r219","excerpt":" pp. 105457–105485, 2025. doi: 10.52202/085713-3181. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, and Ed Chi. Least-to-most prompting enables complex reasoning in large language models. In International Conference on Learning Representations, 2023. A RE X A LGORITHM AND I MPLEMENTATION D ETAILS Algorithm 1 gives the complete recursive execution procedure. We index the root task at ","arxiv_match":false},{"id":"zYUaM9Xm0l","title":"Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers","forum_url":"https://openreview.net/forum?id=zYUaM9Xm0l","excerpt":"a-judge. arXiv preprint arXiv:2507.08794, 2025. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. Least-to-most prompting enables complex reasoning in large language models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. A F ROM REINFORCE-S TYLE C ORRECTIONS TO GRPO U PDATES Setup (si","arxiv_match":false},{"id":"x6oIXJYZ7g","title":"LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs","forum_url":"https://openreview.net/forum?id=x6oIXJYZ7g","excerpt":"04.05419, 2025. doi: 10.48550/arXiv.2504.05419. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. Least-to-most prompting enables complex reasoning in large language models. In International Conference on Learning Representations, 2023. Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombr","arxiv_match":false}],"id":"2205.10625","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":647},{"title":"MLAgentBench: Evaluating language agents on machine learning experimentation","arxiv_id":"2310.03302","year":2023,"url":"https://arxiv.org/abs/2310.03302","citing_submissions":162,"explicit_arxiv_matches":53,"examples":[{"id":"P9bJmoIx5X","title":"Controlling False Discoveries via Auditing Scientific Agents","forum_url":"https://openreview.net/forum?id=P9bJmoIx5X","excerpt":"l-purpose biomedical AI agent. bioRxiv, 2025b. doi: 10.1101/2025.05.30.656746. URL https://www.biorxiv.org/content/ 10.1101/2025.05.30.656746v1. Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec. MLAgentBench: Evaluating language agents on machine learning experimentation. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 20271– 20309. PMLR, 2024. URL https://proceedings.mlr.press/v235/","arxiv_match":false},{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":" agentic systems. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview. net/forum?id=Joc8ecV2im. Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec. MLAgentBench: Evaluating language agents on machine learning experimentation. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 20271– 20309. PMLR, 2024. URL https://proceedings.mlr.press/v235/","arxiv_match":false},{"id":"6EWMGy11eE","title":"CreditAgentBench: Measuring Agentic Credit-Model Repair under Production Constraints","forum_url":"https://openreview.net/forum?id=6EWMGy11eE","excerpt":"jing Huang. AgentHPOBench: A benchmark for evaluating LLM agents as sequential hyperparameter optimizers. arXiv preprint arXiv:2607.29626, 2026. Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec. MLAgentBench: Evaluating language agents on machine learning experimentation. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 20271–20309. PMLR, 2024. Frank Hutter, Holger H. Hoos, and Kevin ","arxiv_match":false}],"id":"2310.03302","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":648},{"title":"Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking","arxiv_id":"2601.04720","year":2026,"url":"https://arxiv.org/abs/2601.04720","citing_submissions":162,"explicit_arxiv_matches":147,"examples":[{"id":"zoKRDAQpft","title":"AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval","forum_url":"https://openreview.net/forum?id=zoKRDAQpft","excerpt":"Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning (ICML), 2023. Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, Sibo Song, Shuai Bai, Zhibo Yang, Pengjun Xie, An Yang, Dayiheng Liu, et al. Qwen3-vl-embedding and qwen3-vl-reranker: A unified framework for state-of-the-art multimodal retrieval and ranking. arXiv preprint arXiv:2601.04720, 2026. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr ","arxiv_match":true},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"v:2507.16746, 2025. Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023. Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, Sibo Song, Shuai Bai, Zhibo Yang, Pengjun Xie, An Yang, Dayiheng Liu, et al. Qwen3-vl-embedding and qwen3-vl-reranker: A unified framework for state-of-the-art multimodal retrieval and ranking. arXiv preprint arXiv:2601.04720, 2026. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr ","arxiv_match":true},{"id":"F0hAiHbpm3","title":"FigAct: Turning Scientific Figures into Active Canvases for Explanation","forum_url":"https://openreview.net/forum?id=F0hAiHbpm3","excerpt":"olume 1: Long Papers), pp. 14369–14387, 2024. Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, Sibo Song, Shuai Bai, Zhibo Yang, Pengjun Xie, An Yang, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Qwen3-vl-embedding and qwen3-vl-reranker: A unified framework for state-of-the-art multimodal retrieval and ranking. arXiv, 2026. Kevin Qinghong Lin, Linjie Li, Difei Gao, Zhengyuan Yang, Shiwei Wu, Zechen Bai, Weixian Lei, Lijuan Wang, and Mike Zheng Shou. Showui: One vision-language-action model for gui visual a","arxiv_match":false}],"id":"2601.04720","metadata_source":"Cached arXiv API metadata","rank":649},{"title":"Video-R1: Reinforcing Video Reasoning in MLLMs","arxiv_id":"2503.21776","year":2025,"url":"https://arxiv.org/abs/2503.21776","citing_submissions":162,"explicit_arxiv_matches":53,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":" and audio understanding in video-llms. arXiv:2406.07476, 2024. Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Junfei Wu, Xiaoying Zhang, Benyou Wang, and Xiangyu Yue. Video-r1: Reinforcing video reasoning in mllms. In NeurIPS, 2025. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluat","arxiv_match":false},{"id":"9XqqxYouLj","title":"Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning","forum_url":"https://openreview.net/forum?id=9XqqxYouLj","excerpt":"optimization for video reasoning. CoRR, abs/2602.23823, 2026. 6 Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Junfei Wu, Xiaoying Zhang, Benyou Wang, and Xiangyu Yue. Video-r1: Reinforcing video reasoning in mllms. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, NeurIPS 2025, San Diego, CA, USA, December 2-7, 2025 / Mexico City, Mexico, ","arxiv_match":false},{"id":"7EtSASfnVn","title":"OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning","forum_url":"https://openreview.net/forum?id=7EtSASfnVn","excerpt":"s, 2026. URL https://arxiv.org/abs/2512.12772. Zhangquan Chen, Jiale Tao, Ruihuang Li, Yihao Hu, Ruitao Chen, Zhantao Yang, Xinlei Yu, Haodong Jing, et al. OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention. arXiv preprint arXiv:2602.05847, 2026. Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Junfei Wu, Xiaoying Zhang, et al. Video-R1: Reinforcing Video Reasoning in MLLMs. arXiv preprint arXiv:2503.21776, 2025. Chaoyou Fu, Haozhi Yuan, Yuhao Dong, Yi-Fan Zhang, Yunhang Shen, Xiaoxing Hu, Xueying Li, Ji","arxiv_match":true}],"id":"2503.21776","metadata_source":"Cached arXiv API metadata","rank":650},{"title":"tinyBenchmarks: Evaluating LLMs with fewer examples","arxiv_id":"2402.14992","year":2024,"url":"https://arxiv.org/abs/2402.14992","citing_submissions":162,"explicit_arxiv_matches":51,"examples":[{"id":"fP0yCyTBGy","title":"Anchor Reconstruction for Efficient Model-Task Evaluation: Identifiability and Stability","forum_url":"https://openreview.net/forum?id=fP0yCyTBGy","excerpt":"randomized and deterministic sampling points. SIAM Journal on Scientific Computing, 42:A2837–A2864, 2020. Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin. tinybenchmarks: evaluating LLMs with fewer examples. In Proceedings of the 41st International Conference on Machine Learning, pp. 34303–34326, 2024. Viktoria Schram, Daniel Beck, and Trevor Cohn. Performance prediction via bayesian matrix factorisatio","arxiv_match":false},{"id":"T2LjlcdfvD","title":"SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities","forum_url":"https://openreview.net/forum?id=T2LjlcdfvD","excerpt":"al Linguistics. doi: 10.18653/v1/ 2024.naacl-long.139. URL https://aclanthology.org/2024.naacl-long.139/. Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin. tinybenchmarks: evaluating llms with fewer examples. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id= qAml3FpfhG. Qwen Team. Qwen3.7, August 2026. URL https://qwen.ai/blog?id=qwen3.7. Published Au","arxiv_match":false},{"id":"bLQ22POBgo","title":"Beyond Parameter Space: Closed-Form Cross-Trajectory Repair After Harmful Fine-Tuning","forum_url":"https://openreview.net/forum?id=bLQ22POBgo","excerpt":"inguistics, 2025. doi: 10.18653/v1/2025. acl-long.1055. URL https://aclanthology.org/2025.acl-long.1055/. Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin. tinybenchmarks: Evaluating LLMs with fewer examples. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 34303–34326. PMLR, 2024. URL https://proceedings.mlr. press/v235/","arxiv_match":false}],"id":"2402.14992","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":651},{"title":"Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs","arxiv_id":"2605.00674","year":2026,"url":"https://arxiv.org/abs/2605.00674","citing_submissions":161,"explicit_arxiv_matches":142,"examples":[{"id":"iksnFm6vFW","title":"Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage","forum_url":"https://openreview.net/forum?id=iksnFm6vFW","excerpt":"erable reasoning calibration of large language models for free. arXiv preprint arXiv:2504.07986, 2025. Muzhi Dai, Chenxu Yang, and Qingyi Si. S-grpo: Early exit via reinforcement learning in reasoning models, 2025. URL https://arxiv.org/abs/2505.07686. Jasper Dekoninck, Nikola Jovanović, Tim Gehrunger, Kári Rögnvaldsson, Ivo Petrov, Chenhao Sun, and Martin Vechev. Beyond benchmarks: Matharena as an evaluation platform for mathematics with llms. arXiv preprint arXiv:2605.00674, 2026. Boyi Deng, Xu Wang, Yaoning Wang, Yu Wan, Yubo Ma, Baosong Yang, Haoran Wei, Jialong Tang, H","arxiv_match":true},{"id":"0XTyMGNG5R","title":"ReLay: Are We Ready for Latent Routing for Multi-Agent Systems?","forum_url":"https://openreview.net/forum?id=0XTyMGNG5R","excerpt":"ong Sun. Multi-agent collaboration via evolving orchestration. arXiv preprint arXiv:2505.19591, 2025. DeepSeek-AI. DeepSeek-V4: Towards highly efficient million-token context intelligence, 2026. URL https://arxiv.org/abs/2606.19348. Jasper Dekoninck, Nikola Jovanović, Tim Gehrunger, Kári Rögnvalddson, Ivo Petrov, Chenhao Sun, and Martin Vechev. Beyond benchmarks: Matharena as an evaluation platform for mathematics with llms. 2026. URL https://arxiv.org/abs/2605.00674. Lucio M. Dery, Zohar Yahav, Henry Prior, Qixuan Feng, Jiajun Shen, and Arthur Szlam. Latent space ","arxiv_match":true},{"id":"1Kxk30PvqF","title":"From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery","forum_url":"https://openreview.net/forum?id=1Kxk30PvqF","excerpt":"un Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, and Zhen Zhang. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Jasper Dekoninck, Nikola Jovanović, Tim Gehrunger, Kári Rögnvaldsson, Ivo Petrov, Chenhao Sun, and Martin Vechev. Beyond benchmarks: Matharena as an evaluation platform for mathematics with llms. 2026. URL https://arxiv.org/abs/2605.00674. Shiyi Du, Jiayuan Liu, Weihua Du, Yue Huang, Jiayi Li, Yingtao Luo, Xiangliang Zhang, Vincent Coni","arxiv_match":true}],"id":"2605.00674","metadata_source":"Cached arXiv API metadata","rank":652},{"title":"Human motion diffusion model","arxiv_id":"2209.14916","year":2022,"url":"https://arxiv.org/abs/2209.14916","citing_submissions":161,"explicit_arxiv_matches":34,"examples":[{"id":"wcae0JKDe0","title":"MoGeFi: Learning Geometric Feedback for Few-Step Human Motion Generation","forum_url":"https://openreview.net/forum?id=wcae0JKDe0","excerpt":"xposing human motion generation to CLIP space. In European Conference on Computer Vision, pp. 358–374, 2022. Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H. Bermano. Human motion diffusion model. In International Conference on Learning Representations, 2023. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention","arxiv_match":false},{"id":"UiEj3slVG7","title":"Kinematic Transition Model for Training-free Online Human Motion Control","forum_url":"https://openreview.net/forum?id=UiEj3slVG7","excerpt":"ios Tzionas. Goal: Generating 4d whole-body motion for hand-object grasping. In CVPR, pp. 13253–13263, 2022. Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H. Bermano. Human Motion Diffusion Model. In ICLR, 2023. Guy Tevet, Sigal Raab, Setareh Cohan, Daniele Reda, Zhengyi Luo, Xue Bin Peng, Amit Bermano, and Michiel van de Panne. CLoSD: Closing the loop between simulation and diffusion for mul","arxiv_match":false},{"id":"BKhScrAhtQ","title":"Phase-Aware Video Generation for Physics-Grounded Dynamics and Interactions","forum_url":"https://openreview.net/forum?id=BKhScrAhtQ","excerpt":"ale video generative models. arXiv preprint arXiv:2503.20314, 2025. URL https://arxiv.org/abs/2503. 20314. Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit Haim Bermano. Human motion diffusion model. In International Conference on Learning Representations, 2023. Nils Thuerey and Tobias Pfaff. Mantaflow: An extensible framework for fluid simulation. Opensource software, 2018. URL http://mantaflow","arxiv_match":false}],"id":"2209.14916","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":653},{"title":"Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation","arxiv_id":"2503.11926","year":2025,"url":"https://arxiv.org/abs/2503.11926","citing_submissions":161,"explicit_arxiv_matches":148,"examples":[{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"d, et al. Many-shot jailbreaking. NeurIPS, 2024. Andy Arditi, Oscar Balcells Obeso, Aaquib Syed, Daniel Paleka, Nina Rimsky, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. In NeurIPS, 2024. Bowen Baker, Joost Huizinga, Leo Gao, Zehao Dou, Melody Y Guan, Aleksander Madry, Wojciech Zaremba, Jakub Pachocki, and David Farhi. Monitoring reasoning models for misbehavior and the risks of promoting obfuscation. arXiv preprint arXiv:2503.11926, 2025. Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances. Computational L","arxiv_match":true},{"id":"Dy3RZfaeEs","title":"Do What You Say: Towards Verifiable Reasoning for Scientific Editing","forum_url":"https://openreview.net/forum?id=Dy3RZfaeEs","excerpt":" Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, and Arthur Conmy. Chain-of-thought reasoning in the wild is not always faithful, 2026. URL https://arxiv.org/abs/2503.08679. Bowen Baker, Joost Huizinga, Leo Gao, Zehao Dou, Melody Y. Guan, Aleksander Madry, Wojciech Zaremba, Jakub Pachocki, and David Farhi. Monitoring reasoning models for misbehavior and the risks of promoting obfuscation, 2025. URL https://arxiv.org/abs/2503.11926. Parit Bansal, Anne Morgat, Kristian B Axelsen, Venkatesh Muthukrishnan, Elisabeth Coudert, Lucila ","arxiv_match":true},{"id":"9Ou6wrydAL","title":"Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following","forum_url":"https://openreview.net/forum?id=9Ou6wrydAL","excerpt":" Artificial Analysis. Artificial analysis. https://artificialanalysis.ai/, 2026. Bowen Baker, Joost Huizinga, Leo Gao, Zehao Dou, Melody Y Guan, Aleksander Madry, Wojciech Zaremba, Jakub Pachocki, and David Farhi. Monitoring reasoning models for misbehavior and the risks of promoting obfuscation. arXiv preprint arXiv:2503.11926, 2025. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xue","arxiv_match":true}],"id":"2503.11926","metadata_source":"Cached arXiv API metadata","rank":654},{"title":"Rethinking attention with Performers","arxiv_id":"2009.14794","year":2020,"url":"https://arxiv.org/abs/2009.14794","citing_submissions":161,"explicit_arxiv_matches":59,"examples":[{"id":"XIvhtY214J","title":"SOLAR: SVD-Optimized Lifelong Attention for Recommendation","forum_url":"https://openreview.net/forum?id=XIvhtY214J","excerpt":"oromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, and Adrian Weller. Rethinking attention with performers. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id= Ua6zuk0WRH. Qihang Fan, Huaibo Huang, Yuang Ai, and Ran He. Rectifying magnitude neglect in linear","arxiv_match":false},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"xin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, et al. Videocrafter1: Open diffusion models for highquality video generation. arXiv preprint arXiv:2310.19512, 2023. Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al. Rethinking attention with performers. arXiv preprint arXiv:2009.14794, 2020. Jiaxing Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, and C","arxiv_match":true},{"id":"SaboBZsQiW","title":"KernelWeaver: A Shape-Aware Kernel Agent for GPU Kernel Optimization","forum_url":"https://openreview.net/forum?id=SaboBZsQiW","excerpt":"ii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, and Adrian Weller. Rethinking attention with performers. In International Conference on Learning Representations, 2021. URL https: //openreview.net/forum?id=Ua6zuk0WRH. Weinan Dai, Hanlin Wu, Qiying Yu, Huan ang Gao, Jiahao Li, Chengquan Jiang, Weiqiang L","arxiv_match":false}],"id":"2009.14794","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":655},{"title":"GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control","arxiv_id":"2503.03751","year":2025,"url":"https://arxiv.org/abs/2503.03751","citing_submissions":160,"explicit_arxiv_matches":12,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"Xiv preprint arXiv:2410.18072, 2024. URL https://arxiv.org/abs/2410.18072. Qwen Team. Qwen3.5-9B. Official model card, 2026. URL https://huggingface.co/Qwen/Qwen3. 5-9B. Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3C: 3d-informed world-consistent video generation with precise camera control. arXiv preprint arXiv:2503.03751, 2025. URL https: //arxiv.org/abs/2503.03751. Robbyant Team, Zelin Gao, Qiuyu Wang, Yanhong Zeng, et al. Advancing open-source world models. arX","arxiv_match":true},{"id":"aocYTIRnhk","title":"GeoVerse: World-Consistent Novel View Synthesis in Geometric Latent Space","forum_url":"https://openreview.net/forum?id=aocYTIRnhk","excerpt":" 3d gaussians. arXiv preprint arXiv:2403.17898, 2024. Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3c: 3d-informed world-consistent video generation with precise camera control. In CVPR, 2025. Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M. Susskind. Hypersim: A photorealistic synthetic dataset for h","arxiv_match":false},{"id":"KP1C5Vu5Nr","title":"Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction","forum_url":"https://openreview.net/forum?id=KP1C5Vu5Nr","excerpt":"ern Recognition (CVPR), pp. 11179– 11188. IEEE, 2021. Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3c: 3d-informed world-consistent video generation with precise camera control. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6121–6132. IEEE, 2025. Robbyant Team, Zelin Gao, Qiuyu Wang, Yanhong Zeng, Jiapeng Zhu, Ka Leong Cheng, Yixuan Li, ","arxiv_match":false}],"id":"2503.03751","metadata_source":"Cached arXiv API metadata","rank":656},{"title":"Rewarding progress: Scaling automated process verifiers for LLM reasoning","arxiv_id":"2410.08146","year":2024,"url":"https://arxiv.org/abs/2410.08146","citing_submissions":160,"explicit_arxiv_matches":43,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"ization algorithms. arXiv preprint arXiv:1707.06347, 2017. Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, and Aviral Kumar. Rewarding progress: Scaling automated process verifiers for llm reasoning. In International Conference on Learning Representations, volume 2025, pp. 60808–60838, 2025. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK ","arxiv_match":false},{"id":"ff2QTmBUmn","title":"Collaborative Learning and Reasoning over Model Slices","forum_url":"https://openreview.net/forum?id=ff2QTmBUmn","excerpt":"rocessing (EMNLP), 2026. https://arxiv.org/abs/2609.00237. Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, and Aviral Kumar. Rewarding progress: Scaling automated process verifiers for LLM reasoning. In International Conference on Learning Representations (ICLR), 2025. https://proceedings.iclr.cc/paper_files/paper/2025/ hash/98711dea460bdefe0e651ca23ec98ba2-Abstract-Conference.html. Charlie Snel","arxiv_match":false},{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"or embodied agents. arXiv preprint arXiv:2602.04326, 2026. Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, and Aviral Kumar. Rewarding progress: Scaling automated process verifiers for llm reasoning. In International Conference on Learning Representations, volume 2025, pp. 60808–60838, 2025. Idan Shenfeld, Jyothish Pari, and Pulkit Agrawal. Rl’s razor: Why online reinforcement learning forgets l","arxiv_match":false}],"id":"2410.08146","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":657},{"title":"Rolling Forcing: Autoregressive Long Video Diffusion in Real Time","arxiv_id":"2509.25161","year":2025,"url":"https://arxiv.org/abs/2509.25161","citing_submissions":160,"explicit_arxiv_matches":66,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"on via annotation-free continuation preference optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20024–20033, 2026. Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022. Kunhao Liu, Wenbo Hu, Jiale Xu, Ying Shan, and Shijian Lu. Rolling forcing: Autoregressive long video diffusion in real time. arXiv preprint arXiv:2509.25161, 2025. Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and","arxiv_match":true},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"tematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024. Kuaishou. Kling 3.0. https://klingai.com/, 2026. Kunhao Liu, Wenbo Hu, Jiale Xu, Ying Shan, and Shijian Lu. Rolling forcing: Autoregressive long video diffusion in real time. In International Conference on Learning Representations, volume 2026, pp. 91177–91196, 2026. Yunhong Lu, Yanhong Zeng, Haobo Li, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Jiapeng Zhu, Hengyuan Cao, Zh","arxiv_match":false},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":"1: Test-time scaling for video generation. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 18671–18681. IEEE, 2025. Kunhao Liu, Wenbo Hu, Jiale Xu, Ying Shan, and Shijian Lu. Rolling forcing: Autoregressive long video diffusion in real time. In International Conference on Learning Representations, volume 2026, pp. 91177–91196, 2026. Do Xuan Long, Xingchen Wan, Hootan Nakhost, Chen-Yu Lee, Tomas Pfister, and Sercan Ö Arik. Vista: A test","arxiv_match":false}],"id":"2509.25161","metadata_source":"Cached arXiv API metadata","rank":658},{"title":"Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer","arxiv_id":"2511.22699","year":2025,"url":"https://arxiv.org/abs/2511.22699","citing_submissions":160,"explicit_arxiv_matches":155,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"ing He. MiniT2I: A minimalist baseline for text-to-image generation. https://peppaking8. github.io/#/post/minit2i, 2026d. Yongsheng Yu, Wei Xiong, Weili Nie, Yichen Sheng, Shiqiu Liu, and Jiebo Luo. Pixeldit: Pixel diffusion transformers for image generation. In CVPR, 2026. Z-Image Team, Huanqia Cai, Sihan Cao, Ruoyi Du, Peng Gao, Steven Hoi, et al. Z-image: An efficient image generation foundation model with single-stream diffusion transformer. arXiv preprint arXiv:2511.22699, 2025. Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, and Zhuang Liu. i1: ","arxiv_match":true},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":"rmers for image generation. In CVPR, 2026. Z-Image Team, Huanqia Cai, Sihan Cao, Ruoyi Du, Peng Gao, Steven Hoi, Zhaohui Hou, Shijie Huang, Dengyang Jiang, Xin Jin, Liangchen Li, Zhen Li, Zhong-Yu Li, David Liu, Dongyang Liu, Junhan Shi, Qilong Wu, Feng Yu, Chi Zhang, Shifeng Zhang, and Shilin Zhou. Z-image: An efficient image generation foundation model with single-stream diffusion transformer. arXiv preprint arXiv:2511.22699, 2025. URL https://arxiv.org/abs/2511.22699. Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, and Zhuang Liu. i1: A simp","arxiv_match":true},{"id":"7x4AAlYBDM","title":"Stabilizing Multi-Step Diffusion Distillation with Student Perturbation Consistency","forum_url":"https://openreview.net/forum?id=7x4AAlYBDM","excerpt":"ei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Frédo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In CVPR, 2024b. Yuichi Yoshida and Takeru Miyato. Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941, 2017. Z-Image Team. Z-image: An efficient image generation foundation model with single-stream diffusion transformer. arXiv preprint arXiv:2511.22699, 2025. Kaiwen Zheng, Yuji Wang, Qianli Ma, Huayu Chen, Jintao Zhang, Yogesh Balaji, Jianfei Chen, M","arxiv_match":true}],"id":"2511.22699","metadata_source":"Cached arXiv API metadata","rank":659},{"title":"Emu3: Next-token prediction is all you need","arxiv_id":"2409.18869","year":2024,"url":"https://arxiv.org/abs/2409.18869","citing_submissions":159,"explicit_arxiv_matches":151,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":". Uniddt: Unifying multimodal understanding and generation with decoupled diffusion transformer, 2026d. URL https://arxiv.org/abs/2606.16255. Sophie L Wang, Phillip Isola, and Brian Cheung. Words that make language models perceive. arXiv preprint arXiv:2510.02425, 2025b. Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024b. Yuqing Wang, Zhijie Lin, Ceyuan Yang, Yang Zhao, Fei Xiao, Hao He, Qi Zhao, Zihan Ding, Fuy","arxiv_match":true},{"id":"GM9LePOwrX","title":"Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=GM9LePOwrX","excerpt":"en Li, Qiying Yu, Yingli Zhao, Yulong Ao, Xuebin Min, Tao Li, Boya Wu, Bo Zhao, Bowen Zhang, Liangdong Wang, Guang Liu, Zheqi He, Xi Yang, Jingjing Liu, Yonghua Lin, Tiejun Huang, and Zhongyuan Wang. Emu3: Next-token prediction is all you need. CoRR, abs/2409.18869, 2024. Keming Wu, Yijing Cui, Wenhan Xue, Qijie Wang, Xuan Luo, Zhiyuan Feng, Zuhao Yang, Sudong Wang, Sicong Jiang, Haowei Zhu, Zihan Wang, Ping Nie, Wenhu Chen, and Bin Wang. ","arxiv_match":false},{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"lications. arXiv preprint arXiv:2412.09726, 2024. Fu-Yun Wang, Zhaoyang Huang, Alexander William Bergman, Dazhong Shen, Peng Gao, Michael Lingelbach, Keqiang Sun, Weikang Bian, Guanglu Song, Yu Liu, et al. Phased consistency model. arXiv preprint arXiv:2405.18407, 2024a. Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024b. Yifei Wang, Weimin Bai, Debing Zhang, Weijian Luo, He Sun, et al. Uni-instruct: One-step di","arxiv_match":true}],"id":"2409.18869","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":660},{"title":"Movie gen: A cast of media foundation models","arxiv_id":"2410.13720","year":2024,"url":"https://arxiv.org/abs/2410.13720","citing_submissions":159,"explicit_arxiv_matches":147,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"-a-new-frontier-for-world-models/. https://docs.nvidia.com/cosmos/latest/cosmos3/model NVIDIA, Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, et al. Cosmos World Foundation Model Platform for Physical AI. arXiv preprint arXiv:2501.03575, 2025. URL https://arxiv.org/ abs/2501.03575. Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, et al. Movie Gen: A Cast of Media Foundation Models. arXiv preprint arXiv:2410.13720, 2024. URL https://arxiv.org/abs/2410. 13720. Runjia Qian, Zile Wang, Jihai Zhang, Kai Zou, Wei Yu, Jiaxing Li, Zexiang Liu, Yaokun Li, Fei Kang","arxiv_match":true},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":", 2026b. MiniMax. Minimax h3. minimax-h3-open-source, 2026. https://www.minimax.io/news/ William Peebles and Saining Xie. Scalable diffusion models with transformers. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4172–4182. IEEE, 2023. Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Chih-Yao Ma, Ching-Yao Chuang, et al. Movie gen: A cast of media foundation models. arXiv preprint arXiv:2410.13720, 2024. Haonan Qiu, Menghan Xia, Yong Zhang, Yingqing He, Xintao Wang, Ying Shan, and Ziwei Liu. Fre","arxiv_match":true},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":".25730, 2026. Yu Meng, Xiangyang Luo, Letian Li, Wenyuan Jiang, Chen Gao, Xinlei Chen, Yong Li, and XiaoPing Zhang. Tethercache: Stabilizing autoregressive long-form video generation with gated recall and trusted alignment. arXiv preprint arXiv:2606.13035, 2026. Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Chih-Yao Ma, Ching-Yao Chuang, et al. Movie gen: A cast of media foundation models. arXiv preprint arXiv:2410.13720, 2024. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Gi","arxiv_match":true}],"id":"2410.13720","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":661},{"title":"Reasoning Models Don't Always Say What They Think","arxiv_id":"2505.05410","year":2025,"url":"https://arxiv.org/abs/2505.05410","citing_submissions":159,"explicit_arxiv_matches":150,"examples":[{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"g with sparse autoencoding. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pp. 30297–30305, 2026. doi: 10.1609/aaai.v40i36.40281. Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, Vlad Mikulik, Samuel R. Bowman, Jan Leike, Jared Kaplan, and Ethan Perez. Reasoning Models Don’t Always Say What They Think, 2025. URL https://arxiv.org/abs/2505.05410. James Chua and Owain Evans. Are DeepSeek R1 And Other Reasoning Models More Faithful?, 2025. URL h","arxiv_match":true},{"id":"Dy3RZfaeEs","title":"Do What You Say: Towards Verifiable Reasoning for Scientific Editing","forum_url":"https://openreview.net/forum?id=Dy3RZfaeEs","excerpt":"m W. Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, 2023. URL https: //arxiv.org/abs/2211.12588. Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, Vlad Mikulik, Samuel R. Bowman, Jan Leike, Jared Kaplan, and Ethan Perez. Reasoning models don’t always say what they think, 2025. URL https://arxiv.org/abs/2505.05410. Vishal Dey, Xiao Hu, and Xia Ning. Gellmo: Generalizing large language models for multi-property m","arxiv_match":true},{"id":"I35nG4HR52","title":"When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making","forum_url":"https://openreview.net/forum?id=I35nG4HR52","excerpt":" Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Chris McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022. Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, et al. Reasoning models don’t always say what they think. arXiv preprint arXiv:2505.05410, 2025. Justin Cui, Wei-Lin Chiang, Ion Stoica, and Cho-Jui Hsieh. Or-bench: An over-refusal benchma","arxiv_match":true}],"id":"2505.05410","metadata_source":"Cached arXiv API metadata","rank":662},{"title":"Soap: Improving and stabilizing shampoo using adam","arxiv_id":"2409.11321","year":2024,"url":"https://arxiv.org/abs/2409.11321","citing_submissions":159,"explicit_arxiv_matches":53,"examples":[{"id":"AFB7bSu4ee","title":"Gradient–Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks","forum_url":"https://openreview.net/forum?id=AFB7bSu4ee","excerpt":"r Machine Learning, 2012. URL https://www.cs.toronto.edu/˜hinton/coursera/lecture6/lec6.pdf. Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, and Sham Kakade. Soap: Improving and stabilizing shampoo using adam for language modeling. In International Conference on Learning Representations, volume 2025, pp. 93423–93444, 2025. Sifan Wang, Yujun Teng, and Paris Perdikaris. Understanding and mitigating gradient","arxiv_match":false},{"id":"41Axobxx7a","title":"Scaling Muon for Diffusion Transformers","forum_url":"https://openreview.net/forum?id=41Axobxx7a","excerpt":"ewton-schulz updates for efficient muon optimization. arXiv preprint arXiv:2606.27216, 2026. Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, and Sham Kakade. SOAP: Improving and stabilizing shampoo using adam for language modeling. In International Conference on Learning Representations, volume 2025, pp. 93423–93444, 2025. Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongshen","arxiv_match":false},{"id":"W1kmyDnBQE","title":"DAWN: Scalable Matrix-Whitening Optimization via Spectral Selectivity","forum_url":"https://openreview.net/forum?id=W1kmyDnBQE","excerpt":"on’s spectral design benefits generalization: A study on imbalanced data. 2025. Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi. Powersgd: Practical low-rank gradient compression for distributed optimization. Advances in Neural Information Processing Systems, 32, 2019. Nikhil Vyas, Depen Morwani, Rosie Zhao, Mujin Kwun, Itai Shapira, David Brandfonbrener, Lucas Janson, and Sham Kakade. Soap: Improving and stabilizing shampoo using adam. arXiv preprint arXiv:2409.11321, 2024. Per-Åke Wedin. Perturbation bounds in connection with singular value decomposition. BIT Nume","arxiv_match":true}],"id":"2409.11321","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":663},{"title":"DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models","arxiv_id":"2503.02175","year":2025,"url":"https://arxiv.org/abs/2503.02175","citing_submissions":158,"explicit_arxiv_matches":16,"examples":[{"id":"iBCBaGASs6","title":"HEAT: Hierarchical Elastic Adaptive Token Sparsification via Depth Scaling","forum_url":"https://openreview.net/forum?id=iBCBaGASs6","excerpt":" Saeed Ranjbar Alvar, Gursimran Singh, Mohammad Akbari, and Yong Zhang. Divprune: Diversitybased visual token pruning for large multimodal models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9392–9401, 2025. Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi P","arxiv_match":false},{"id":"Q5WFeaxnf9","title":"ViSparse: Retrieval-Based Visual Attention Sparsification for Multimodal Reasoning","forum_url":"https://openreview.net/forum?id=Q5WFeaxnf9","excerpt":" Saeed Ranjbar Alvar, Gursimran Singh, Mohammad Akbari, and Yong Zhang. Divprune: Diversitybased visual token pruning for large multimodal models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9392–9401. IEEE, 2025. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, W","arxiv_match":false},{"id":"KY3xZQ7lP8","title":"ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding","forum_url":"https://openreview.net/forum?id=KY3xZQ7lP8","excerpt":" Saeed Ranjbar Alvar, Gursimran Singh, Mohammad Akbari, and Yong Zhang. Divprune: Diversity-based visual token pruning for large multimodal models. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9392–9401, 2025. URL https://api.semanticscholar.org/CorpusID:276775957. Shehreen Azad, Vibhav Vineet, and Yogesh Sing","arxiv_match":false}],"id":"2503.02175","metadata_source":"Cached arXiv API metadata","rank":664},{"title":"Empirical Bernstein bounds and sample variance penalization","arxiv_id":"0907.3740","year":2009,"url":"https://arxiv.org/abs/0907.3740","citing_submissions":158,"explicit_arxiv_matches":65,"examples":[{"id":"lzTppWmKLw","title":"Certificate-Guided Learning and Residual-Load Repair in Matching Markets with Delayed Feedback","forum_url":"https://openreview.net/forum?id=lzTppWmKLw","excerpt":"gonekar, and Shankar Sastry. Competing bandits in time varying matching markets. In Learning for Dynamics and Control Conference, pp. 1020–1031. PMLR, 2023. Andreas Maurer and Massimiliano Pontil. In Empirical bernstein bounds and sample variance penalization, 2009. Baharak Rastegari, Anne Condon, Nicole Immorlica, Robert Irving, and Kevin Leyton-Brown. Reasoning about optimal stable matchings under partial information. In Proceedings of the fifteenth ACM","arxiv_match":false},{"id":"GJfdHOgKGE","title":"Skip, Adapt, or Route? Certified Deployment of Test-Time Adaptive Rerankers under Limited Labels","forum_url":"https://openreview.net/forum?id=GJfdHOgKGE","excerpt":"edback with deep language models and dense retrievers: Successes and pitfalls. ACM Transactions on Information Systems, 41(3):1–40, 2023. doi: 10.1145/3570724. Andreas Maurer and Massimiliano Pontil. Empirical Bernstein bounds and sample variance penalization. In Proceedings of the 22nd Annual Conference on Learning Theory (COLT), Montreal, Quebec, Canada, June 2009. URL https://www.cs.mcgill.ca/~colt2009/papers/ 012.pdf. Charles F. Manski. Statistical tr","arxiv_match":false},{"id":"8OeZCBje2W","title":"Precision Thresholds for Shared Evaluation Caches","forum_url":"https://openreview.net/forum?id=8OeZCBje2W","excerpt":"y, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. Holistic evaluation of language models. Transactions on Machine Learning Research, 2023. Andreas Maurer and Massimiliano Pontil. Empirical Bernstein bounds and sample variance penalization. In Proceedings of the 22nd Annual Conference on Learning Theory (COLT), 2009. George L. Nemhauser and Leslie E. Trotter, Jr. Vertex packings: Structural properties and algorithms. Mathematical Progr","arxiv_match":false}],"id":"0907.3740","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":665},{"title":"LightMem: Lightweight and Efficient Memory-Augmented Generation","arxiv_id":"2510.18866","year":2025,"url":"https://arxiv.org/abs/2510.18866","citing_submissions":158,"explicit_arxiv_matches":56,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"emory. arXiv preprint arXiv:2504.19413, 2025. Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi Yao, Mengru Wang, Shuofei Qiao, Huajun Chen, and Ningyu Zhang. LightMem: Lightweight and efficient memory-augmented generation. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=dyJ0GWpjJB. Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu S","arxiv_match":false},{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"g Agents: Learning, Aligning, Evolving, 2026. Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi Yao, Mengru Wang, Shuofei Qiao, Huajun Chen, and Ningyu Zhang. Lightmem: Lightweight and efficient memory-augmented generation. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 98706–98729, 2026. URL https://proceedings.icl","arxiv_match":false},{"id":"a0TQ3mSSY5","title":"NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents","forum_url":"https://openreview.net/forum?id=a0TQ3mSSY5","excerpt":" 2025. URL https://arxiv.org/ abs/2512.20237. Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi Yao, Mengru Wang, Shuofei Qiao, Huajun Chen, and Ningyu Zhang. LightMem: Lightweight and efficient memory-augmented generation. In International Conference on Learning Representations, 2026. URL https://proceedings.iclr.cc/paper_files/paper/2026/ hash/a05b72653ec5b473732129829ae04195-Abstract-Conference.html. Chrisantha Fern","arxiv_match":false}],"id":"2510.18866","metadata_source":"Cached arXiv API metadata","rank":666},{"title":"TheAgentCompany: Benchmarking LLM agents on consequential real world tasks","arxiv_id":"2412.14161","year":2024,"url":"https://arxiv.org/abs/2412.14161","citing_submissions":158,"explicit_arxiv_matches":59,"examples":[{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":" Bao, Zora Wang, Xuhui Zhou, Zhitong Guo, Murong Cao, Mingyang Yang, Hao Yang Lu, Amaad Martin, Zhe Su, Leander Maben, Raj Mehta, Wayne Chi, Lawrence Jang, Yiqing Xie, Shuyan Zhou, and Graham Neubig. TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks. In D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, and N. Chen (eds.), Advances in Neural Information Processing Systems, volume 38, Main Conference. Curran Associates, Inc., 202","arxiv_match":false},{"id":"CFVCzCNC1l","title":"Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=CFVCzCNC1l","excerpt":"g, Xuhui Zhou, Zhitong Guo, Murong Cao, Mingyang Yang, Hao Yang Lu, Amaad Martin, Zhe Su, Leander Melroy Maben, Raj Mehta, Wayne Chi, Lawrence Keunho Jang, Yiqing Xie, Shuyan Zhou, and Graham Neubig. TheAgentCompany: Benchmarking LLM agents on consequential real world tasks. In Advances in Neural Information Processing Systems 38, pp. 10291–10321. Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2025a. doi: 10.52202/085713-0315. Minrui Xu, Zilin Wang, M","arxiv_match":false},{"id":"g1rzEH4l9g","title":"APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces","forum_url":"https://openreview.net/forum?id=g1rzEH4l9g","excerpt":"ural Information Processing Systems, 37:52040–52094, 2024. Frank Fangzheng Xu, Yufan Song, Boxuan Li, Yuxuan Tang, Kritanjali Jain, Mengxue Bao, Zora Wang, Xuhui Zhou, Zhitong Guo, Murong Cao, et al. Theagentcompany: benchmarking llm agents on consequential real world tasks. Advances in Neural Information Processing Systems, 38, 2026. Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. Injecagent: Benchmarking indirect prompt injections in tool-integrated large lan","arxiv_match":false}],"id":"2412.14161","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":667},{"title":"Chatglm: A family of large language models from glm-130b to glm-4 all tools","arxiv_id":"2406.12793","year":2024,"url":"https://arxiv.org/abs/2406.12793","citing_submissions":157,"explicit_arxiv_matches":135,"examples":[{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"iu, Xinyue Yang, Xixuan Song, Xunkai Zhang, Yifan An, Yifan Xu, Yilin Niu, Yuantao Yang, Yueyan Li, Yushi Bai, Yuxiao Dong, Zehan Qi, Zhaoyu Wang, Zhen Yang, Zhengxiao Du, Zhenyu Hou, and Zihan Wang. Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024. Google DeepMind. Gemma 4 Model Card. https://ai.google.dev/gemma/docs/ core/model_card_4, 2026. Accessed: 2026-05-07. Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari","arxiv_match":false},{"id":"fG6gMiEzqt","title":"From Measured Biology to Analysis Decisions: How Far Does Post-Trained Knowledge Travel?","forum_url":"https://openreview.net/forum?id=fG6gMiEzqt","excerpt":"areeb, Benjamin Chang, Ludovico Mitchener, Angela Yiu, Caralyn J Szostkiewicz, Dmytro Shved, Gavin J Gyimesi, Jon M Laurent, Samantha M Wright, Muhammed T Razzak, et al. A multi-agent system for automating scientific discovery. Nature, pp. 1–3, 2026. Team Glm, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Dan Zhang, Diego Rojas, Guanyu Feng, Hanlin Zhao, et al. Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.12793, 2024. Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Petar Sirkovic, Artiom Myaskovsky, ","arxiv_match":true},{"id":"L3kuCiQVS0","title":"Perspective-Conditioned Multimodal Reasoning for E-Commerce Disputes Verdict","forum_url":"https://openreview.net/forum?id=L3kuCiQVS0","excerpt":"urun Yuan and Tengyang Xie. Reinforce LLM reasoning through multi-agent reflection. In Fortysecond International Conference on Machine Learning, pp. 1–31, 2025. Aohan Zeng, Bin Xu, Bowen Wang, et al. ChatGLM: A family of large language models from GLM-130B to GLM-4 all tools. CoRR, abs/2406.12793:1–19, 2024. Jinghui Zhang, Kaiyang Wan, Longwei Xu, Ao Li, Zongfang Liu, and Xiuying Chen. From individuals to crowds: Dual-level public response prediction in social media. In ","arxiv_match":false}],"id":"2406.12793","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":668},{"title":"Eureka: Human-level reward design via coding large language models","arxiv_id":"2310.12931","year":2023,"url":"https://arxiv.org/abs/2310.12931","citing_submissions":157,"explicit_arxiv_matches":34,"examples":[{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"45/3745756.3809204. URL https://doi.org/10.1145/3745756.3809204. Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Jim Fan, and Anima Anandkumar. Eureka: Human-level reward design via coding large language models. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=IEduRUO55F. Ali Maatouk, Fadhel Ayed, Nicola Piovesan, Antonio De Domenico, Merouane De","arxiv_match":false},{"id":"z409Nct6AS","title":"EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL","forum_url":"https://openreview.net/forum?id=z409Nct6AS","excerpt":"aluating Large Video Generation Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. URL https://arxiv.org/abs/2310.11440. Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Eureka: Human-Level Reward Design via Coding Large Language Models. In International Conference on Learning Representations, 2024. URL https://arxiv.org/abs/2310.12931. Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li. HPSv3: Towards Wide-Spectrum Human Preferenc","arxiv_match":true},{"id":"mL3MYH1pYu","title":"Reshaping Guidance Objective with Large Language Models","forum_url":"https://openreview.net/forum?id=mL3MYH1pYu","excerpt":"tive decoding with adaptive token trees. arXiv preprint arXiv:2601.07353, 2026. Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Jim Fan, et al. Eureka: Human-level reward design via coding large language models. In International conference on learning Representations, pp. 26516–26560, 2024. Yurii Nesterov and Vladimir Spokoiny. Random gradient-free minimization of convex functions. Foundations of Computatio","arxiv_match":false}],"id":"2310.12931","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":669},{"title":"Large language models are human-level prompt engineers","arxiv_id":"2211.01910","year":2022,"url":"https://arxiv.org/abs/2211.01910","citing_submissions":157,"explicit_arxiv_matches":55,"examples":[{"id":"a0TQ3mSSY5","title":"NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents","forum_url":"https://openreview.net/forum?id=a0TQ3mSSY5","excerpt":"gents are experiential learners. Proceedings of the AAAI Conference on Artificial Intelligence, 38(17):19632–19642, 2024. doi: 10.1609/aaai.v38i17.29936. URL https: //ojs.aaai.org/index.php/AAAI/article/view/29936. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large language models are human-level prompt engineers. In International Conference on Learning Representations, 2023. URL https://arxiv.org/abs/2211.01910. ","arxiv_match":true},{"id":"wbQy4kEPym","title":"CoEvolve: Traceable Reflective Memory Evolution for Prompt Optimization","forum_url":"https://openreview.net/forum?id=wbQy4kEPym","excerpt":"4. doi: 10.1609/aaai.v38i17.29946. URL https: //ojs.aaai.org/index.php/AAAI/article/view/29946. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large language models are human-level prompt engineers. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id= 92gvk82DE-. Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, ","arxiv_match":false},{"id":"l1fGdveTJc","title":"Relational Response Fields: A General Theory of Black-Box LLM Response Consistency and Recovery","forum_url":"https://openreview.net/forum?id=l1fGdveTJc","excerpt":"oning in large language models. In International Conference on Learning Representations, 2023a. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large language models are human-level prompt engineers. In International Conference on Learning Representations, 2023b. A S UPPLEMENT ROADMAP AND CLAIM LEDGER This supplement is intentionally self-contained. It serves three audiences: readers familiar wi","arxiv_match":false}],"id":"2211.01910","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":670},{"title":"Latent action pretraining from videos","arxiv_id":"2410.11758","year":2024,"url":"https://arxiv.org/abs/2410.11758","citing_submissions":157,"explicit_arxiv_matches":38,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"cial Intelligence, volume 40, pp. 11910–11918, 2026. Seonghyeon Ye, Joel Jang, Byeongguk Jeon, Se June Joo, Jianwei Yang, Baolin Peng, Ajay Mandlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, et al. Latent action pretraining from videos. In International Conference on Learning Representations, volume 2025, pp. 28213–28239, 2025. Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Rep","arxiv_match":false},{"id":"hLTtjANO5B","title":"SpatialLAM: Latent Actions Need Spatial Structure","forum_url":"https://openreview.net/forum?id=hLTtjANO5B","excerpt":"g, Byeongguk Jeon, Sejune Joo, Jianwei Yang, Baolin Peng, Ajay Mandlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, Lars Liden, Kimin Lee, Jianfeng Gao, Luke Zettlemoyer, Dieter Fox, and Minjoon Seo. Latent action pretraining from videos. In ICLR, 2025. Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Ayzaan Wahid, Vikas Sindhwani, and Johnny Lee. Tra","arxiv_match":false},{"id":"ubiZuUXpr6","title":"Copper-Policy: Focus on the Representation for Robust Robot Manipulation","forum_url":"https://openreview.net/forum?id=ubiZuUXpr6","excerpt":", Byeongguk Jeon, Se June Joo, Jianwei Yang, Baolin Peng, Ajay Mandlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, Lars Liden, Kimin Lee, Jianfeng Gao, Luke Zettlemoyer, Dieter Fox, and Minjoon Seo. Latent action pretraining from videos. In Y. Yue, A. Garg, N. Peng, F. Sha, and R. Yu (eds.), International Conference on Learning Representations, volume 2025, pp. 28213–28239, 2025. URL https://proceedings.iclr.cc/paper_files/paper/202","arxiv_match":false}],"id":"2410.11758","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":671},{"title":"Retrieval-augmented generation for large language models: A survey","arxiv_id":"2312.10997","year":2023,"url":"https://arxiv.org/abs/2312.10997","citing_submissions":156,"explicit_arxiv_matches":139,"examples":[{"id":"PEoM2nDAcy","title":"EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation","forum_url":"https://openreview.net/forum?id=PEoM2nDAcy","excerpt":". arXiv preprint arXiv:2510.18866, 2025. Jason Alan Fries, Michael Wornow, Ethan Steinberg, Zepeng Frazier Huo, Hejie Cui, Suhana Bedi, Alyssa Unell, and Nigam Shah. Advancing responsible healthcare ai with longitudinal ehr datasets, 2025. Stanford HAI News. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2(1), 2023. Mark L Graber, Nancy Franklin, and Ruthanna Gordon. Diagnostic error in internal medic","arxiv_match":true},{"id":"3FIUVX3ffG","title":"DatasetResearch: Benchmarking LLM Agents For Demand-Driven Dataset Discovery","forum_url":"https://openreview.net/forum?id=3FIUVX3ffG","excerpt":"r long-horizon task training. arXiv preprint arXiv:2510.27630, 2025. Saumya Gandhi, Ritu Gala, Vijay Viswanathan, Tongshuang Wu, and Graham Neubig. Better synthetic data by retrieving and transforming existing datasets. arXiv preprint arXiv:2404.14361, 2024. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2(1), 2023. Google. Gemini-deepresearch, 2025. deep-research/?hl=en. URL https://gemini.google/ove","arxiv_match":true},{"id":"c8pOKbbPaZ","title":"MoRAG: Bringing Graph-Based Retrieval-Augmented Generation to Real Smartphones","forum_url":"https://openreview.net/forum?id=c8pOKbbPaZ","excerpt":"Accurate post-training quantization for generative pre-trained transformers. In Proceedings of the International Conference on Learning Representations, 2023. Yunfan Gao, Yun Xiong, Xinyu Gao, et al. Retrieval-augmented generation for large language models: A survey, 2023. Zirui Guo, Lianghao Xia, Yanhua Yu, et al. LightRAG: Simple and fast retrieval-augmented generation. In Findings of the Association for Computational Linguistics: EMNLP 2025, 2025. Bernal Jimé","arxiv_match":false}],"id":"2312.10997","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":672},{"title":"SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills","arxiv_id":"2504.07079","year":2025,"url":"https://arxiv.org/abs/2504.07079","citing_submissions":156,"explicit_arxiv_matches":144,"examples":[{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"rew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Boyuan Zheng, Michael Y Fatemi, Xiaolong Jin, Zora Zhiruo Wang, Apurva Gandhi, Yueqi Song, Yu Gu, Jayanth Srinivasa, Gaowen Liu, Graham Neubig, et al. Skillweaver: Web agents can self-improve by discovering and honing skills. arXiv preprint arXiv:2504.07079, 2025. Longtao Zheng, Rundong Wang, Xinrun Wang, and Bo An. Synapse: Trajectory-as-exemplar prompti","arxiv_match":true},{"id":"ZSRMEDG6G8","title":"Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=ZSRMEDG6G8","excerpt":"e Janeiro, Brazil, 2026. Qingfei Zhao, Ruobing Wang, Dingling Xu, Daren Zha, and Limin Liu. R-search: Empowering llm reasoning with search via multi-reward reinforcement learning. arXiv preprint arXiv:2506.04185, 2025. Boyuan Zheng, Michael Y. Fatemi, Xiaolong Jin, Zora Zhiruo Wang, Apurva Gandhi, Yueqi Song, Yu Gu, Jayanth Srinivasa, Gaowen Liu, Graham Neubig, and Yu Su. Skillweaver: Web agents can self-improve by discovering and honing skills. arXiv preprint arXiv:2504.07079, 2025. YanZhao Zheng, ZhenTao Zhang, Chao Ma, YuanQiang Yu, JiHuai Zhu, Yong Wu, Tianze Xu, Baohua ","arxiv_match":true},{"id":"835acKoDx9","title":"DL-OPSD: Dual-Level Selective On-Policy Self-Distillation for Skill Internalization","forum_url":"https://openreview.net/forum?id=835acKoDx9","excerpt":"20757, 2022. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover. Self-distilled reasoner: On-policy self-distillation for large language models. arXiv preprint arXiv:2601.18734, 2026. Boyuan Zheng, Michael Y Fatemi, Xiaolong Jin, Zora Zhiruo Wang, Apurva Gandhi, Yueqi Song, Yu Gu, Jayanth Srinivasa, Gaowen Liu, Graham Neubig, et al. Skillweaver: Web agents can self-improve by discovering and honing skills. arXiv preprint arXiv:2504.07079, 2025. ","arxiv_match":true}],"id":"2504.07079","metadata_source":"Cached arXiv API metadata","rank":673},{"title":"Umap: Uniform manifold approximation and projection for dimension reduction","arxiv_id":"1802.03426","year":2018,"url":"https://arxiv.org/abs/1802.03426","citing_submissions":156,"explicit_arxiv_matches":145,"examples":[{"id":"o6HcWPCLiL","title":"GTS: Gap-Targeted Synthesis for Budget-Efficient Synthetic Data Scaling","forum_url":"https://openreview.net/forum?id=o6HcWPCLiL","excerpt":"ewed federated learning. arXiv preprint arXiv:2607.06616, 2026. URL https://arxiv.org/abs/2607.06616. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. In ICLR, 2023. URL https://arxiv.org/abs/2210.02747. Leland McInnes, John Healy, and James Melville. UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018. URL https: //arxiv.org/abs/1802.03426. Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre ","arxiv_match":true},{"id":"8vPRaoD8i0","title":"Causal Attribution from Video via Counterfactual Estimation","forum_url":"https://openreview.net/forum?id=8vPRaoD8i0","excerpt":"dal. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1–3):503–528, 1989. doi: 10.1007/BF01589116. Zitong Lu, Zhi Geng, Wei Li, Shengyu Zhu, and Jinzhu Jia. Evaluating causes of effects by posterior effects of causes. Biometrika, 110(2):449–465, 2023. doi: 10.1093/biomet/asac038. Leland McInnes, John Healy, and James Melville. UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018. OpenAI. GPT-5.5 model. https://developers.openai.com/api/docs/models/ gpt-5.5, 2026a. Access","arxiv_match":true},{"id":"wJdmPx189t","title":"Reconstructing is Not Acting: Action-Centric Latent Dynamics Modeling","forum_url":"https://openreview.net/forum?id=wJdmPx189t","excerpt":"m unlabeled demonstrations. arXiv preprint arXiv:2505.04999, 2025. Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2017. Mengya Liu, Baoxiong Jia, Jiangyong Huang, Jingze Zhang, and Siyuan Huang. Lara: Latent action representation alignment for vision-language-action models. In ICML, 2026. Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018. Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta. R3m: A univer","arxiv_match":true}],"id":"1802.03426","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":674},{"title":"V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning","arxiv_id":"2603.14482","year":2026,"url":"https://arxiv.org/abs/2603.14482","citing_submissions":156,"explicit_arxiv_matches":118,"examples":[{"id":"8p5UQCDo47","title":"Align2Refine: Diagnosing and Refining Video Representations through Spatial Latent Alignment","forum_url":"https://openreview.net/forum?id=8p5UQCDo47","excerpt":"Foundation_Models_ICCV_ 2023_paper.html. Rui Li and Dong Liu. Spatial-then-temporal self-supervised learning for video correspondence. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2279– 2288. IEEE, 2023. Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar, Mido Assran, Koustuv Sinha, Mike Rabbat, Yann LeCun, Nicolas Ballas, and Adrien Bardes. V-jepa 2.1: Unlocking dense features in video self-supervised learning. arXiv preprint arXiv:2603.14482, 2026. Jisu Nam, Soowon Son, Dahyun Chung, Jiyoung Kim, Siyoon Jin, Junhwa Hur, and Seungryong Kim.","arxiv_match":true},{"id":"k4AeVmHwTU","title":"Generation-Aware Latent Compression for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=k4AeVmHwTU","excerpt":"https://arxiv.org/abs/2502.10248. Aniruddha Mahapatra, Long Mai, Yitian Zhang, David Bourgin, and Feng Liu. Progressive growing of video tokenizers for highly compressed latent spaces. arXiv preprint arXiv:2501.05442, 2025. Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar, Mido Assran, Koustuv Sinha, Mike Rabbat, Yann LeCun, Nicolas Ballas, and Adrien Bardes. V-jepa 2.1: Unlocking dense features in video self-supervised learning, 2026. URL https://arxiv.org/abs/2603.14482. NVIDIA, :, Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithv","arxiv_match":true},{"id":"GZdwmSCjJs","title":"Juno: Taming Predictive Latents for Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=GZdwmSCjJs","excerpt":"re from pixels. arXiv preprint arXiv:2603.19312, 2026. Shangchen Miao, Ningya Feng, Jialong Wu, Ye Lin, Xu He, Dong Li, and Mingsheng Long. Jepa-vla: Video predictive embedding is needed for vla models. arXiv preprint arXiv:2602.11832, 2026. Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar, Mido Assran, Koustuv Sinha, Mike Rabbat, Yann LeCun, Nicolas Ballas, and Adrien Bardes. V-jepa 2.1: Unlocking dense features in video self-supervised learning. arXiv preprint arXiv:2603.14482, 2026. NVIDIA, Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi \"J","arxiv_match":true}],"id":"2603.14482","metadata_source":"Cached arXiv API metadata","rank":675},{"title":"Beyond the imitation game: Quantifying and extrapolating the capabilities of language models","arxiv_id":"2206.04615","year":2022,"url":"https://arxiv.org/abs/2206.04615","citing_submissions":155,"explicit_arxiv_matches":37,"examples":[{"id":"8zRnR4jkT9","title":"HealthCoreBench: Measuring Evaluation Artifacts for Reliable Medical LLM Evaluation","forum_url":"https://openreview.net/forum?id=8zRnR4jkT9","excerpt":"Kamyar Rajabalifardi, Fei-Fei Li, Ehsan Adeli, and Euan Ashley. Mirage: The illusion of visual understanding. arXiv preprint arXiv:2603.21687, 2026. BIG-Bench Collaboration, Aarohi Srivastava, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research, 2023. URL https://openreview.net/forum?id=uyTL5Bvosj. Min Cao et al. OpenCompass: A universal evaluation platform for large language models. arXiv preprint","arxiv_match":false},{"id":"rHVNuGP2yx","title":"Latent Thought Flow: Efficient Latent Reasoning in Large Language Models","forum_url":"https://openreview.net/forum?id=rHVNuGP2yx","excerpt":"eprint arXiv:2601.03267, 2025. 1 Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions on machine learning research, 2023. 7 Guohao Sun, Hang Hua, Jian Wang, Jiebo Luo, Sohail Dianat, Majid Rabbani, Raghuveer Rao, and Zhiqiang Tao. Latent chain-of-thought for visual reaso","arxiv_match":false},{"id":"CONFJT1Bkx","title":"Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning","forum_url":"https://openreview.net/forum?id=CONFJT1Bkx","excerpt":"u Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Sophie Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, and Ziyi Wu. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL https://openreview.net/forum?id=uyTL5Bvosj. Featured Certification. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Geh","arxiv_match":false}],"id":"2206.04615","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":676},{"title":"GSM-Symbolic: Understanding the limitations of mathematical reasoning in large language models","arxiv_id":"2410.05229","year":2024,"url":"https://arxiv.org/abs/2410.05229","citing_submissions":155,"explicit_arxiv_matches":53,"examples":[{"id":"mSO30AmGJA","title":"Solve, Recover, Reuse: LLM Reasoning Across Workflows with Corrupted Contexts","forum_url":"https://openreview.net/forum?id=mSO30AmGJA","excerpt":" In International Conference on Learning Representations, volume 2026, pp. 54738–54778, 2026. Iman Mirzadeh, Keivan Alizadeh-Vahid, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar. Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models. In International Conference on Learning Representations, volume 2025, pp. 94743–94765, 2025. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhi","arxiv_match":false},{"id":"oWnpJoClPs","title":"$\\textit{seq2cause}$: A Unified Framework for Causal Discovery in Event Sequences","forum_url":"https://openreview.net/forum?id=oWnpJoClPs","excerpt":"cal Reasoning of Large Language Models, 2026. URL https://openreview.net/forum?id=M5ZszfsJxm. Seyed Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar. GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=AjXkRZIvjB. Meike Nauta, Doina Bucur, and Christin Seifert. Causal discovery with at","arxiv_match":false},{"id":"7Et5MDmRRV","title":"Beyond Accuracy: Re-evaluating Failures in LLM Physics Reasoning","forum_url":"https://openreview.net/forum?id=7Et5MDmRRV","excerpt":"all in one model, May 2026. URL https://www.minimax.io/blog/minimax-m3. Accessed: 2026-09-24. Seyed Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar. GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=AjXkRZIvjB. Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and ","arxiv_match":false}],"id":"2410.05229","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":677},{"title":"Hierarchical textconditional image generation with CLIP latents","arxiv_id":"2204.06125","year":2022,"url":"https://arxiv.org/abs/2204.06125","citing_submissions":155,"explicit_arxiv_matches":139,"examples":[{"id":"IdwA2KX2G3","title":"StateWise: From Noise Awareness to State-Calibrated MLLM Conditioning for Unified Generation","forum_url":"https://openreview.net/forum?id=IdwA2KX2G3","excerpt":"s, 2024. Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang, Yi Jiang, Yiming Gao, Hu Ye, Daniel K Du, Zehuan Yuan, and Xinglong Wu. Tokenflow: Unified image tokenizer for multimodal understanding and generation. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2545–2555. IEEE, 2025. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical textconditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. H","arxiv_match":true},{"id":"HYY82JXv09","title":"BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generation","forum_url":"https://openreview.net/forum?id=HYY82JXv09","excerpt":"25, 2024. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 8748–8763. PMLR, 2021. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical textconditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams. Xcu","arxiv_match":true},{"id":"8XSTiawhII","title":"CortexLink: Reconstructing Long Videos from fMRI with Event-Level Semantic Consensus and Continuous Geometry","forum_url":"https://openreview.net/forum?id=8XSTiawhII","excerpt":":9, 2019. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 8748–8763. PmLR, 2021. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical textconditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022. Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xin","arxiv_match":true}],"id":"2204.06125","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":678},{"title":"Mmmu-pro: A more robust multi-discipline multimodal understanding benchmark","arxiv_id":"2409.02813","year":2024,"url":"https://arxiv.org/abs/2409.02813","citing_submissions":155,"explicit_arxiv_matches":38,"examples":[{"id":"Bb2HSkeepN","title":"An Exam for Active Observers","forum_url":"https://openreview.net/forum?id=Bb2HSkeepN","excerpt":"ks. arXiv preprint arXiv:2606.29537, 2026. Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, and Graham Neubig. MMMUPro: A more robust multi-discipline multimodal understanding benchmark. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15050–15070. Association for Computational Linguistics, 2025. URL https: //acl","arxiv_match":false},{"id":"6N8dYXmsNk","title":"Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=6N8dYXmsNk","excerpt":"g benchmark for expert agi. In CVPR, 2024. Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, and Graham Neubig. Mmmu-pro: A more robust multi-discipline multimodal understanding benchmark. In ACL, 2025. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. STar: Bootstrapping reasoning with reasoning. In NeurIPS, 2022. Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, and","arxiv_match":false},{"id":"09osI0HV7E","title":"Weak-to-Strong On-Policy Distillation via Perception Gap Supervision","forum_url":"https://openreview.net/forum?id=09osI0HV7E","excerpt":"odal understanding and reasoning benchmark for expert agi. In CVPR, 2024. Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, et al. Mmmu-pro: A more robust multi-discipline multimodal understanding benchmark. In ACL, 2025. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diag","arxiv_match":false}],"id":"2409.02813","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":679},{"title":"Unleashing large-scale video generative pre-training for visual robot manipulation","arxiv_id":"2312.13139","year":2023,"url":"https://arxiv.org/abs/2312.13139","citing_submissions":155,"explicit_arxiv_matches":40,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"odeling. arXiv preprint arXiv:2507.07982, 2025. URL https://arxiv.org/abs/2507.07982. Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and Tao Kong. Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation. In International Conference on Learning Representations, pp. 10641–10662, 2024. URL https://proceedings.iclr.cc/paper_files/paper/2024/hash/ 2c37c5bcef24b9541550261dcd63261b-Abstract-Conference.html","arxiv_match":false},{"id":"dthpKuMMBX","title":"A2DB: Affordance as a Distillation Bottleneck for Compressing Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dthpKuMMBX","excerpt":"dation model for functionality understanding. arXiv preprint arXiv:2606.02551, 2026b. Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and Tao Kong. Unleashing large-scale video generative pre-training for visual robot manipulation. In International Conference on Learning Representations, volume 2024, pp. 10641–10662, 2024. Wenli Xiao, Haotian Lin, Andy Peng, Haoru Xue, Tairan He, Zhengyi Luo, Yuqi Xie, Fengyuan Hu, Jim Fan, Gu","arxiv_match":false},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"ion models: The next frontier in embodied ai. arXiv preprint arXiv:2605.12090, 2026b. Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and Tao Kong. Unleashing large-scale video generative pre-training for visual robot manipulation. In International Conference on Learning Representations, volume 2024, pp. 10641–10662, 2024. Wei Wu, Fan Lu, Yunnan Wang, Shuai Yang, Shi Liu, Fangjing Wang, Qian Zhu, He Sun, Yong Wang, Shuailei Ma","arxiv_match":false}],"id":"2312.13139","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":680},{"title":"LLM.int8(): 8-bit matrix multiplication for transformers at scale","arxiv_id":"2208.07339","year":2022,"url":"https://arxiv.org/abs/2208.07339","citing_submissions":154,"explicit_arxiv_matches":44,"examples":[{"id":"hAJ2b6a8w1","title":"CoCurve: Cross-Module Co-Pruning Curvature for Structured LLM Pruning","forum_url":"https://openreview.net/forum?id=hAJ2b6a8w1","excerpt":" Oyvind Tafjord. Think you have solved question answering? try ARC, the AI2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018. Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. LLM.int8(): 8-bit matrix multiplication for transformers at scale. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. QLoRA: Efficient finetuning of quantized LLMs. In Advances i","arxiv_match":false},{"id":"JNo1goQAdN","title":"GRIK: Grouped Relevance-Integrated Key Pruning for Grouped-Query Attention LLMs","forum_url":"https://openreview.net/forum?id=JNo1goQAdN","excerpt":"t understanding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 3119–3137, 2024. Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. LLM.int8(): 8-bit matrix multiplication for transformers at scale. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Yuan Feng, Junlin Lv, Yukun Cao, Xike Xie, and S. Kevin Zhou. Ada-KV: Optimizing KV cache eviction by adaptive budget allocation","arxiv_match":false},{"id":"JINWOT9zWh","title":"Open-Weight Masked Introspection: Measuring What Language Models Can Report About Their Own Computation","forum_url":"https://openreview.net/forum?id=JINWOT9zWh","excerpt":"d highly interpretable features in language models. In International Conference on Learning Representations (ICLR), 2024. arXiv:2309.08600. Emilio Ferrara. Defeat devices in AI systems. Future Internet, 18(7):339, 2026a. doi: 10.3390/fi18070339. Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. LLM.int8(): 8-bit matrix multiplication for transformers at scale. In Advances in Neural Information Processing Systems 35 (NeurIPS), 2022. arXiv:2208.07339. Emilio Ferrara. QuantiBias: Benchmarking quantization-induced bias in LLMs, 2026b. arXiv:2607.2106","arxiv_match":true}],"id":"2208.07339","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":681},{"title":"Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes","arxiv_id":"2603.25562","year":2026,"url":"https://arxiv.org/abs/2603.25562","citing_submissions":154,"explicit_arxiv_matches":134,"examples":[{"id":"AVKgs60Glj","title":"Flux-OPD: On-Policy Distillation with Evolving Contexts","forum_url":"https://openreview.net/forum?id=AVKgs60Glj","excerpt":" Lyu, Xiaotao Gu, Xiao Liu, Jiazheng Xu, Yida Lu, Jiayan Teng, Zhuoyi Yang, Yuxiao Dong, Jie Tang, et al. Vpo: Aligning text-to-video generation models with prompt optimization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15636–15645, 2025. Yuqian Fu, Haohuan Huang, Kaiwen Jiang, Jiacai Liu, Zhuo Jiang, Yuanheng Zhu, and Dongbin Zhao. Revisiting on-policy distillation: Empirical failure modes and simple fixes. arXiv preprint arXiv:2603.25562, 2026. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. Minillm: Knowledge distillation of large lan","arxiv_match":true},{"id":"IkzBak1MYU","title":"RL Starts Before RL: Rethinking Policy Initialization for Outcome-Based RL","forum_url":"https://openreview.net/forum?id=IkzBak1MYU","excerpt":"learning. arXiv preprint arXiv:2501.12948, 2025. URL https: //arxiv.org/abs/2501.12948. Bradley Efron. Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1): 1–26, 1979. doi: 10.1214/aos/1176344552. Yuqian Fu, Haohuan Huang, Kaiwen Jiang, Jiacai Liu, Zhuo Jiang, Yuanheng Zhu, and Dongbin Zhao. Revisiting on-policy distillation: Empirical failure modes and simple fixes. arXiv preprint arXiv:2603.25562, 2026. URL https://arxiv.org/abs/2603.25562. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. MiniLLM: Knowledge distillation of large language ","arxiv_match":true},{"id":"4Dg0z4hCoY","title":"Scaling Properties of Capability Transfer in Same-Family On-Policy Distillation","forum_url":"https://openreview.net/forum?id=4Dg0z4hCoY","excerpt":"to-strong generalization via direct on-policy distillation, 2026. URL https://arxiv.org/abs/2607.05394. Benoît Frénay and Michel Verleysen. Classification in the presence of label noise: a survey. IEEE Transactions on Neural Networks and Learning Systems, 25(5):845–869, 2014. Yuqian Fu, Haohuan Huang, Kaiwen Jiang, Jiacai Liu, Zhuo Jiang, Yuanheng Zhu, and Dongbin Zhao. Revisiting on-policy distillation: Empirical failure modes and simple fixes. arXiv preprint arXiv:2603.25562, 2026. Leo Gao, John Schulman, and Jacob Hilton. Scaling laws for reward model overoptimization. In","arxiv_match":true}],"id":"2603.25562","metadata_source":"Cached arXiv API metadata","rank":682},{"title":"TabICLv2: A better, faster, scalable, and open tabular foundation model","arxiv_id":"2602.11139","year":2026,"url":"https://arxiv.org/abs/2602.11139","citing_submissions":154,"explicit_arxiv_matches":93,"examples":[{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":" TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. In International Conference on Machine Learning, 2025. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICLv2: A better, faster, scalable, and open tabular foundation model. In International Conference on Machine Learning, 2026. Theodore Rogers and Srividya Ponnada. Zero-Shot Bayesian Optimization with TabPFN: Competitive with State-of-the-Art without Per-Task Training.","arxiv_match":false},{"id":"mZ3FfjH6Gm","title":"Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=mZ3FfjH6Gm","excerpt":" In Advances in Neural Information Processing Systems, 2018. Lennart Purucker. Private communication, 2026. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICL: A tabular foundation model for in-context learning on large data. In International Conference on Machine Learning, 2025. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICLv2: A better, faster, scalable, and open tabular foundation model. arXiv:2602.11139, 2026. Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu, Rasa Hosseinzadeh, Zhaoyan Liu, George Ste","arxiv_match":true},{"id":"wSHvuy1308","title":"Training Fair Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=wSHvuy1308","excerpt":"Learning on Large Data. In Proceedings of the 42nd International Conference on Machine Learning, volume 267, pp. 50817–50847, 2025. Jingang Qu, David Holzmüller, Gaël Varoquaux, and Marine Le Morvan. TabICLv2: A better, faster, scalable, and open tabular foundation model. In Forty-third International Conference on Machine Learning, 2026. Jake Robertson, Noah Hollmann, Samuel Müller, Noor Awad, and Frank Hutter. FairPFN: A Tabular Foundation Model for Causal Fairness.","arxiv_match":false}],"id":"2602.11139","metadata_source":"Cached arXiv API metadata","rank":683},{"title":"CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation","arxiv_id":"2502.21074","year":2025,"url":"https://arxiv.org/abs/2502.21074","citing_submissions":153,"explicit_arxiv_matches":33,"examples":[{"id":"AaaogEZLc6","title":"Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization","forum_url":"https://openreview.net/forum?id=AaaogEZLc6","excerpt":".11851, 2025. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36:68539–68551, 2023. Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, and Yulan He. Codi: Compressing chain-of-thought into continuous space via self-distillation. arXiv preprint arXiv:2502.21074, 2025. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion","arxiv_match":true},{"id":"6kjTlyCrof","title":"Agent Harness Distillation: Inference-Time Harness Extraction and Exploitation in Autonomous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=6kjTlyCrof","excerpt":"al Linguistics. ISBN 979-8-89176-251-0. doi: 10.18653/v1/2025.acl-long.476. URL https://aclanthology. org/2025.acl-long.476/. Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, and Yulan He. CODI: Compressing chain-of-thought into continuous space via self-distillation. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 677–693, Suzhou,","arxiv_match":false},{"id":"2LN3rZeePs","title":"Latent Bridge: Asking for Help Without Words","forum_url":"https://openreview.net/forum?id=2LN3rZeePs","excerpt":" google-proof q&a benchmark. In First Conference on Language Modeling, 2024. URL https://openreview. net/forum?id=Ti67584b98. Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, and Yulan He. Codi: Compressing chain-of-thought into continuous space via self-distillation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 677–693, 2025. Haoxiang Sun, Yingqian Min, Zhipeng Chen, Xin Zhao, and Ji-Rong Wen. Challenging the bou","arxiv_match":false}],"id":"2502.21074","metadata_source":"Cached arXiv API metadata","rank":684},{"title":"Datacomp-lm: In search of the next generation of training sets for language models","arxiv_id":"2406.11794","year":2024,"url":"https://arxiv.org/abs/2406.11794","citing_submissions":153,"explicit_arxiv_matches":37,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"ari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, and Vaishaal Shankar. Datacomp-lm: In search of the next generation of training sets for language models, 2024b. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2023. Yanghao Li, Rui Q","arxiv_match":false},{"id":"Fhj95cY1wD","title":"Decoupling Token Roles in Autoregressive Pretraining","forum_url":"https://openreview.net/forum?id=Fhj95cY1wD","excerpt":"se knowledge in LLMs. arXiv preprint arXiv:2607.18639, 2026. Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, et al. DataComp-LM: In search of the next generation of training sets for language models. In Advances in Neural Information Processing Systems, Datasets and Benchmarks Track, 2024a. Tianjian Li, Haoran Xu, Philipp Koehn, Daniel Khashabi, and Kenton Murray. Error norm truncation: Robust t","arxiv_match":false},{"id":"83QM0tLiCR","title":"Scaling Domain Data Repetition in LLM Pretraining","forum_url":"https://openreview.net/forum?id=83QM0tLiCR","excerpt":"tems, 2025. URL https://openreview.net/forum? id=dpllevHMbc. Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, et al. Datacomp-lm: In search of the next generation of training sets for language models. Advances in Neural Information Processing Systems, 37:14200–14282, 2024. R Li, LB Allal, Y Zi, N Muennighoff, D Kocetkov, C Mou, M Marone, C Akiki, J Li, J Chim, et al. Starcoder: May the source be ","arxiv_match":false}],"id":"2406.11794","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":685},{"title":"Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses","arxiv_id":"2604.25850","year":2026,"url":"https://arxiv.org/abs/2604.25850","citing_submissions":152,"explicit_arxiv_matches":140,"examples":[{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"026. doi: 10. 48550/ARXIV.2604.13016. URL https://doi.org/10.48550/arXiv.2604.13016. Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Xuanjing Huang, Hang Yan, Zhenhua Han, and Tao Gui. Agentic harness engineering: Observability-driven automatic evolution of coding-agent harnesses. CoRR, abs/2604.25850, 2026a. doi: 10.48550/ARXIV.2604.25850. URL https://doi.org/10.48550/arXiv.2604.25850. Minhua Lin, Hanqing Lu, Zhan Shi, Bing He, Rui Mao, Zhiwei Zhang, Zongyu Wu, Xianfeng Tang","arxiv_match":false},{"id":"YAVRc0xfa9","title":"Game-Evolver: Dual-Loop Evolution Elicits Better End-to-End Game Orchestration","forum_url":"https://openreview.net/forum?id=YAVRc0xfa9","excerpt":"mmender systems. arXiv preprint arXiv:2606.26859, 2026. Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, and Chelsea Finn. Metaharness: End-to-end optimization of model harnesses. arXiv preprint arXiv:2603.28052, 2026. Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Xuanjing Huang, Hang Yan, Zhenhua Han, and Tao Gui. Agentic harness engineering: Observability-driven automatic evolution of coding-agent harnesses. arXiv preprint arXiv:2604.25850, 2026a. Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem.","arxiv_match":true},{"id":"GGUmZybpbd","title":"AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?","forum_url":"https://openreview.net/forum?id=GGUmZybpbd","excerpt":"Chen, Bingran You, Zonglin Di, Yifeng He, Shenghan Zheng, Kyoung Whan Choe, Jiankai Sun, Shuyi Wang, et al. Skillsbench: Benchmarking how well agent skills work across diverse tasks. arXiv preprint arXiv:2602.12670, 2026b. Jiahang Lin, Shichun Liu, Chengjun Pan, Lizhi Lin, Shihan Dou, Zhiheng Xi, Xuanjing Huang, Hang Yan, Zhenhua Han, Tao Gui, et al. Agentic harness engineering: Observability-driven automatic evolution of coding-agent harnesses. arXiv preprint arXiv:2604.25850, 2026a. Minhua Lin, Hanqing Lu, Zhan Shi, Bing He, Rui Mao, Zhiwei Zhang, Zongyu Wu, Xianfeng Tang,","arxiv_match":true}],"id":"2604.25850","metadata_source":"Cached arXiv API metadata","rank":686},{"title":"Directly fine-tuning diffusion models on differentiable rewards","arxiv_id":"2309.17400","year":2023,"url":"https://arxiv.org/abs/2309.17400","citing_submissions":152,"explicit_arxiv_matches":49,"examples":[{"id":"tSpuyBaWWf","title":"Displacement Matching: One Objective for Reward Learning and Distillation in Few-Step Generators","forum_url":"https://openreview.net/forum?id=tSpuyBaWWf","excerpt":"ingo-Enrich, and Jakiw Pidstrigach. Reinforce adjoint matching: Scaling rl post-training of diffusion and flow-matching models, 2026. Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. In International Conference on Learning Representations, 2024. Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet. Directly fine-tuning diffusion models on differentiable rewards. arXiv preprint arXiv:2309.17400, 2023. Linwei Dong, Ruoyu Guo, Ge Bai, Zehuan Yuan, Yawei Luo, and Changqing Zou. Guiding distribut","arxiv_match":true},{"id":"U6seufWPDg","title":"AMINO: Generative Auto-bidding with Multimodal Trajectory Modeling and Initial Noise Optimization","forum_url":"https://openreview.net/forum?id=U6seufWPDg","excerpt":"rmer: Reinforcement learning via sequence modeling. In Advances in Neural Information Processing Systems, volume 34, pp. 15084–15097, 2021. Kevin Clark, Paul Vicol, Kevin Swersky, and David J. Fleet. Directly fine-tuning diffusion models on differentiable rewards. In The Twelfth International Conference on Learning Representations, 2024. Miduo Cui, Haochen Wang, Shangqin Mao, Xun Yang, Qianlong Xie, Xingxing Wang, Xuri Ge, Ying Zhou, and Zhiwei Xu. DRIVE: Dis","arxiv_match":false},{"id":"zdCDVB1RA8","title":"STAR: Selective Timestep Anchor Reward for Identity-Preserving Video Generation","forum_url":"https://openreview.net/forum?id=zdCDVB1RA8","excerpt":" Kevin Clark, Paul Vicol, Kevin Swersky, and David J. Fleet. Directly fine-tuning diffusion models on differentiable rewards. In International Conference on Learning Representations (ICLR), 2024. Thomas Coste, Usman Anwar, Robert Kirk, and David Krueger. Reward model ensembles help mitigate overoptimization. In Internation","arxiv_match":false}],"id":"2309.17400","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":687},{"title":"IP-Adapter: Text compatible image prompt adapter for text-to-image diffusion models","arxiv_id":"2308.06721","year":2023,"url":"https://arxiv.org/abs/2308.06721","citing_submissions":152,"explicit_arxiv_matches":142,"examples":[{"id":"ItanNdt5x7","title":"WithEveryone: Unified Planning and Identity Grounding for Group Image Generation","forum_url":"https://openreview.net/forum?id=ItanNdt5x7","excerpt":"Ermon, and Bin Cui. Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms. In Icml, volume 3, pp. 7, 2024. Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. 2023. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference on computer vision, pp","arxiv_match":false},{"id":"ce0PwbWId1","title":"SCULPT: Subtractive Composition for 3D Part Generation","forum_url":"https://openreview.net/forum?id=ce0PwbWId1","excerpt":"7.3763872. Yunhan Yang, Yuan-Chen Guo, Yukun Huang, Zi-Xin Zou, Zhipeng Yu, Yangguang Li, Yan-Pei Cao, and Xihui Liu. Holopart: Generative 3d part amodal segmentation. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum? id=2VsBJwefDC. Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models, 2023. URL https://arxiv.org/abs/2308. 06721. Mingqiao Ye, Martin Danelljan, Fisher Yu, and Lei Ke. Gaussian grouping: Segment and edit anything","arxiv_match":true},{"id":"SB1sZgVxRZ","title":"MoLaGen: Multimodal Conditional Layout Control for Image Generation","forum_url":"https://openreview.net/forum?id=SB1sZgVxRZ","excerpt":" on Machine Learning, 2024. Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, et al. Reco: Region-controlled text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14246–14255, 2023. Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.06721, 2023. Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris ","arxiv_match":true}],"id":"2308.06721","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":688},{"title":"MetaMath: Bootstrap your own mathematical questions for large language models","arxiv_id":"2309.12284","year":2023,"url":"https://arxiv.org/abs/2309.12284","citing_submissions":152,"explicit_arxiv_matches":57,"examples":[{"id":"K9wqTXAYns","title":"LLARA: Low-Rank Linear Attention Residual Adaptation for Large Language Models","forum_url":"https://openreview.net/forum?id=K9wqTXAYns","excerpt":"cture. In International Conference on Machine Learning. PMLR, 2020. Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. MetaMath: Bootstrap your own mathematical questions for large language models. In International Conference on Learning Representations, 2024. Biao Zhang and Rico Sennrich. Root mean square layer normalization. In Advances in Neural Information Processing Systems, 2019. Renrui ","arxiv_match":false},{"id":"pZW7kmIsBq","title":"DeIRD: A Decoupled Continuous Implicit Reasoning Framework for Dialogue Models","forum_url":"https://openreview.net/forum?id=pZW7kmIsBq","excerpt":"iyan Su, Xiaocheng Zou, Wei Feng, Dwarikanath Mahapatra, Yifan Peng, Mingquan Lin, and Zongyuan Ge. Thinking in uncertainty: Mitigating hallucinations in mlrms with latent entropy-aware decoding, 2026. URL https://arxiv.org/abs/2603.13366. Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284, 2023. Jun Zhan, Mingyang Han, Yuxuan Xie, Chen Wang, Dong Zhang, Kexin Huang, Haoxiang Shi, DongXi","arxiv_match":true},{"id":"bw7hexJ2fs","title":"Training-Free Transformer Merging via Sequential Local Operator Alignment","forum_url":"https://openreview.net/forum?id=bw7hexJ2fs","excerpt":"veys, 58(8):1–41, 2026. Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. Language models are super mario: Absorbing abilities from homologous models as a free lunch. In Forty-first International Conference on Machine Learning, 2024. Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284, 2023. Binchi Zhang, Zaiyi Zheng, Zhengzhang Chen, and Jundong Li. Beyond the permutation symmetry ","arxiv_match":true}],"id":"2309.12284","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":689},{"title":"Sparse feature circuits: Discovering and editing interpretable causal graphs in language models","arxiv_id":"2403.19647","year":2024,"url":"https://arxiv.org/abs/2403.19647","citing_submissions":152,"explicit_arxiv_matches":40,"examples":[{"id":"iksnFm6vFW","title":"Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage","forum_url":"https://openreview.net/forum?id=iksnFm6vFW","excerpt":"rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6025–6035, 2025. Samuel Marks, Can Rager, Eric Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller. Sparse feature circuits: Discovering and editing interpretable causal graphs in language models. In International Conference on Learning Representations, volume 2025, pp. 23888–23923, 2025. Dehai Min, Giovanni Vaccarino, Huiyi Chen, Yongliang Wu, Gal Yona, and Lu Cheng. Stop when reasoning conv","arxiv_match":false},{"id":"WmdhapRKFO","title":"Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits","forum_url":"https://openreview.net/forum?id=WmdhapRKFO","excerpt":" and scalable method for localizing LLM behaviour to components, 2024. URL https://arxiv.org/ abs/2403.00745. Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller. Sparse feature circuits: Discovering and editing interpretable causal graphs in language models. In The Thirteenth International Conference on Learning Representations, 2025. URL https: //openreview.net/forum?id=I4e82CIDxv. Callum Stuart McDougall, Arthur Conmy, Cody Rushing, Thomas McGrath, an","arxiv_match":false},{"id":"eqmbSYhaLy","title":"Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility","forum_url":"https://openreview.net/forum?id=eqmbSYhaLy","excerpt":"ls via sparse autoencoders. arXiv preprint arXiv:2410.06981, 2024. Samuel Marks, Adam Karvonen, and Aaron Mueller. dictionary learning. https://github. com/saprmarks/dictionary_learning, 2024. Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller. Sparse feature circuits: Discovering and editing interpretable causal graphs in language models. In International Conference on Learning Representations (ICLR), 2025. arXiv:2403.19647. Brian B. Moser, Arundhati S. Shanbhag, Stanislav Frolov, Federico Raue, Joachim Folz, and Andreas ","arxiv_match":true}],"id":"2403.19647","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":690},{"title":"Opt: Open pre-trained transformer language models","arxiv_id":"2205.01068","year":2022,"url":"https://arxiv.org/abs/2205.01068","citing_submissions":151,"explicit_arxiv_matches":127,"examples":[{"id":"u6mwjJUXXB","title":"Point clouds as visual tokens for pretrained multimodal large language models","forum_url":"https://openreview.net/forum?id=u6mwjJUXXB","excerpt":"5 technical report. arXiv preprint arXiv:2412.15115, 2024. Kadir Yilmaz, Adrian Kruse, Tristan Höfer, Daan de Geus, and Bastian Leibe. Volume transformer: Revisiting vanilla transformers for 3d scene understanding. arXiv preprint arXiv:2604.19609, 2026. Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068, 2022. Duo Zheng, Shijia Huang, and Liwei Wang. Video-3d llm: Learning position-aware video represe","arxiv_match":true},{"id":"8v0pPgA8ph","title":"Training Foundation Models with a Single Algorithm","forum_url":"https://openreview.net/forum?id=8v0pPgA8ph","excerpt":"ang, Graham Neubig, and Xiang Yue. On the interplay of pre-training, mid-training, and RL on reasoning language models. In Forty-third International Conference on Machine Learning, 2026a. URL https://openreview.net/forum?id=TBaUfO9znF. Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. OPT: Open pre-trained transformer language models, 2022. URL https://arxiv.org/abs/2205.01068. Wenhao Zhang, Yuexiang Xie, Yuchang Sun, Yanxi Chen, Guoyin Wang, Yaliang Li, Bolin Ding, and Jing","arxiv_match":true},{"id":"69nOcEaHQA","title":"BranchTokens: Branch-Centric Tokenization and Test-Time Augmentation for Skeleton Generation","forum_url":"https://openreview.net/forum?id=69nOcEaHQA","excerpt":"Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. OPT: Open pre-trained transformer language models, 2022. 1, 2, 8 Ruisi Zhao, Haoren Zheng, Zongxin Yang, Hehe Fan, and Yi Yang. Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion models. arXiv preprint arXiv:2602.09713, 2026. 2, ","arxiv_match":false}],"id":"2205.01068","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":691},{"title":"Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks","arxiv_id":"2402.04396","year":2024,"url":"https://arxiv.org/abs/2402.04396","citing_submissions":151,"explicit_arxiv_matches":30,"examples":[{"id":"PTL1u5Zn05","title":"Fine-Tuning Leaves Clues: Calibration-Free Quantization via Task-Aware Basis","forum_url":"https://openreview.net/forum?id=PTL1u5Zn05","excerpt":"douard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. CoRR, abs/2302.13971, 2023. Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. Quip#: Even better LLM quantization with hadamard incoherence and lattice codebooks. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, Proceedings of Machine Learning Research, pp. 48630–48656. PMLR / OpenReview.net, 2024. Ale","arxiv_match":false},{"id":"ITI0eC0Jwe","title":"Prune-Quantize-Prune: Towards Better Joint Model Compression by Getting Free Sparsity from Symmetric Quantization","forum_url":"https://openreview.net/forum?id=ITI0eC0Jwe","excerpt":", Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023b. Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks. In ICML, 2024a. Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks. In ICML, 2024b.","arxiv_match":false},{"id":"BZAhEGxrVx","title":"Improve MXFP4 Quantization via Inter-block Permutation and Micro-block Givens Rotation","forum_url":"https://openreview.net/forum?id=BZAhEGxrVx","excerpt":"ro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks. In ICML, 2024. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. Huggingface’s transformers: St","arxiv_match":false}],"id":"2402.04396","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":692},{"title":"Rewardbench: Evaluating reward models for language modeling","arxiv_id":"2403.13787","year":2024,"url":"https://arxiv.org/abs/2403.13787","citing_submissions":151,"explicit_arxiv_matches":38,"examples":[{"id":"UrDgYAs0MP","title":"When Good Verifiers Go Bad: Silent Negative Transfer in Verifier-Guided VLM Training","forum_url":"https://openreview.net/forum?id=UrDgYAs0MP","excerpt":"istics, 2024b. Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. RewardBench: Evaluating reward models for language modeling. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 1755–1797. Association for Computational Linguistics, 2025. Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard","arxiv_match":false},{"id":"KvWRJQwcme","title":"AUDIT-FIRST VAPO: RISK-CERTIFIED SELECTIVE UPDATES UNDER IMPERFECT VERIFICATION","forum_url":"https://openreview.net/forum?id=KvWRJQwcme","excerpt":"Representations, 2022. Aviral Kumar, Rishabh Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. In Advances in Neural Information Processing Systems, 2020. Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. RewardBench: Evaluating reward models for language modeling. arXiv preprint arXiv:2403.13787, 2024. Hunter Lightman et al. Let’s verify step by step. In International Conference on Learning Re","arxiv_match":true},{"id":"XfNsXlvV7S","title":"VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers","forum_url":"https://openreview.net/forum?id=XfNsXlvV7S","excerpt":"International Conference on Machine Learning, 2025. Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit q-learning. In International Conference on Learning Representations, 2022. Nathan Lambert, Valentina Pyatkin, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. Rewardbench: Evaluating reward models for language modeling. arXiv preprint arXiv:2403.13787, 2024. Aviral Kumar, Rishabh Zhou, George Tucker, and Sergey Levine. Conservative q-learning for of","arxiv_match":true}],"id":"2403.13787","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":693},{"title":"Seedance 2.0: Advancing Video Generation for World Complexity","arxiv_id":"2604.14148","year":2026,"url":"https://arxiv.org/abs/2604.14148","citing_submissions":151,"explicit_arxiv_matches":144,"examples":[{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"linearity, Sparsity, and Attention-Sink-Free. arXiv preprint arXiv:2505.06708, 2025. Qwen Team. Qwen3-Next: Towards Ultimate Training & Inference Efficiency. Qwen Team blog, https://qwen.ai/blog?id=qwen3-next, 2025. Sand.ai, Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, et al. MAGI-1: Autoregressive Video Generation at Scale. arXiv preprint arXiv:2505.13211, 2025. Team Seedance et al. Seedance 2.0: Advancing Video Generation for World Complexity. arXiv preprint arXiv:2604.14148, 2026. Tomáš Souček and Jakub Lokoč. TransNet V2: An Effective Deep Network Architecture for Fa","arxiv_match":true},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"024. Haonan Qiu, Menghan Xia, Yong Zhang, Yingqing He, Xintao Wang, Ying Shan, and Ziwei Liu. Freenoise: Tuning-free longer video diffusion via noise rescheduling. In International Conference on Learning Representations, volume 2024, pp. 5260–5274, 2024. Team Seedance, De Chen, Liyang Chen, Xin Chen, Ying Chen, Zhuo Chen, Zhuowei Chen, Feng Cheng, Tianheng Cheng, Yufeng Cheng, et al. Seedance 2.0: Advancing video generation for world complexity. arXiv preprint arXiv:2604.14148, 2026. Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zh","arxiv_match":true},{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":"ex as an indicator of warning skill. Weather and forecasting, 1990. Team Seedance, De Chen, Liyang Chen, Xin Chen, Ying Chen, Zhuo Chen, Zhuowei Chen, Feng Cheng, Tianheng Cheng, Yufeng Cheng, et al. Seedance 2.0: Advancing video generation for world complexity. arXiv, 2026. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical rea","arxiv_match":false}],"id":"2604.14148","metadata_source":"Cached arXiv API metadata","rank":694},{"title":"The Lessons of Developing Process Reward Models in Mathematical Reasoning","arxiv_id":"2501.07301","year":2025,"url":"https://arxiv.org/abs/2501.07301","citing_submissions":151,"explicit_arxiv_matches":57,"examples":[{"id":"CC4ksJJPIN","title":"Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff","forum_url":"https://openreview.net/forum?id=CC4ksJJPIN","excerpt":"ning Representations, 2026b. URL https://openreview.net/forum?id=dCm9 bBrk5d. Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. The lessons of developing process reward models in mathematical reasoning. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Findings of the Association for Computational Linguistics: ACL 2025, pp. 10495–10516, Vienna, Austria, July 202","arxiv_match":false},{"id":"gJSZEDEBBU","title":"DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling","forum_url":"https://openreview.net/forum?id=gJSZEDEBBU","excerpt":"otic learning: A comprehensive survey. arXiv preprint arXiv:2607.21655, 2026. Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. The lessons of developing process reward models in mathematical reasoning. In ACL Findings, 2025. Appendix C ONTENTS A Algorithm B Experimental Details B.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.2 Hyperparameters . . . . . ","arxiv_match":false},{"id":"mCcMNyHoSX","title":"RALA: Reliability-Aware Latent Alignment under Heterogeneous Feedback","forum_url":"https://openreview.net/forum?id=mCcMNyHoSX","excerpt":"SSN 2835-8856. Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal. Generative verifiers: Reward modeling as next-token prediction. In The Thirteenth International Conference on Learning Representations, 2025c. Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. The lessons of developing process reward models in mathematical reasoning. arXiv preprint arXiv:2501.07301, 2025d. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi L","arxiv_match":true}],"id":"2501.07301","metadata_source":"Cached arXiv API metadata","rank":695},{"title":"Webrl: Training llm web agents via self-evolving online curriculum reinforcement learning","arxiv_id":"2411.02337","year":2024,"url":"https://arxiv.org/abs/2411.02337","citing_submissions":151,"explicit_arxiv_matches":43,"examples":[{"id":"MREjTH2FlB","title":"Seeing What Changed: Transition-Guided Credit Assignment for GUI Agents","forum_url":"https://openreview.net/forum?id=MREjTH2FlB","excerpt":"-5.5 system card. gpt-5-5-system-card/, April 2026. https://openai.com/index/ Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai, Xueqiao Sun, Jiadai Sun, Xinyue Yang, Yu Yang, Shuntian Yao, Wei Xu, et al. WebRL: Training LLM web agents via self-evolving online curriculum reinforcement learning. In International Conference on Learning Representations, volume 2025, pp. 79791–79821, 2025. Jiachen Qian. VisCritic: Visual state comparison as process reward for GUI agents. arXiv preprint arXiv:2","arxiv_match":false},{"id":"5HLwlj7lYB","title":"Less Is More: Selective Expert Supervision for Multi-Teacher On-Policy Distillation","forum_url":"https://openreview.net/forum?id=5HLwlj7lYB","excerpt":"v:2607.24720, 2026. URL https://arxiv.org/abs/2607.24720. Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai, Xueqiao Sun, Jiadai Sun, Xinyue Yang, Yu Yang, Shuntian Yao, Wei Xu, Jie Tang, and Yuxiao Dong. WebRL: Training LLM web agents via selfevolving online curriculum reinforcement learning. In International Conference on Learning Representations, 2025. URL https://proceedings.iclr.cc/paper_files/paper/ 2025/hash/c66e1fcc9691aae706250638f36f681b-Abstract-Conference. html. Mohit Shridhar","arxiv_match":false},{"id":"1dYqONTHJM","title":"SCOPE: Scaling Counterfactual, Observation-Preserving Environment Families for Terminal Agents","forum_url":"https://openreview.net/forum?id=1dYqONTHJM","excerpt":"020/hash/ 294e09f267683c7ddc6cc5134a7e68a8-Abstract.html. Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai, Xueqiao Sun, Jiadai Sun, Xinyue Yang, Yu Yang, Shuntian Yao, Wei Xu, Jie Tang, and Yuxiao Dong. Webrl: Training LLM web agents via selfevolving online curriculum reinforcement learning. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. URL https://openreview.net/forum?id=oVKEAFjEqv. Cheng Qian, Emr","arxiv_match":false}],"id":"2411.02337","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":696},{"title":"DFlash: Block Diffusion for Flash Speculative Decoding","arxiv_id":"2602.06036","year":2026,"url":"https://arxiv.org/abs/2602.06036","citing_submissions":150,"explicit_arxiv_matches":96,"examples":[{"id":"nStFNVfn07","title":"Matching While Alive: A Survival View of On-Policy Trajectory Distillation","forum_url":"https://openreview.net/forum?id=nStFNVfn07","excerpt":"es in Statistics. Springer-Verlag, New York, 1993. doi: 10.1007/978-1-4612-4348-9. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. Program synthesis with large language models, 2021. URL https://arxiv.org/abs/2108.07732. Jian Chen, Yesheng Liang, and Zhijian Liu. DFlash: Block diffusion for flash speculative decoding, 2026. URL https://arxiv.org/abs/2602.06036. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, ","arxiv_match":true},{"id":"ie4ipFVb04","title":"LAM++: Enhanced LocateAnything via High-Resolution Post-Training and Geometry-Aware Speculative Decoding","forum_url":"https://openreview.net/forum?id=ie4ipFVb04","excerpt":"mi, and Hilde Kuehne. Falcon perception-hd: High density perception via reinforcement learning. arXiv preprint arXiv:2608.18881, 2026. Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318, 2023a. Jian Chen, Yesheng Liang, and Zhijian Liu. Dflash: Block diffusion for flash speculative decoding. arXiv preprint arXiv:2602.06036, 2026. Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashin","arxiv_match":true},{"id":"V295SYnVAe","title":"PC-VOT: Prefix-Coupled Verifier-Induced Optimal Transport for Draft Training","forum_url":"https://openreview.net/forum?id=V295SYnVAe","excerpt":"ri Dao. Medusa: Simple llm inference acceleration framework with multiple decoding heads. arXiv preprint arXiv:2401.10774, 2024. 3 Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318, 2023. 1, 3 Jian Chen, Yesheng Liang, and Zhijian Liu. Dflash: Block diffusion for flash speculative decoding. arXiv preprint arXiv:2602.06036, 2026. 1, 3, 7, 18 Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pin","arxiv_match":true}],"id":"2602.06036","metadata_source":"Cached arXiv API metadata","rank":697},{"title":"DoReMi: Optimizing data mixtures speeds up language model pretraining","arxiv_id":"2305.10429","year":2023,"url":"https://arxiv.org/abs/2305.10429","citing_submissions":150,"explicit_arxiv_matches":30,"examples":[{"id":"B3bJ2E4EoT","title":"CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives","forum_url":"https://openreview.net/forum?id=B3bJ2E4EoT","excerpt":"vision-language data selection. arXiv preprint arXiv:2501.00654, 2024. Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy S Liang, Quoc V Le, Tengyu Ma, and Adams Wei Yu. Doremi: Optimizing data mixtures speeds up language model pretraining. Advances in Neural Information Processing Systems, 36:69798–69818, 2023a. Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy S Liang. Data selection for language models via importance resampl","arxiv_match":false},{"id":"q5NpkqNLNY","title":"Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation","forum_url":"https://openreview.net/forum?id=q5NpkqNLNY","excerpt":"preprint arXiv:2601.02780, 2026. URL https:// arxiv.org/abs/2601.02780. Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, and Adams Wei Yu. Doremi: Optimizing data mixtures speeds up language model pretraining. Advances in Neural Information Processing Systems, 36:69798– 69818, 2023. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, et al. Qwen3 Technical Report. arXiv preprint arXiv:","arxiv_match":false},{"id":"Fhj95cY1wD","title":"Decoupling Token Roles in Autoregressive Pretraining","forum_url":"https://openreview.net/forum?id=Fhj95cY1wD","excerpt":"in natural language processing: system demonstrations, pp. 38–45, 2020. Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, and Adams Wei Yu. DoReMi: Optimizing data mixtures speeds up language model pretraining. In Advances in Neural Information Processing Systems, 2023. Ziang Xie, Sida I Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, and Andrew Y Ng. Data noising as smoothing in neural network lang","arxiv_match":false}],"id":"2305.10429","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":698},{"title":"The instruction hierarchy: Training LLMs to prioritize privileged instructions","arxiv_id":"2404.13208","year":2024,"url":"https://arxiv.org/abs/2404.13208","citing_submissions":150,"explicit_arxiv_matches":140,"examples":[{"id":"TO4R4c6u7V","title":"From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents","forum_url":"https://openreview.net/forum?id=TO4R4c6u7V","excerpt":"ent: Securing ai agents with privilege control, 2026. URL https://arxiv.org/abs/ 2504.11703. Ankur Singh, Jinqiu Yang, and Tse-Hsun Chen. Issuetrojanbench: Benchmarking ai coding agents against malicious issue requests, 2026. URL https://arxiv.org/abs/2607.20759. Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. The instruction hierarchy: Training llms to prioritize privileged instructions, 2024. URL https: //arxiv.org/abs/2404.13208. Haoyu Wang, Christopher M. Poskitt, and Jun Sun. Agentspec: Customizable runtime enforcement for s","arxiv_match":true},{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":" Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, Ian Ballantyne, Cormac Brick, Victor Cărbune, Michelle Casbon, et al. Gemma 4 technical report. arXiv preprint arXiv:2607.02770, 2026. Qwen Team. Qwen3. 5-omni technical report. arXiv preprint arXiv:2604.15804, 2026. Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. The instruction hierarchy: Training llms to prioritize privileged instructions. arXiv preprint arXiv:2404.13208, 2024. Miles Wang, Tom Dupré la Tour, Olivia Watkins, Alex Makelov, Ryan A Chi, Samuel Miserendino,","arxiv_match":true},{"id":"81BK0yhhXO","title":"Document-Channel Manipulation in a Text-Extracted AI Review Pipeline","forum_url":"https://openreview.net/forum?id=81BK0yhhXO","excerpt":"ge-scale randomized study of large language model feedback in peer review. Nature Machine Intelligence, 8(3):326–336, 2026. Pukaphol Thienpreecha and Karthik Subramanian. CrackedPDFs: A Controlled Benchmark for Hidden Prompt Injection in PDFs. arXiv preprint arXiv:2607.19396, 2026. Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. The instruction hierarchy: Training llms to prioritize privileged instructions. arXiv preprint arXiv:2404.13208, 2024. Jiongxiao Wang, Fangzhou Wu, Wendi Li, Jinsheng Pan, Edward Suh, Z. Morley Mao, Muhao Chen, ","arxiv_match":true}],"id":"2404.13208","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":699},{"title":"Video prediction policy: A generalist robot policy with predictive visual representations","arxiv_id":"2412.14803","year":2024,"url":"https://arxiv.org/abs/2412.14803","citing_submissions":150,"explicit_arxiv_matches":78,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"ng Hu, Jianke Zhang, Yen-Jen Wang, Xiaoyu Chen, Chaochao Lu, and Jianyu Chen. Prediction with action: Visual policy learning via joint denoising process. Advances in Neural Information Processing Systems, 37:112386–112410, 2024. Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, and Jianyu Chen. Video prediction policy: A generalist robot policy with predictive visual representations. arXiv preprint arXiv:2412.14803, 2024. Sunshine Jiang, Xiaolin Fang, Nicholas Roy, Tomás Lozano-Pérez, Leslie Pack Kaelbling, and S","arxiv_match":true},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"reprint arXiv:2508.17230, 2025. URL https://arxiv.org/abs/2508.17230. Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, and Jianyu Chen. Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 24328–24346. PMLR, 2025. URL https://proceedings.mlr.press/v267/h","arxiv_match":false},{"id":"Lc2iGieCPZ","title":"Language-Forcing: Semantic Alignment for Whole-Body World Action Model","forum_url":"https://openreview.net/forum?id=Lc2iGieCPZ","excerpt":"njia Xu, Zhenyu Jiang, Jan Kautz, Changliu Liu, Guanya Shi, Xiaolong Wang, Linxi Fan, and Yuke Zhu. Hover: Versatile neural whole-body controller for humanoid robots, 2025. URL https://arxiv.org/abs/2410.21229. Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, and Jianyu Chen. Video prediction policy: A generalist robot policy with predictive visual representations, 2025. URL https://arxiv.org/abs/2412.14803. Yash Jangir, Yidi Zhang, Pang-Chi Lo, Kashu Yamazaki, Chenyu Zhang, Kuan-Hsun Tu, TsungWei Ke, Lei","arxiv_match":true}],"id":"2412.14803","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":700},{"title":"mixup: Beyond empirical risk minimization","arxiv_id":"1710.09412","year":2017,"url":"https://arxiv.org/abs/1710.09412","citing_submissions":150,"explicit_arxiv_matches":44,"examples":[{"id":"JKleuAE2gx","title":"Trajectory-aware Differentiable Blur Augmentation for Image Deblurring","forum_url":"https://openreview.net/forum?id=JKleuAE2gx","excerpt":"nd pattern recognition (CVPR), pp. 14816–14826. IEEE, 2021. Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and MingHsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5728–5739, 2022. Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonab","arxiv_match":true},{"id":"qcXYhyyzVO","title":"Vision Fluxer with Dynamic Global Receptive Field","forum_url":"https://openreview.net/forum?id=qcXYhyyzVO","excerpt":" train strong classifiers with localizable features. In Proceedings of International Conference on Computer Vision (ICCV), 2019. A7 Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. In Proceedings of International Conference on Learning Representations (ICLR), 2018. A7 Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. In Proceedin","arxiv_match":false},{"id":"zTtsERHvIf","title":"Convolutional Neural Network Can Satisfy Representation and Interpretability without Nonlinear Activation Functions Simultaneously","forum_url":"https://openreview.net/forum?id=zTtsERHvIf","excerpt":"nt Biao Zhang and Rico Sennrich. Root mean square layer normalization. Advances in neural information processing systems, 32, 2019. Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. In The Seventh International Conference on Learning Representations, 2018a. Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. Shufflenet: An extremely efficient convolutional neural network for","arxiv_match":false}],"id":"1710.09412","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":701},{"title":"A Survey of On-Policy Distillation for Large Language Models","arxiv_id":"2604.00626","year":2026,"url":"https://arxiv.org/abs/2604.00626","citing_submissions":149,"explicit_arxiv_matches":135,"examples":[{"id":"5H9yPPYAla","title":"Tangent On-Policy Distillation: Local-Direction Supervision Beyond Outputs","forum_url":"https://openreview.net/forum?id=5H9yPPYAla","excerpt":" Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768, 2020. Mingyang Song and Mao Zheng. A survey of on-policy distillation for large language models. arXiv preprint arXiv:2604.00626, 2026. Songjun Tu, Chengdong Xu, Qichao Zhang, Yiwen Ma, Yaocheng Zhang, Linjing Li, Dong Li, Xiang","arxiv_match":true},{"id":"5iMbperkQz","title":"Context-consistent Teacher Signals for On-policy Distillation","forum_url":"https://openreview.net/forum?id=5iMbperkQz","excerpt":"ang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768, 2020. Mingyang Song and Mao Zheng. A survey of on-policy distillation for large language models. arXiv preprint arXiv:2604.00626, 2026. Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, I","arxiv_match":true},{"id":"NkTmsKRnDh","title":"StageOPD: Stage-aware Loss Reweighting for On-policy Distillation","forum_url":"https://openreview.net/forum?id=NkTmsKRnDh","excerpt":"ang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768, 2020. Mingyang Song and Mao Zheng. A survey of on-policy distillation for large language models. arXiv preprint arXiv:2604.00626, 2026. Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, I","arxiv_match":true}],"id":"2604.00626","metadata_source":"Cached arXiv API metadata","rank":702},{"title":"Gaia-1: A generative world model for autonomous driving","arxiv_id":"2309.17080","year":2023,"url":"https://arxiv.org/abs/2309.17080","citing_submissions":149,"explicit_arxiv_matches":140,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"), pp. 16000–16009, June 2022a. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16000–16009, 2022b. Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado. Gaia-1: A generative world model for autonomous driving. arXiv preprint arXiv:2309.17080, 2023. Zhaojin Huang, Lichao Huang, Yongchao Gong, Chang Huang, and Xinggang Wang. Mask scoring R-C","arxiv_match":true},{"id":"3nSV2brMqb","title":"PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives","forum_url":"https://openreview.net/forum?id=3nSV2brMqb","excerpt":" Yifan Zhang, Qi Cui, Fei Kang, Biao Jiang, Mengyin An, Yangyang Ren, Baixin Xu, Hao-Xiang Guo, Kaixiong Gong, Size Wu, Wei Li, Xuchen Song, Yang Liu, Yangguang Li, and Yahui Zhou. Matrix-Game 2.0: An open-source real-time and streaming interactive world model, August 2025. Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado. GAIA-1: A generative world model for autonomous driving. arXiv preprint arXiv:2309.17080, 2023. Xiaotao Hu, Wei Yin, Mingkai Jia, Junyuan Deng, Xiaoyang Guo, Qian Zhang, Xiaoxiao Long, and","arxiv_match":true},{"id":"hjPoL9WGRc","title":"AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents","forum_url":"https://openreview.net/forum?id=hjPoL9WGRc","excerpt":"iv preprint Shahab Heshmati-Alamdari, George C. Karras, Panos Marantos, and Kostas J. Kyriakopoulos. A robust predictive control approach for underwater robotic vehicles. IEEE Transactions on Control Systems Technology, 28(6):2352–2363, 2020. doi: 10.1109/TCST.2019.2939248. Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado. GAIA-1: A generative world model for autonomous driving. arXiv preprint arXiv:2309.17080, 2023. Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sr","arxiv_match":true}],"id":"2309.17080","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":703},{"title":"MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents","arxiv_id":"2506.15841","year":2025,"url":"https://arxiv.org/abs/2506.15841","citing_submissions":149,"explicit_arxiv_matches":71,"examples":[{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"onments. arXiv preprint arXiv:2504.03160, 2025. URL https://arxiv.org/abs/2504. 03160. Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low, and Paul Liang. MEM1: Learning to Synergize Memory and Reasoning for Efficient LongHorizon Agents. In International Conference on Learning Representations (ICLR), 2026. URL https://openreview.net/forum?id=XY8AaxDSLb. A T RAINING D ETAILS Following Section 3, we post-train small-model workers for ","arxiv_match":false},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":"/2024/hash/ 4410c0711e9154a7a2d26f9b3816d1ef-Abstract-Conference.html. Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Jinhua Zhao, Bryan Kian Hsiang Low, and Paul Pu Liang. MEM1: Learning to synergize memory and reasoning for efficient long-horizon agents. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=XY8AaxDSLb. A This section describes the offline pipeline used to construct demonstr","arxiv_match":false},{"id":"ZvTJOV7xQk","title":"EvoRule Harness: Lifelong Harness Adaptation with Validated Intervention Rules","forum_url":"https://openreview.net/forum?id=ZvTJOV7xQk","excerpt":"s of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19632–19642, 2024. Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low, and Paul Liang. Mem1: Learning to synergize memory and reasoning for efficient longhorizon agents. In International Conference on Learning Representations, volume 2026, pp. 58413–58438, 2026. A ","arxiv_match":false}],"id":"2506.15841","metadata_source":"Cached arXiv API metadata","rank":704},{"title":"Sharpness-aware minimization for efficiently improving generalization","arxiv_id":"2010.01412","year":2020,"url":"https://arxiv.org/abs/2010.01412","citing_submissions":149,"explicit_arxiv_matches":42,"examples":[{"id":"EPREfh3h40","title":"TRACE: Trajectory Return Attribution and Contrastive Erasure for Multi-Turn Safety","forum_url":"https://openreview.net/forum?id=EPREfh3h40","excerpt":"Simplicity prevails: Rethinking negative preference optimization for LLM unlearning. In Proceedings of NeurIPS, pp. 1540–1567, 2025. Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In Proceedings of ICLR, 2021. Gemma Team. Gemma 3 technical report. arXiv preprint arXiv:2503.19786, 2025. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, and et al. The llama 3 herd of models. ","arxiv_match":false},{"id":"KAvPoroQnT","title":"Rethinking Vision Foundation Models for High-Fidelity Forgery Detection by Diagnosing Optimization Collapse","forum_url":"https://openreview.net/forum?id=KAvPoroQnT","excerpt":"laum, Nicole Baram, and Cristian Canton-Ferrer. The deepfake detection challenge (DFDC) preview dataset. CoRR, abs/1910.08854, 2019. Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In ICLR. OpenReview.net, 2021. Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In I","arxiv_match":false},{"id":"8qOKRk4Hvt","title":"Why does Post-Training Quantization Work?","forum_url":"https://openreview.net/forum?id=8qOKRk4Hvt","excerpt":"39(16):16453–16461, 2025. Matthew Finlayson, Xiang Ren, and Swabha Swayamdipta. Every language model has a forgeryresistant signature. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=vLFqOoMBol. Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412, 2021. URL https://arxiv.org/abs/2010.01412. Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. OPTQ: Accurate quantization for ","arxiv_match":true}],"id":"2010.01412","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":705},{"title":"ToolRL: Reward is All Tool Learning Needs","arxiv_id":"2504.13958","year":2025,"url":"https://arxiv.org/abs/2504.13958","citing_submissions":149,"explicit_arxiv_matches":47,"examples":[{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"36th annual acm symposium on user interface software and technology, pp. 1–22, 2023. Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. Gorilla: Large language model connected with massive apis. Proceedings of Advances in Neural Information Processing Systems, pp. 126544–126565, 2024. Cheng Qian, Emre Can Acikgoz, Qi He, Hongru Wang, Xiusi Chen, Dilek Hakkani-Tür, Gokhan Tur, and Heng Ji. Toolrl: Reward is all tool learning needs. arXiv preprint arXiv:2504.13958, 2025. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xian","arxiv_match":true},{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"OpenAI. Introducing deep research. OpenAI, 2025. URL https://openai.com/index/ introducing-deep-research/. OpenAI. On the Navier–Stokes millennium prize problem. OpenAI Research, September 2026. URL https://openai.com/index/navier-stokes-solution/. Cheng Qian, Emre Can Acikgoz, Qi He, Hongru Wang, Xiusi Chen, Dilek Hakkani-Tür, Gokhan Tur, and Heng Ji. ToolRL: Reward is All Tool Learning Needs. arXiv preprint arXiv:2504.13958, 2025. URL https://arxiv.org/abs/2504.13958. Qwen Team. Qwen3.5-9B. Hugging Face model card, 2026. URL https://huggingface. co/Qwen/Qwen3.5-9B.","arxiv_match":true},{"id":"pK04w5wiDp","title":"Trust Is a Process, Not Just a Score: Agentic Verification for Test-Time Scaling","forum_url":"https://openreview.net/forum?id=pK04w5wiDp","excerpt":"ch, pp. 26106–26128. PMLR, 23–29 Jul 2023. URL https://proceedings.mlr.press/v202/ni23b.html. Cheng Qian, Emre Can Acikgoz, Qi He, Hongru Wang, Xiusi Chen, Dilek Hakkani-Tür, Gokhan Tur, and Heng Ji. ToolRL: Reward is all tool learning needs. In Advances in Neural Information Processing Systems, 2025. URL https://proceedings.neurips.cc/paper_files/ paper/2025/hash/97c5b2707228e7e3fb67e4ecc2e0e607-Abstract-Conference.html. Qwen Team. Qwen","arxiv_match":false}],"id":"2504.13958","metadata_source":"Cached arXiv API metadata","rank":706},{"title":"Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions","arxiv_id":"2406.15877","year":2024,"url":"https://arxiv.org/abs/2406.15877","citing_submissions":148,"explicit_arxiv_matches":49,"examples":[{"id":"w9AtxUbfFB","title":"Loop-Distill: Refining Information Flow in Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=w9AtxUbfFB","excerpt":"e models. arXiv preprint arXiv:2510.25741, 2025c. Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al. Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions. In International Conference on Learning Representations, volume 2025, pp. 66602–66656, 2025. Table 8: Per-benchmark measured decoding throughput. We report generated tokens per second (TPS) under th","arxiv_match":false},{"id":"WaybMSS9Gr","title":"Every Token Counts: Online Memory Worth for Resource-Constrained Lifelong Agents","forum_url":"https://openreview.net/forum?id=WaybMSS9Gr","excerpt":"uage models with long-term memory. In AAAI, 2024. Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al. Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions. In International Conference on Learning Representations, volume 2025, pp. 66602–66656, 2025. A ","arxiv_match":false},{"id":"4V3WtVam0y","title":"Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees","forum_url":"https://openreview.net/forum?id=4V3WtVam0y","excerpt":"preprint arXiv:2606.32025, 2026. Jingyuan Zheng et al. SkillSelect-Serve: QoS-aware budgeted skill service recommendation for LLM agents. arXiv preprint arXiv:2607.00011, 2026a. Terry Yue Zhuo et al. BigCodeBench: Benchmarking code generation with diverse function calls and complex instructions. In Proc. 13th Int. Conf. Learn. Represent. (ICLR), 2025. YanZhao Zheng et al. SkillRouter: Skill routing for LLM agents at scale. arXiv:2603.22455, 2026b. arXiv preprint A Throughout this section, f","arxiv_match":false}],"id":"2406.15877","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":707},{"title":"Dreamfusion: Text-to-3d using 2d diffusion","arxiv_id":"2209.14988","year":2022,"url":"https://arxiv.org/abs/2209.14988","citing_submissions":148,"explicit_arxiv_matches":65,"examples":[{"id":"DmH3HpzlV6","title":"From Parts to Sim: Bottom-Up Autoregressive Generation of  Sim-Ready 3D Assets","forum_url":"https://openreview.net/forum?id=DmH3HpzlV6","excerpt":", Kyle Stachowicz, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, and Ury Zhilinsky. π0.5 : A vision-language-action model with open-world generalization. arXiv preprint arXiv:2504.16054, 2025. doi: 10.48550/arXiv.2504.16054. URL https://arxiv.org/abs/2504.16054. Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. DreamFusion: Text-to-3D using 2D diffusion. arXiv preprint arXiv:2209.14988, 2022. URL https://arxiv.org/abs/2209. 14988. Xiaowen Qiu, Jincheng Yang, Yian Wang, Zhehuan Chen, Yufei Wang, Tsun-Hsuan Wang, Zhou Xian, and C","arxiv_match":true},{"id":"MOLTwuUqhX","title":"Intrinsic Alignment by Generation: Monocular Hand–Object Reconstruction via Interaction Scaffolds","forum_url":"https://openreview.net/forum?id=MOLTwuUqhX","excerpt":"ffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4195–4205, 2023. Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. DreamFusion: Text-to-3d using 2d diffusion. In International Conference on Learning Representations (ICLR), 2023. Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Tr","arxiv_match":false},{"id":"jqz9NzbWeN","title":"DreamBridge: Bridged Cross-Modal Distillation for Text-to-3D Generation","forum_url":"https://openreview.net/forum?id=jqz9NzbWeN","excerpt":"nd Yao Yao. Direct2.5: Diverse text-to-3d generation via multi-view 2.5d diffusion. Computer Vision and Pattern Recognition (CVPR), 2024. Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. DreamFusion: Text-to-3D using 2D diffusion. In International Conference on Learning Representations (ICLR), 2023. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela M","arxiv_match":false}],"id":"2209.14988","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":708},{"title":"GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization","arxiv_id":"2601.05242","year":2026,"url":"https://arxiv.org/abs/2601.05242","citing_submissions":148,"explicit_arxiv_matches":122,"examples":[{"id":"aJacDo8LVg","title":"Rubric-to-Segment: Criterion-grounded Semantic Credit Assignment for Language Model Alignment","forum_url":"https://openreview.net/forum?id=aJacDo8LVg","excerpt":"a Sutskever, and Karl Cobbe. Let’s verify step by step. In International Conference on Learning Representations, volume 2024, pp. 39578–39601, 2024. Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao, Peter Belcak, Mingjie Liu, Min-Hung Chen, Hongxu Yin, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Yejin Choi, Jan Kautz, and Pavlo Molchanov. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multireward RL Optimization, 2026. URL https://arxiv.org/abs/2601.05242. Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. U","arxiv_match":true},{"id":"F0hAiHbpm3","title":"FigAct: Turning Scientific Figures into Active Canvases for Explanation","forum_url":"https://openreview.net/forum?id=F0hAiHbpm3","excerpt":"di Wang. Preacher: Paper-tovideo agentic system. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 17129–17139. IEEE, 2025. Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao, Peter Belcak, Mingjie Liu, Min-Hung Chen, Hongxu Yin, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Yejin Choi, Jan Kautz, and Pavlo Molchanov. Gdpo: Group reward-decoupled normalization policy optimization for multi-reward rl optimization, 2026a. URL https://arxiv.org/abs/2601.05242. Yepeng Liu, Dasen Dai, Chengzhi Liu, Yiren Song, Hai Ci, Yu Zhang, Qi Zhang, Mike Zheng Shou, Xin ","arxiv_match":true},{"id":"2te1UDMg3T","title":"MedRouter: Demystifying Knowledge Differences Across Medical LLMs for Routing-Based Reasoning","forum_url":"https://openreview.net/forum?id=2te1UDMg3T","excerpt":"yu Zhou, Haoyu Dong, Xiaojun Ma, Shi Han, and Dongmei Zhang. Bingo: Boosting efficient reasoning of llms via dynamic and significance-based reinforcement learning. arXiv preprint arXiv:2506.08125, 2025a. Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao, Peter Belcak, Mingjie Liu, Min-Hung Chen, Hongxu Yin, Yu-Chiang Frank Wang, Kwang-Ting Cheng, et al. Gdpo: Group rewarddecoupled normalization policy optimization for multi-reward rl optimization. arXiv preprint arXiv:2601.05242, 2026. Yihao Liu, Shuocheng Li, Lang Cao, Yuhang Xie, Mengyu Zhou, Haoyu Dong, Xiaojun Ma, Shi Han,","arxiv_match":true}],"id":"2601.05242","metadata_source":"Cached arXiv API metadata","rank":709},{"title":"LLMs know more than they show: On the intrinsic representation of LLM hallucinations","arxiv_id":"2410.02707","year":2024,"url":"https://arxiv.org/abs/2410.02707","citing_submissions":148,"explicit_arxiv_matches":37,"examples":[{"id":"W1xeiKanFu","title":"Do Correct Answers Have Stable Latent Knowledge Support?","forum_url":"https://openreview.net/forum?id=W1xeiKanFu","excerpt":". In Conference on Language Modeling, 2025. URL https://openreview.net/forum?id= 2ezugTT9kU. Hadas Orgad, Michael Toker, Zorik Gekhman, Roi Reichart, Idan Szpektor, Hadas Kotek, and Yonatan Belinkov. Llms know more than they show: On the intrinsic representation of llm hallucinations. In International Conference on Learning Representations, volume 2025, pp. 66880– 66913, 2025. Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexand","arxiv_match":false},{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"6, 2026a. OpenAI. GPT-5.5 System Card. https://openai.com/index/gpt-5-5-system-card/, 2026b. Hadas Orgad, Michael Toker, Zorik Gekhman, Roi Reichart, Idan Szpektor, Hadas Kotek, and Yonatan Belinkov. LLMs know more than they show: On the intrinsic representation of LLM hallucinations. In ICLR, 2025. OWASP Gen AI Security Project. Owasp top 10 for agentic applications for 2026, 2025. URL https: //genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/. Alexander P","arxiv_match":false},{"id":"CZAIZlb4t2","title":"Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade","forum_url":"https://openreview.net/forum?id=CZAIZlb4t2","excerpt":"with conformal factuality guarantees. In International Conference on Machine Learning, 2024. Hadas Orgad, Michael Toker, Zorik Gekhman, Roi Reichart, Idan Szpektor, Hadas Kotek, and Yonatan Belinkov. LLMs know more than they show: On the intrinsic representation of LLM hallucinations. In International Conference on Learning Representations, 2025. Trilok Padhi, Ramneet Kaur, Krishiv Agarwal, Adam D. Cobb, Daniel Elenius, Manoj Acharya, Colin Samplawski, Alexander M. Berenbeim, Nat","arxiv_match":false}],"id":"2410.02707","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":710},{"title":"MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation","arxiv_id":"2508.19236","year":2025,"url":"https://arxiv.org/abs/2508.19236","citing_submissions":148,"explicit_arxiv_matches":67,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":", Peter Welinder, Wojciech Zaremba, and Pieter Abbeel. Asymmetric actor critic for image-based robot learning. In Proceedings of Robotics: Science and Systems, Pittsburgh, Pennsylvania, June 2018. doi: 10.15607/RSS.2018.XIV.008. Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xiangyu Zhang, and Gao Huang. Memoryvla: Perceptual-cognitive memory in vision-languageaction models for robotic manipulation. arXiv preprint arXiv:2508.19236, 2025a. Lucy Xiaoyang Shi, Brian Ichter, Michael Robert Equi, Liyiming Ke, Karl Pertsch, Quan Vuong","arxiv_match":true},{"id":"Rb57sy6lCT","title":"SHARP-WAM: Sparse Hierarchical Attention with Probe Tokens for Bounded-Context World Action Modeling","forum_url":"https://openreview.net/forum?id=Rb57sy6lCT","excerpt":"on in visionlanguage-action models. arXiv preprint arXiv:2606.09827, 2026a. Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xiangyu Zhang, and Gao Huang. MemoryVLA: Perceptual-cognitive memory in vision-languageaction models for robotic manipulation. In The Fourteenth International Conference on Learning Representations, 2026b. Xiaoquan Sun, Ruijian Zhang, Chen Cao, Yihan Sun, Jiahui Chen, Zetian Xu, Bo Chen, Haijier Chen, Zhen Yang, Jiarun Zhu,","arxiv_match":false},{"id":"ygNU3le5s8","title":"Remember What Matters: On-Demand Memory for Long-Horizon Robotic Manipulation","forum_url":"https://openreview.net/forum?id=ygNU3le5s8","excerpt":"ernational Conference on Computer Vision (ICCV), pp. 4172–4182. IEEE, 2023. Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xiangyu Zhang, and Gao Huang. Memoryvla: Perceptual-cognitive memory in vision-languageaction models for robotic manipulation. In International Conference on Learning Representations, volume 2026, pp. 18567–18602, 2026. Oriane Siméoni, Huy V Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalid","arxiv_match":false}],"id":"2508.19236","metadata_source":"Cached arXiv API metadata","rank":711},{"title":"LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization","arxiv_id":"2510.03827","year":2025,"url":"https://arxiv.org/abs/2510.03827","citing_submissions":147,"explicit_arxiv_matches":140,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":"d Trevor Darrell. Playful agentic robot learning. arXiv preprint arXiv:2606.19419, 2026. URL https://arxiv.org/abs/ 2606.19419. Xufeng Zhao, Cornelius Weber, and Stefan Wermter. Agentic skill discovery. arXiv preprint arXiv:2405.15019, 2024. Xueyang Zhou, Yangming Xu, Guiyao Tie, Yongchao Chen, Guowen Zhang, Duanfeng Chu, Pan Zhou, and Lichao Sun. LIBERO-PRO: Towards robust and fair evaluation of vision-languageaction models beyond memorization. arXiv preprint arXiv:2510.03827, 2025. Yihang Zhu, Weiqing Wang, Shijie Wu, Ye Shi, and Jingya Wang. Learning semantic atomic skill","arxiv_match":true},{"id":"vwDnaAnR3T","title":"When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models","forum_url":"https://openreview.net/forum?id=vwDnaAnR3T","excerpt":", Rika Antonova, Shuran Song, and Jeannette Bohg. Equibot: SIM(3)-equivariant diffusion policy for generalizable and data efficient learning. In 8th Annual Conference on Robot Learning, 2024. URL https://openreview.net/forum? id=ueBmGhLOXP. Xueyang Zhou, Yangming Xu, Guiyao Tie, Yongchao Chen, Guowen Zhang, Duanfeng Chu, Pan Zhou, and Lichao Sun. Libero-pro: Towards robust and fair evaluation of vision-language-action models beyond memorization. arXiv preprint arXiv:2510.03827, 2025. Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhar","arxiv_match":true},{"id":"piPoqhcKrP","title":"RoboSafeBench: Diagnosing Dynamic Contact Risk in Vision-Language-Action Policies","forum_url":"https://openreview.net/forum?id=piPoqhcKrP","excerpt":"Feng, Yinan Zheng, Jiayin Zou, Yilun Chen, Jia Zeng, et al. X-VLA: Soft-prompted transformer as scalable crossembodiment vision-language-action model. arXiv preprint arXiv:2510.10274, 2025. Xueyang Zhou, Yangming Xu, Guiyao Tie, Yongchao Chen, Guowen Zhang, Duanfeng Chu, Pan Zhou, and Lichao Sun. LIBERO-PRO: Towards robust and fair evaluation of vision-languageaction models beyond memorization. arXiv preprint arXiv:2510.03827, 2025. URL https: //arxiv.org/abs/2510.03827. A S UPPLEMENTARY P ROTOCOL D ETAILS Table 6: Per-host disturbance calibration used for the reporte","arxiv_match":true}],"id":"2510.03827","metadata_source":"Cached arXiv API metadata","rank":712},{"title":"Palm: Scaling language modeling with pathways","arxiv_id":"2204.02311","year":2022,"url":"https://arxiv.org/abs/2204.02311","citing_submissions":147,"explicit_arxiv_matches":30,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"andr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. Palm: scaling language modeling with pathways. J. Mach. Learn. Res., 24(1), January 2023. ISSN 1532-4435. Somnath Basu Roy Chowdhury, Krzysztof Marcin Choromanski, Arijit Sehanobish, Kumar Avinava Dubey, and Snigdha Chaturvedi. Towards scalable ","arxiv_match":false},{"id":"1P00VnlXQ7","title":"Lossy Projection, Exact Expansion: Retrofitting Pretrained Gated Feed-Forward Blocks","forum_url":"https://openreview.net/forum?id=1P00VnlXQ7","excerpt":"andr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. PaLM: Scaling language modeling with Pathways. Journal of Machine Learning Research, 24(240):1–113, 2023. URL https: //jmlr.org/papers/v24/22-1144.html. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, a","arxiv_match":false},{"id":"KvMIeBWgq8","title":"Ewen: A Covariate-Aware Foundation Model for EEG Understanding","forum_url":"https://openreview.net/forum?id=KvMIeBWgq8","excerpt":"om Time Series for Health, 2022. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. Journal of machine learning research, 24(240):1–113, 2023. Mingzhe Cui, Tao Chen, Yang Jiao, Yiqin Wang, Lei Xie, Yi Pan, and Luca Mainardi. Brainrvq: A high-fidelity eeg foundation model via dual-d","arxiv_match":false}],"id":"2204.02311","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":713},{"title":"Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs","arxiv_id":"2503.01743","year":2025,"url":"https://arxiv.org/abs/2503.01743","citing_submissions":147,"explicit_arxiv_matches":132,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"hnan, Franziska Meier, Chris Paxton, Alexander Sax, and Aravind Rajeswaran. OpenEQA: Embodied question answering in the era of foundation models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16488– 16498, 2024. URL https://open-eqa.github.io/. Microsoft. Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs. arXiv preprint arXiv:2503.01743, 2025. URL https://arxiv.org/ abs/2503.01743. NVIDIA. Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning. arXiv preprint arXiv:250","arxiv_match":true},{"id":"4ihDGJ5CAY","title":"What Early Stopping Hides: Cost-Aware Auditing in Post-Training","forum_url":"https://openreview.net/forum?id=4ihDGJ5CAY","excerpt":" Abdelrahman Abouelenin, Atabak Ashfaq, Adam Atkinson, Hany Awadalla, Nguyen Bach, Jianmin Bao, Alon Benhaim, Martin Cai, Vishrav Chaudhary, Congcong Chen, et al. Phi-4-Mini technical report: Compact yet powerful multimodal language models via Mixture-of-LoRAs. arXiv preprint arXiv:2503.01743, 2025. Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, and Frank Hutter. Efficient baye","arxiv_match":true},{"id":"xVqGCq3X2z","title":"SRAM: Learning Spectral Relation-Aware Representations for Environmental Sound Deepfake Detection","forum_url":"https://openreview.net/forum?id=xVqGCq3X2z","excerpt":"tin, Thomas Portet, Kai Qiu, Bo Ren, Liliang Ren, Sambuddha Roy, Ning Shang, Yelong Shen, Saksham Singhal, Subhojit Som, Xia Song, Tetyana Sych, Praneetha Vaddamanu, Shuohang Wang, Yiming Wang, Zhenghao Wang, Haibin Wu, Haoran Xu, Weijian Xu, Yifan Yang, Ziyi Yang, Donghan Yu, Ishmam Zabir, Jianwen Zhang, Li Lyna Zhang, Yunan Zhang, and Xiren Zhou. Phi-4-mini technical report: Compact yet powerful multimodal language models via mixture-of-loras. arXiv preprint arXiv:2503.01743, 2025. Boson AI. Higgs Audio V2: Redefining Expressiveness in Audio Generation. github.com/boson-ai","arxiv_match":true}],"id":"2503.01743","metadata_source":"Cached arXiv API metadata","rank":714},{"title":"SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training","arxiv_id":"2501.17161","year":2025,"url":"https://arxiv.org/abs/2501.17161","citing_submissions":147,"explicit_arxiv_matches":68,"examples":[{"id":"9T7UsqeKov","title":"Beyond Coordinates: Semantic Reward for GUI Agent Post-Training","forum_url":"https://openreview.net/forum?id=9T7UsqeKov","excerpt":" report. arXiv preprint arXiv:2511.21631, 2025a. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923, 2025b. Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V Le, Sergey Levine, and Yi Ma. Sft memorizes, rl generalizes: A comparative study of foundation model post-training. arXiv preprint arXiv:2501.17161, 2025. Gaole Dai, Shiqi Jiang, Ting Cao, Yuanchun Li, Yuqing Yang, Rui Tan, Mo Li, and Lili Qiu. Ad","arxiv_match":true},{"id":"mczw113vfG","title":"On-Policy Parameter Update Direction Underlies Generalization in LLM Post-Training","forum_url":"https://openreview.net/forum?id=mczw113vfG","excerpt":"on artificial intelligence, volume 34, pp. 7432–7439, 2020. Han Chen, Garvesh Raskutti, and Ming Yuan. Non-convex projected gradient descent for generalized low-rank tensor regression. Journal of Machine Learning Research, 20(5):1–37, 2019. Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V Le, Sergey Levine, and Yi Ma. Sft memorizes, rl generalizes: A comparative study of foundation model post-training. arXiv preprint arXiv:2501.17161, 2025. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, a","arxiv_match":true},{"id":"LfIpbuIKLb","title":"Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Memory Requirements","forum_url":"https://openreview.net/forum?id=LfIpbuIKLb","excerpt":" Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V Le, Sergey Levine, and Yi Ma. Sft memorizes, rl generalizes: A comparative study of foundation model post-training. arXiv preprint arXiv:2501.17161, 2025. Shangheng Du, Jiabao Zhao, Jinxin Shi, Zhentao Xie, Xin Jiang, Yanhong Bai, and Liang He. A ","arxiv_match":true}],"id":"2501.17161","metadata_source":"Cached arXiv API metadata","rank":715},{"title":"Sleeper agents: Training deceptive LLMs that persist through safety training","arxiv_id":"2401.05566","year":2024,"url":"https://arxiv.org/abs/2401.05566","citing_submissions":147,"explicit_arxiv_matches":134,"examples":[{"id":"AnxK3Tb5VS","title":"To Ask or Not to Ask: Backdooring the Learned Consultation Policy of Collaborative LLM Agents","forum_url":"https://openreview.net/forum?id=AnxK3Tb5VS","excerpt":"38, 2025. Raghu Vamshi Hemadri, Humaira Firdowse Mohammed, Rishabh Maheshwary, Srivatsava Daruru, Sagar Davasam, Vikas Yadav, Srinivas Sunkara, and Sai Rajeswar. R2v agent: Teaching slms when to ask for help. arXiv preprint arXiv:2605.16604, 2026. Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, et al. Sleeper agents: Training deceptive llms that persist through safety training. arXiv preprint arXiv:2401.05566, 2024. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zh","arxiv_match":true},{"id":"yVGzyZ8zMc","title":"Offline Scores Overestimate Online Oversight: Timely Interception of LLM Agents with Causal-Cone Monitoring","forum_url":"https://openreview.net/forum?id=yVGzyZ8zMc","excerpt":"Matthew Hall, Federico Zarfati, Yonatan Zunger, and Emre Kiciman. Defending Against Indirect Prompt Injection Attacks With Spotlighting, 2024. URL https: //arxiv.org/abs/2403.14720. Preprint, Microsoft. Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, et al. Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, 2024. URL https://arxiv.org/abs/ 2401.05566. Anthropic. Alexis Huet, Jose Manuel Navarro, and Dario Rossi. Local Evaluation of Time Series Anom","arxiv_match":true},{"id":"UnQVghvXji","title":"Beyond OOD: Evaluating Large Foundation Models via Out-of-World Distribution","forum_url":"https://openreview.net/forum?id=UnQVghvXji","excerpt":" preprint arXiv:2503.24290, 2025. Thomas Hubert, Ramana Mehta, Laurent Sartran, et al. Olympiad-level formal mathematical reasoning with reinforcement learning. Nature, 651:607–613, 2026. doi: 10.1038/s41586-025-09833-y. Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, et al. Sleeper agents: Training deceptive llms that persist through safety training. arXiv preprint arXiv:2401.05566, 2024. Naman Jain, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando SolarLezama,","arxiv_match":true}],"id":"2401.05566","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":716},{"title":"Voxposer: Composable 3d value maps for robotic manipulation with language models","arxiv_id":"2307.05973","year":2023,"url":"https://arxiv.org/abs/2307.05973","citing_submissions":147,"explicit_arxiv_matches":75,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"Embodied Foundation Model Technical Report. arXiv preprint arXiv:2511.16518, 2025. URL https://arxiv.org/abs/ 2511.16518. Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li, Jiajun Wu, and Li Fei-Fei. VoxPoser: Composable 3D value maps for robotic manipulation with language models. In Proceedings of the 7th Conference on Robot Learning, volume 229 of Proceedings of Machine Learning Research, pp. 540–562, 2023. URL https://proceedings.mlr.press/v229/ huang23b.html. Baoxiong Jia","arxiv_match":false},{"id":"wvk6f9bAAD","title":"PerM4D: Persistent Memory for Streaming 4D Gaussian Splatting from Monocular Videos","forum_url":"https://openreview.net/forum?id=wvk6f9bAAD","excerpt":"rnational Conference on Computer Vision (ICCV), pp. 5692–5701. IEEE, 2021. Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, and Angjoo Kanazawa. Monocular dynamic view synthesis: A reality check. In Advances in Neural Information Processing Systems, volume 35, pp. 33768–33780, 2022. Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li, Jiajun Wu, and Li Fei-Fei. VoxPoser: Composable 3D value maps for robotic manipulation with language models. arXiv preprint arXiv:2307.05973, 2023. Wenlong Huang, Chen Wang, Yunzhu Li, Ruohan Zhang, and Li Fei-Fei. ReKep: Spatiotemporal rea","arxiv_match":true},{"id":"N2GAuIods8","title":"World Action Planner: Generalizable Robot Decision-Making with Action-Conditioned World Models","forum_url":"https://openreview.net/forum?id=N2GAuIods8","excerpt":"unction approximation. In International Conference on Machine Learning, pp. 8971–9019. PMLR, 2022. 18 Yingdong Hu, Fanqi Lin, Tong Zhang, Li Yi, and Yang Gao. Look before you leap: Unveiling the power of gpt-4v in robotic vision-language planning. arXiv preprint arXiv:2311.17842, 2023. 15 Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li, Jiajun Wu, and Li Fei-Fei. Voxposer: Composable 3d value maps for robotic manipulation with language models. arXiv preprint arXiv:2307.05973, 2023. 15 Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Dr","arxiv_match":true}],"id":"2307.05973","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":717},{"title":"A survey on the memory mechanism of large language model based agents","arxiv_id":"2404.13501","year":2024,"url":"https://arxiv.org/abs/2404.13501","citing_submissions":146,"explicit_arxiv_matches":37,"examples":[{"id":"L7iP938SBC","title":"Test-Time Memory Construction","forum_url":"https://openreview.net/forum?id=L7iP938SBC","excerpt":"ng-horizon agents. ArXiv preprint, abs/2601.06377, 2026. URL https://arxiv.org/abs/2601.06377. Zeyu Zhang, Quanyu Dai, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. A survey on the memory mechanism of large language model-based agents. TOIS, 43(6):1–47, 2025. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph Gonzalez, and Ion Stoica. ","arxiv_match":false},{"id":"mbmr0VIBUV","title":"Joint Agent Memory and Exploration Learning via Novelty Signals","forum_url":"https://openreview.net/forum?id=mbmr0VIBUV","excerpt":"76-390-6. doi: 10.18653/v1/2026.acl-long.981. URL https://aclanthology.org/2026.acl-long.981/. Zeyu Zhang, Quanyu Dai, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. A survey on the memory mechanism of large language model-based agents. ACM Trans. Inf. Syst., 43(6), September 2025. ISSN 1046-8188. doi: 10.1145/3748302. URL https://doi.org/10.1145/3748302. Zeyu Zhang, Rui Li, Xiaoyan Zhao, Yang Zhang, Wenjie Wang, Xu Chen, and Tat-S","arxiv_match":false},{"id":"CmUuIRtIeu","title":"Bayesian-Harness: Probabilistic Knowledge Control for LLM Agents","forum_url":"https://openreview.net/forum?id=CmUuIRtIeu","excerpt":"gs.iclr.cc/paper_files/paper/2026/file/ 8a94ff6f922d995d7d3f4ebf4143e442-Paper-Conference.pdf. Zeyu Zhang, Quanyu Dai, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. A survey on the memory mechanism of large language model-based agents. ACM Transactions on Information Systems, 43(6):1–47, November 2025. doi: 10.1145/3748302. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. ExpeL: LLM agents are expe","arxiv_match":false}],"id":"2404.13501","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":718},{"title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","arxiv_id":"1312.6120","year":2013,"url":"https://arxiv.org/abs/1312.6120","citing_submissions":146,"explicit_arxiv_matches":79,"examples":[{"id":"vWBRavgrbx","title":"Spectral Optimization in Residual Networks: Learning Anisotropic Corrections","forum_url":"https://openreview.net/forum?id=vWBRavgrbx","excerpt":"ional Conference on Machine Learning, 2025. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020. Andrew M Saxe, James L McClelland, and Surya Ganguli. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120, 2013. Ishaan Shah, Anthony M Polloreno, Karl Stratos, Philip Monk, Adarsh Chaluvaraju, Andrew Hoje","arxiv_match":true},{"id":"aXeWS1nXYM","title":"Attenuation or Removal? Calibrated Reading of Usable Conditional Information","forum_url":"https://openreview.net/forum?id=aXeWS1nXYM","excerpt":"rstanding contrastive learning requires incorporating inductive biases. In International Conference on Machine Learning, pp. 19250–19286, 2022. Andrew M. Saxe, James L. McClelland, and Surya Ganguli. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. In International Conference on Learning Representations, 2014. Simon Schrodi, David T. Hoffmann, Max Argus, Volker Fischer, and Thomas Brox. Two effects, one trigger: On the modality gap, object bia","arxiv_match":false},{"id":"EQZDpDpNR7","title":"Percolation Dynamics in Optimization: Variance Cascades and Nested Symmetry","forum_url":"https://openreview.net/forum?id=EQZDpDpNR7","excerpt":"lation is continuous. Science, 333:322–324, 2011. Hannes Risken. The Fokker-Planck Equation: Methods of Solution and Applications. Springer-Verlag, 1996. Gennady Samorodnitsky and Murad S. Taqqu. Stable Non-Gaussian Random Processes: Stochastic Models with Infinite Variance. Chapman & Hall, New York, 1994. Andrew M. Saxe, James L. McClelland, and Surya Ganguli. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120, 2013. Harshay Shah, Kaustubh Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli. The","arxiv_match":true}],"id":"1312.6120","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":719},{"title":"Learning by distilling context","arxiv_id":"2209.15189","year":2022,"url":"https://arxiv.org/abs/2209.15189","citing_submissions":146,"explicit_arxiv_matches":136,"examples":[{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":" Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Yuanyi Song, Yukai Wang, Xinbei Ma, Zhihui Fu, Jianghao Lin, Weiwen Liu, Jun Wang, Huarong Deng, Yong Yu, and Weinan Zhang. Retrieval-driven memory reconsolidation for long-term llm agents. arXiv preprint arXiv:2609.16053, 2026. Charlie Snell, Dan Klein, and Ruiqi Zhong. arXiv:2209.15189, 2022. Learning by distilling context. arXiv preprint Md Nayem Uddin, Kumar Shubham, Eduardo Blanco","arxiv_match":true},{"id":"AVKgs60Glj","title":"Flux-OPD: On-Policy Distillation with Evolving Contexts","forum_url":"https://openreview.net/forum?id=AVKgs60Glj","excerpt":"uan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Charlie Snell, Dan Klein, and Ruiqi Zhong. Learning by distilling context. CoRR, abs/2209.15189, 2022. doi: 10.48550/ARXIV.2209.15189. URL https://doi.org/10.48550/arXiv. 2209.15189. Zeqing Wang, Xinyu Wei, Bairui Li, Zhen Guo, Jinrui Zhang, Hongyang Wei, Keze Wang, and Le","arxiv_match":false},{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":"Son, Jiwoo Hong, Hyunwoo Ko, and James Thorne. Linguistic generalizability of test-time scaling in mathematical reasoning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 14333–14368, Vienna, Austria, 2025. Association for Computational Linguistics. doi: 10.18653/v1/2025.acl-long.699. URL https://aclanthology.org/2025.acl-long.699/. Charlie Snell, Dan Klein, and Ruiqi Zhong. arXiv:2209.15189, 2022. Learning by distilling context. arXiv preprint Kanghui Tian, Siyuan Liu, Ziang Yan, Sheng Xi","arxiv_match":true}],"id":"2209.15189","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":720},{"title":"Native and Compact Structured Latents for 3D Generation","arxiv_id":"2512.14692","year":2025,"url":"https://arxiv.org/abs/2512.14692","citing_submissions":146,"explicit_arxiv_matches":42,"examples":[{"id":"DmH3HpzlV6","title":"From Parts to Sim: Bottom-Up Autoregressive Generation of  Sim-Ready 3D Assets","forum_url":"https://openreview.net/forum?id=DmH3HpzlV6","excerpt":"1506, 2024. URL https://arxiv.org/abs/2412.01506. Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu, Yue Dong, Hao Zhao, Nicholas Jing Yuan, and Jiaolong Yang. Native and compact structured latents for 3D generation. Tech report, 2025. URL https://github.com/microsoft/ TRELLIS.2. Xianghui Yang, Huiwen Shi, Bowen Zhang, Fan Yang, Jiacheng Wang, Hongxu Zhao, Xinhai Liu, Xinzhou Wang, Qingxiang Lin, Jiaao Yu, Lifu ","arxiv_match":false},{"id":"6HZCBDqoCa","title":"DisCoMesh: Efficient and Flexible Artist-Mesh Generation via Sparse Computation powered Discrete Diffusion","forum_url":"https://openreview.net/forum?id=6HZCBDqoCa","excerpt":" and Pattern Recognition (CVPR), pp. 21469–21480. IEEE, 2025. Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu, Yue Dong, Hao Zhao, Nicholas Jing Yuan, et al. Native and compact structured latents for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14419–14429, 2026. Rui Xu, Tianyang Xue, Qiujie Dong, Le Wan, Zhe Zhu, Peng Li, Zhiyang Dou, Cheng Lin, Shiq","arxiv_match":false},{"id":"MOLTwuUqhX","title":"Intrinsic Alignment by Generation: Monocular Hand–Object Reconstruction via Interaction Scaffolds","forum_url":"https://openreview.net/forum?id=MOLTwuUqhX","excerpt":" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4840–4851, 2024. URL https://openaccess.thecvf.com/content/CVPR2024/html/Wu_ Point_Transformer_V3_Simpler_Faster_Stronger_CVPR_2024_paper. html. Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu, Yue Dong, Hao Zhao, Nicholas Jing Yuan, and Jiaolong Yang. Native and compact structured latents for 3d generation. arXiv preprint arXiv:2512.14692, 2025a. Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin ","arxiv_match":true}],"id":"2512.14692","metadata_source":"Cached arXiv API metadata","rank":721},{"title":"Prioritized experience replay","arxiv_id":"1511.05952","year":2015,"url":"https://arxiv.org/abs/1511.05952","citing_submissions":146,"explicit_arxiv_matches":76,"examples":[{"id":"j6wn1MIOB5","title":"Runtime Learning in Open World: Adaptive Blending and Cognitive Sampling","forum_url":"https://openreview.net/forum?id=j6wn1MIOB5","excerpt":"controller fusion: Leveraging control priors in deep reinforcement learning for robotics. arXiv 2107.09822, https://arxiv.org/pdf/2107.09822.pdf, 2021. Remo Sasso, Michelangelo Conserva, and Paulo Rauber. Posterior sampling for deep reinforcement learning. The Fortieth International Conference on Machine Learning, pp. 1–19, 2023. Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. Prioritized experience replay. Arxiv 1511.05952, https://arxiv.org/abs/1511.05952, 2016. Per B Sederberg, Marc W Howard, and Michael J Kahana. A context-based theory of recency and ","arxiv_match":true},{"id":"xLkfjG1wVy","title":"Efficient Long-Horizon Learning for Learned Optimization","forum_url":"https://openreview.net/forum?id=xLkfjG1wVy","excerpt":"al information processing systems, 32, 2019. [32] Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 627–635. JMLR Workshop and Conference Proceedings, 2011. [33] Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. Prioritized experience replay. arXiv preprint arXiv:1511.05952, 2015. [34] Simon Schmitt, Jonathan J. Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojcie","arxiv_match":true},{"id":"Nf0p2M2J1i","title":"Principled Data Generation for MetaBBO via Active Task Selection","forum_url":"https://openreview.net/forum?id=Nf0p2M2J1i","excerpt":"e on Artificial Intelligence, pp. 4819–4825, 2020. Raphael Patrick Prager and Heike Trautmann. Pflacco: Feature-based landscape analysis of continuous and constrained optimization problems in Python. Evolutionary Computation, pp. 1–25, 2023. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2006. Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. Prioritized experience replay. arXiv:1511.05952, 2015. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal poli","arxiv_match":true}],"id":"1511.05952","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":722},{"title":"$π^{*}_{0.6}$: a VLA That Learns From Experience","arxiv_id":"2511.14759","year":2025,"url":"https://arxiv.org/abs/2511.14759","citing_submissions":145,"explicit_arxiv_matches":125,"examples":[{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"y Kolobov, Jianlong Fu, and Youngwoon Lee. TwinVLA: Dataefficient bimanual manipulation with twin single-arm vision-language-action models. In International Conference on Learning Representations, 2026. URL https://arxiv.org/abs/ 2511.05275. Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, et al. pi06: a vla that learns from experience. arXiv preprint arXiv:2511.14759, 2025a. Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Drie","arxiv_match":true},{"id":"ygNU3le5s8","title":"Remember What Matters: On-Demand Memory for Long-Horizon Robotic Manipulation","forum_url":"https://openreview.net/forum?id=ygNU3le5s8","excerpt":"7. PMLR, 2022. Yaoting Huang, Yifu Yuan, Linqi Han, Chengwen Li, Shuoheng Zhang, Xianze Yao, Hongyao Tang, Yan Zheng, and Jianye Hao. Robopin: Grounded embodied reasoning via pinned chain-of-thought. arXiv preprint arXiv:2606.15753, 2026. Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, ∗ Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, et al. π0.6 : a vla that learns from experience. arXiv preprint arXiv:2511.14759, 2025a. Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Drie","arxiv_match":true},{"id":"6FEw1esZ1j","title":"Kuramoto Decision Diffusion Models","forum_url":"https://openreview.net/forum?id=6FEw1esZ1j","excerpt":"Advances in Neural Information Processing Systems, 34: 22863–22876, 2021. Cited on page 20. Baris Imre. An investigation of generative replay in deep reinforcement learning. In 34th Twente Student Conference on IT, 2021. Cited on page 21. Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, ∗ Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, et al. π0.6 : a vla that learns from experience. arXiv preprint arXiv:2511.14759, 2025a. Cited on page 1. Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dha","arxiv_match":true}],"id":"2511.14759","metadata_source":"Cached arXiv API metadata","rank":723},{"title":"On the opportunities and risks of foundation models","arxiv_id":"2108.07258","year":2021,"url":"https://arxiv.org/abs/2108.07258","citing_submissions":145,"explicit_arxiv_matches":133,"examples":[{"id":"bdZAOqTSho","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","forum_url":"https://openreview.net/forum?id=bdZAOqTSho","excerpt":" Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. In NeurIPS, 2024. Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, and Stella Biderman. LEACE: Perfect linear concept erasure in closed form. In NeurIPS, 2023. Rishi Bommasani et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021. Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Tr","arxiv_match":true},{"id":"bw7hexJ2fs","title":"Training-Free Transformer Merging via Sequential Local Operator Alignment","forum_url":"https://openreview.net/forum?id=bw7hexJ2fs","excerpt":" Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa. Git re-basin: Merging models modulo permutation symmetries. In The Eleventh International Conference on Learning Representations, 2023. Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021. Joachim Bona-Pellissier, François Bachoc, and François Malgouyres. Parameter identifiabili","arxiv_match":true},{"id":"xmEaMO4noX","title":"Skill Training with Corruption and Reconstruction Loop","forum_url":"https://openreview.net/forum?id=xmEaMO4noX","excerpt":"Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang. On the opportunities and risks of foundation models, 2022. URL https://arxiv.org/abs/2108.07258. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind ","arxiv_match":true}],"id":"2108.07258","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":724},{"title":"What matters in learning from offline human demonstrations for robot manipulation","arxiv_id":"2108.03298","year":2021,"url":"https://arxiv.org/abs/2108.03298","citing_submissions":145,"explicit_arxiv_matches":58,"examples":[{"id":"fKq8qGzBwD","title":"Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning","forum_url":"https://openreview.net/forum?id=fKq8qGzBwD","excerpt":"bs/2210.02747. Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning, 2023. URL https://arxiv. org/abs/2306.03310. Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li FeiFei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín. What matters in learning from offline human demonstrations for robot manipulation. In arXiv preprint arXiv:2108.03298, 2021. D.A. Nix and A.S. Weigend. Estimating the mean and variance of the target probability distri","arxiv_match":true},{"id":"ovv7oYCkNk","title":"Real-Time Action Chunking for Relative-Action Robot Policies","forum_url":"https://openreview.net/forum?id=ovv7oYCkNk","excerpt":" Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. Ilya Loshchilov and Frank Hutter. arXiv:1711.05101, 2017. Decoupled weight decay regularization. arXiv preprint Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li FeiFei, Silvio Savarese, Yuke Zhu, and Roberto Martı́n-Martı́n. What matters in learning from offline human demonstrations for robot manipulation. arXiv preprint arXiv:2108.03298, 2021. Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Y","arxiv_match":true},{"id":"6FEw1esZ1j","title":"Kuramoto Decision Diffusion Models","forum_url":"https://openreview.net/forum?id=6FEw1esZ1j","excerpt":"Lyu, Yunfei Li, Yu Luo, Fuchun Sun, Tao Kong, Jiafeng Xu, and Xiao Ma. Flow-based policy for online reinforcement learning. Advances in Neural Information Processing Systems, 38: 93967–93990, 2026. Cited on page 21. Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li FeiFei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín. What matters in learning from offline human demonstrations for robot manipulation. arXiv preprint arXiv:2108.03298, 2021. Cited on pages 8 and 37. Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising di","arxiv_match":true}],"id":"2108.03298","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":725},{"title":"Mm-vet: Evaluating large multimodal models for integrated capabilities","arxiv_id":"2308.02490","year":2023,"url":"https://arxiv.org/abs/2308.02490","citing_submissions":144,"explicit_arxiv_matches":67,"examples":[{"id":"e4gvA0ZGY5","title":"ReasonRSOS: Reasoning Salient Object Segmentation in Remote Sensing Images","forum_url":"https://openreview.net/forum?id=e4gvA0ZGY5","excerpt":" the IEEE/CVF conference on computer vision and pattern recognition, pp. 18155–18165, 2022. Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: evaluating large multimodal models for integrated capabilities. In Proceedings of the 41st International Conference on Machine Learning, ICML’24. JMLR.org, 2024. Zhenghang Yuan, Lichao Mou, Yuansheng Hua, and Xiao Xiang Zhu. Rrsis: Referring remote sensing image","arxiv_match":false},{"id":"YOc8jsq9dV","title":"OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing","forum_url":"https://openreview.net/forum?id=YOc8jsq9dV","excerpt":"https://arxiv.org/abs/2502.14786. Longrong Yang, Dong Shen, Chaoxiang Cai, Fan Yang, Tingting Gao, Di Zhang, and Xi Li. Solving token gradient conflict in mixture-of-experts for large vision-language model, 2025. URL https: //arxiv.org/abs/2406.19905. Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities, 2024. URL https://arxiv.org/abs/2308.02490. Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jian","arxiv_match":true},{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"-0. doi: 10.18653/v1/2025.acl-long.1354. URL https://aclanthology. org/2025.acl-long.1354/. Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: evaluating large multimodal models for integrated capabilities. In Proceedings of the 41st International Conference on Machine Learning, ICML’24. JMLR.org, 2024. Zhaojian Yu, Penghao Yin, Shuzheng Gao, Shilin He, Kai Cai, and Xiao-Ping Zhang. AutoTrainess: Teach","arxiv_match":false}],"id":"2308.02490","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":726},{"title":"StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing","arxiv_id":"2604.05014","year":2026,"url":"https://arxiv.org/abs/2604.05014","citing_submissions":144,"explicit_arxiv_matches":139,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":" Can Be Generalizable Robot Manipulators. In Advances in Neural Information Processing Systems, volume 38, pp. 95597–95621. Curran Associates, Inc., 2025. doi: 10.52202/085713-3197. URL https://proceedings.neurips.cc/paper_files/paper/2025/hash/ 89a3b655a8b68ae1c76b768152c9c19d-Abstract-Conference.html. StarVLA Community. StarVLA: A lego-like codebase for vision-language-action model developing. arXiv preprint arXiv:2604.05014, 2026. URL https://arxiv.org/abs/2604.05014. Jingwen Sun, Wenyao Zhang, Zekun Qi, Shaojie Ren, Zezhi Liu, Hanxin Zhu, Guangzhong Sun, Xin Jin, ","arxiv_match":true},{"id":"dVXFFMVuTo","title":"ManiUnit: A Manipulation Skill Dataset and Benchmark for Long-Horizon Tasks","forum_url":"https://openreview.net/forum?id=dVXFFMVuTo","excerpt":" for robotic manipulation. In Proceedings of Robotics: Science and Systems, 2024. Lucy Xiaoyang Shi et al. Hi Robot: Open-ended instruction following with hierarchical visionlanguage-action models. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 54919–54933. PMLR, 2025. StarVLA Community. StarVLA: A lego-like codebase for vision-language-action model developing. arXiv preprint arXiv:2604.05014, 2026. Shihan Wu et al. RoboCOIN: An open-sourced bimanual robotic data collection for integrated m","arxiv_match":true},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"omain randomization for robust bimanual robotic manipulation. arXiv preprint arXiv:2506.18088, 2025. Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research, 44(10-11):1684–1704, 2025. StarVLA Community. Starvla: A lego-like codebase for vision-language-action model developing. arXiv preprint arXiv:2604.05014, 2026. Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine. Visual ","arxiv_match":true}],"id":"2604.05014","metadata_source":"Cached arXiv API metadata","rank":727},{"title":"Train short, test long: Attention with linear biases enables input length extrapolation","arxiv_id":"2108.12409","year":2021,"url":"https://arxiv.org/abs/2108.12409","citing_submissions":144,"explicit_arxiv_matches":51,"examples":[{"id":"PCAODQ61Qw","title":"BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation","forum_url":"https://openreview.net/forum?id=PCAODQ61Qw","excerpt":"anguage Processing, pp. 18724–18741, 2024. Guilherme Penedo, Hynek Kydlı́ček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf. The fineweb datasets: Decanting the web for the finest text data at scale, 2024. URL https://arxiv.org/abs/2406.17557. Ofir Press, Noah A. Smith, and Mike Lewis. Train short, test long: Attention with linear biases enables input length extrapolation, 2022. URL https://arxiv.org/abs/2108.12409. W. Rudin. Principles of Mathematical Analysis. International series in pure and applied mathematic","arxiv_match":true},{"id":"gPARqBeh10","title":"MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Coupling","forum_url":"https://openreview.net/forum?id=gPARqBeh10","excerpt":"lvaro Sanchez-Gonzalez, and Peter Battaglia. Learning mesh-based simulation with graph networks. In International Conference on Learning Representations, 2021. Ofir Press, Noah Smith, and Mike Lewis. Train short, test long: Attention with linear biases enables input length extrapolation. In International Conference on Learning Representations, 2022. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring ","arxiv_match":false},{"id":"2lkg5Mj3C8","title":"Ask Self, Ask Others: Relation Is All You Need","forum_url":"https://openreview.net/forum?id=2lkg5Mj3C8","excerpt":"apers), pp. 15632–15654. Association for Computational Linguistics, 2023. doi: 10.18653/v1/ 2023.acl-long.871. URL https://aclanthology.org/2023.acl-long.871/. Ofir Press, Noah Smith, and Mike Lewis. Train short, test long: Attention with linear biases enables input length extrapolation. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=R8sQPpGCv0. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena,","arxiv_match":false}],"id":"2108.12409","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":728},{"title":"Active learning for convolutional neural networks: A core-set approach","arxiv_id":"1708.00489","year":2017,"url":"https://arxiv.org/abs/1708.00489","citing_submissions":143,"explicit_arxiv_matches":45,"examples":[{"id":"4FSkQHh0Cu","title":"Proportional Clustering: Verification and Optimal Selection","forum_url":"https://openreview.net/forum?id=4FSkQHh0Cu","excerpt":"s to combinatorial problems. Management Science, 22(11):1268–1272, 1976. Raman Sanyal, Frank Sottile, and Bernd Sturmfels. Orbitopes. Mathematika, 57(2):275–314, 2011. Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In Proceedings of the 6th International Conference on Learning Representations (ICLR), 2018. Drew Springham. Faster verification of PJR+ via mincuts, 2026. arXiv:2609.20579. Drew Springham, Edith El","arxiv_match":false},{"id":"iBXhdO0M1o","title":"EDGE: RESIDUAL-CONDITIONED CORRECTION ACQUISITION FOR VLA POST-TRAINING","forum_url":"https://openreview.net/forum?id=iBXhdO0M1o","excerpt":"online learning. In International Conference on Artificial Intelligence and Statistics, volume 15 of Proceedings of Machine Learning Research, pp. 627–635. PMLR, 2011. Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations, 2018. Burr Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison, 2009. Mustaf","arxiv_match":false},{"id":"bpZ7gnPe7g","title":"Mind the Tail: Dataset Selection Through Sparse Concepts","forum_url":"https://openreview.net/forum?id=bpZ7gnPe7g","excerpt":"Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. In ICLR, 2020. Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In ICLR, 2018. Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Da","arxiv_match":false}],"id":"1708.00489","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":729},{"title":"Timestep Embedding Tells: It’s Time to Cache for Video Diffusion Model","arxiv_id":"2411.19108","year":2024,"url":"https://arxiv.org/abs/2411.19108","citing_submissions":143,"explicit_arxiv_matches":31,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"s of the Computer Vision and Pattern Recognition Conference, pp. 9131– 9141, 2025b. Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It’s time to cache for video diffusion model. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 7353– 7363, 2025a. Xuewen Liu, Zhikai Li, Junrui Xiao, and Qingyi Gu. Eda-dm: Enhanced distribution alignment for post-t","arxiv_match":false},{"id":"mWR71nELqS","title":"One Budget, Many Requests: Joint Compute Allocation for Diffusion Caching","forum_url":"https://openreview.net/forum?id=mWR71nELqS","excerpt":"6. Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022. Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It’s time to cache for video diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025a. URL https://arxiv.org/abs/2411.19108. Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu, Junjie Chen, and Linfeng Zhang. From reusing to forecastin","arxiv_match":true},{"id":"p6thn2PhEo","title":"Don't Throw Away the Solution: Training-Free Acceleration of Flow-Based Robot Policies","forum_url":"https://openreview.net/forum?id=p6thn2PhEo","excerpt":"ces in Neural Information Processing Systems (Datasets and Benchmarks Track), 2023. Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It’s time to cache for video diffusion model. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025a. Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie, Yoonho Lee, Maximilian Du, and Chelsea Finn. Bidirectional decoding: Improving a","arxiv_match":false}],"id":"2411.19108","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":730},{"title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","arxiv_id":"1707.01926","year":2017,"url":"https://arxiv.org/abs/1707.01926","citing_submissions":142,"explicit_arxiv_matches":31,"examples":[{"id":"vjXJ5SFc3S","title":"CausalGap: Benchmarking Representation Responses in Traffic Prediction under Structural Interventions","forum_url":"https://openreview.net/forum?id=vjXJ5SFc3S","excerpt":"onal similarity analysisconnecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2:4, 2008. doi: 10.3389/neuro.06.004.2008. Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In Proceedings of the International Conference on Learning Representations, 2018. URL https://openreview.net/forum?id=SJiHXGWAZ. Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu,","arxiv_match":false},{"id":"6J1jeyymEe","title":"Matching Scales to Patterns: Soft-Segmented Multi-scale Experts for Irregular Multivariate Time Series Forecasting","forum_url":"https://openreview.net/forum?id=6J1jeyymEe","excerpt":" forecasting. In International Conference on Machine Learning, pp. 35502–35518. PMLR, 2025. Boyuan Li, Zhen Liu, Yicheng Luo, and Qianli Ma. Learning recursive multi-scale representations for irregular multivariate time series forecasting. In The Fourteenth International Conference on Learning Representations, 2026. Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, 2017. Shengsheng Lin, Haojun Chen, Haijie Wu, Chunyun Qiu, and Weiwei Lin. Temporal query network ","arxiv_match":true},{"id":"1mVOXtbUnS","title":"Multimodal Influence Learning for Urban Spatio-Temporal Forecasting: A Cross-City Foundation Model and Dataset","forum_url":"https://openreview.net/forum?id=1mVOXtbUnS","excerpt":"1– 13066, 2024. doi: 10.1109/TITS.2024.3390185. URL https://doi.org/10.1109/TITS .2024.3390185. Yuting Huang, Ziquan Fang, Zhihao Zeng, Lu Chen, and Yunjun Gao. Causal spatio-temporal prediction: an effective and efficient multi-modal approach. Advances in Neural Information Processing Systems, 38:125786–125812, 2026. Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, 2017. Zhonghang Li, Long Xia, Lei Shi, Yong Xu, Dawei Yin, and Chao Huang. Open spatio-temporal fo","arxiv_match":true}],"id":"1707.01926","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":731},{"title":"Galore: Memory-efficient llm training by gradient low-rank projection","arxiv_id":"2403.03507","year":2024,"url":"https://arxiv.org/abs/2403.03507","citing_submissions":142,"explicit_arxiv_matches":53,"examples":[{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"ransformers: State-of-the-art natural language processing, 2019. B. Zhang and R. Sennrich. Root mean square layer normalization, 2019. J. Zhao, Z. Zhang, B. Chen, Z. Wang, A. Anandkumar, and Y. Tian. GaLore: Memory-efficient LLM training by gradient low-rank projection. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, and F. Berkenkamp, editors, Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedi","arxiv_match":false},{"id":"W1kmyDnBQE","title":"DAWN: Scalable Matrix-Whitening Optimization via Spectral Selectivity","forum_url":"https://openreview.net/forum?id=W1kmyDnBQE","excerpt":"al. Glm-4.5: Agentic, reasoning, and coding (arc) foundation models. arXiv preprint arXiv:2508.06471, 2025. Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, and Yuandong Tian. Galore: Memory-efficient llm training by gradient low-rank projection. 2024. A E XTENDED R ELATED W ORK We provide extended discussion of related methods, organized into four themes, with connections to the head-tail framework. A.1 S ECOND -O RDER O PTIMIZATION M ETHOD","arxiv_match":false},{"id":"mczw113vfG","title":"On-Policy Parameter Update Direction Underlies Generalization in LLM Post-Training","forum_url":"https://openreview.net/forum?id=mczw113vfG","excerpt":"and Shanghang Zhang. Gradient-based parameter selection for efficient fine-tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 28566–28577, 2024. Yifan Zhang and Team Math-AI. American invitational mathematics examination (aime) 2025, 2025. Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, and Yuandong Tian. Galore: Memory-efficient llm training by gradient low-rank projection. arXiv preprint arXiv:2403.03507, 2024. Shiwan Zhao, Zhihu Wang, Xuyang Zhao, Jiaming Zhou, Caiyue Xu, Chenfei Liu, Liting Zhang, Yu","arxiv_match":true}],"id":"2403.03507","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":732},{"title":"WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling","arxiv_id":"2512.14614","year":2025,"url":"https://arxiv.org/abs/2512.14614","citing_submissions":142,"explicit_arxiv_matches":107,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":" Jiahui Huang, Huan Ling, Jun Gao, and Xuanchi Ren. Lyra 2.0: Explorable generative 3d worlds. arXiv preprint arXiv:2604.13036, 2026. URL https://arxiv.org/abs/2604.13036. Wenqiang Sun, Haiyu Zhang, Haoyuan Wang, Junta Wu, Zehan Wang, Zhenwei Wang, Yunhong Wang, Jun Zhang, Tengfei Wang, and Chunchao Guo. WorldPlay: Towards long-term geometric consistency for real-time interactive world modeling. arXiv preprint arXiv:2512.14614, 2025. URL https://arxiv.org/abs/2512.14614. Stanislaw Szymanowicz, Jason Y. Zhang, Pratul Srinivasan, Ruiqi Gao, Arthur Brussee, Aleksander Ho","arxiv_match":true},{"id":"3nSV2brMqb","title":"PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives","forum_url":"https://openreview.net/forum?id=3nSV2brMqb","excerpt":" (CVPR), pp. 8406–8416, 2025a. doi: 10.1109/CVPR52734.2025.00787. Wenqiang Sun, Haiyu Zhang, Haoyuan Wang, Junta Wu, Zehan Wang, Zhenwei Wang, Yunhong Wang, Jun Zhang, Tengfei Wang, and Chunchao Guo. WorldPlay: Towards long-term geometric consistency for real-time interactive world modeling, 2025b. URL https://arxiv.org/ab s/2512.14614. Team HY-World, Chenjie Cao, Xuhui Zuo, Zhenwei Wang, Yisu Zhang, Junta Wu, Zhenyang Liu, Yuning Gong, Yang Liu, Bo Yuan, et al. HY-World 2.0: A multi-mo","arxiv_match":false},{"id":"Rf3L7Jkvg7","title":"L3G-World: Real-Time Infinite World Generation with Latent 3D Gaussian Memory","forum_url":"https://openreview.net/forum?id=Rf3L7Jkvg7","excerpt":"jcic, et al. Lyra 2.0: Explorable generative 3d worlds. arXiv preprint arXiv:2604.13036, 2026. Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding, 2021. Wenqiang Sun, Haiyu Zhang, Haoyuan Wang, Junta Wu, Zehan Wang, Zhenwei Wang, Yunhong Wang, Jun Zhang, Tengfei Wang, and Chunchao Guo. Worldplay: Towards long-term geometric consistency for real-time interactive world modeling. arXiv preprint arXiv:2512.14614, 2025. Robbyant Team, Zelin Gao, Qiuyu Wang, Yanhong Zeng, Jiapeng Zhu, Ka Leong Cheng, Yixuan Li, ","arxiv_match":true}],"id":"2512.14614","metadata_source":"Cached arXiv API metadata","rank":733},{"title":"Gymnasium: A standard interface for reinforcement learning environments","arxiv_id":"2407.17032","year":2024,"url":"https://arxiv.org/abs/2407.17032","citing_submissions":141,"explicit_arxiv_matches":76,"examples":[{"id":"wgxqE0mSam","title":"PP-Gym: Unifying Environments, Learning, and Evaluation for Performative Prediction","forum_url":"https://openreview.net/forum?id=wgxqE0mSam","excerpt":"ianluca De Cola, Tristan Deleu, Manuel Goulão, Kallinteris Andreas, Markus Krimmel, Arjun KG, Rodrigo Perez-Vicente, J Terry, Andrea Pierré, Sander Schulhoff, Jun Jet Tai, Hannah Tan, Omar G. Younis. Gymnasium: A Standard Interface for Reinforcement Learning Environments. Advances in Neural Information Processing Systems, 38, Main Conference, 2025. Nikita Tsoy, Ivan Kirev, Negin Rahimiyazdi, Nikola Konstantinov. On the Impact of Performative Risk Minimization for Bin","arxiv_match":false},{"id":"6FEw1esZ1j","title":"Kuramoto Decision Diffusion Models","forum_url":"https://openreview.net/forum?id=6FEw1esZ1j","excerpt":"00482, 2023. Cited on page 20. Mark Towers, Ariel Kwiatkowski, John Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Kallinteris Andreas, Markus Krimmel, Arjun Kg, Rodrigo Perez-Vicente, et al. Gymnasium: A standard interface for reinforcement learning environments. Advances in Neural Information Processing Systems, 38, 2026. Cited on page 36. Brian L Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi S Jaakkola. Diffusio","arxiv_match":false},{"id":"IewWgkhQQH","title":"Returning Skill Discovery","forum_url":"https://openreview.net/forum?id=IewWgkhQQH","excerpt":"print arXiv:1805.11074, 2018. Mark Towers, Ariel Kwiatkowski, John Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Kallinteris Andreas, Markus Krimmel, Arjun Kg, Rodrigo Perez-Vicente, et al. Gymnasium: A standard interface for reinforcement learning environments. Advances in Neural Information Processing Systems, 38, 2026. Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and ","arxiv_match":false}],"id":"2407.17032","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":734},{"title":"Livebench: A challenging, contamination-limited llm benchmark","arxiv_id":"2406.19314","year":2024,"url":"https://arxiv.org/abs/2406.19314","citing_submissions":141,"explicit_arxiv_matches":46,"examples":[{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"rt. arXiv preprint arXiv:2607.02770, 2026. Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, et al. Livebench: A challenging, contamination-limited llm benchmark. In International Conference on Learning Representations, volume 2025, pp. 91595–91631, 2025. Tianze Xu, Yanzhao Zheng, Pengrui Lu, Lyumanshan Ye, Yong Wu, Zhentao Zhang, Yuanqiang Yu, Chao Ma, Jihua","arxiv_match":false},{"id":"g0vDUvCHlP","title":"Video-Index: A Curated Meta-Benchmark for Video Understanding Evaluation","forum_url":"https://openreview.net/forum?id=g0vDUvCHlP","excerpt":"ls. arXiv preprint arXiv:2605.07872, 2026. Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, et al. Livebench: A challenging, contamination-limited llm benchmark. In International Conference on Learning Representations, volume 2025, pp. 91595–91631, 2025. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac,","arxiv_match":false},{"id":"fFJ4m1Ru9U","title":"Hypothesis Testing with Conditional Queries: Learnability and the Value of Interaction","forum_url":"https://openreview.net/forum?id=fFJ4m1Ru9U","excerpt":" Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh-Agrawal, Sandeep Singh Sandha, Siddartha Venkat Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, and Micah Goldblum. LiveBench: A challenging, contamination-limited LLM benchmark. In International Conference on Learning Representations, 2025. David Bruce Wilson. How to couple from the past using a read-once source of randomness. Random Structures & Algorithms, 16(1):85–113, 2","arxiv_match":false}],"id":"2406.19314","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":735},{"title":"Teaching models to express their uncertainty in words","arxiv_id":"2205.14334","year":2022,"url":"https://arxiv.org/abs/2205.14334","citing_submissions":141,"explicit_arxiv_matches":54,"examples":[{"id":"FVJBqG73i0","title":"AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents","forum_url":"https://openreview.net/forum?id=FVJBqG73i0","excerpt":"Team. Kimi K2.5: visual agentic intelligence. CoRR, abs/2602.02276, 2026. doi: 10.48550/ ARXIV.2602.02276. URL https://doi.org/10.48550/arXiv.2602.02276. Stephanie Lin, Jacob Hilton, and Owain Evans. Teaching models to express their uncertainty in words. Transactions on Machine Learning Research, 2022. URL https://openreview.net/ forum?id=8s8K2UZGTZ. Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Ch","arxiv_match":false},{"id":"hlquBnFSq0","title":"A Multidimensional View of LLM Uncertainty Quantification via Scenario Interventions","forum_url":"https://openreview.net/forum?id=hlquBnFSq0","excerpt":"earch. Transactions of the Association for Computational Linguistics, 7:452–466, 2019. doi: 10.1162/tacl_a_00276. URL https://aclanthology.org/Q19-1026/. Stephanie Lin, Jacob Hilton, and Owain Evans. Teaching models to express their uncertainty in words. Transactions on Machine Learning Research, 2022. URL https://openreview. net/forum?id=8s8K2UZGTZ. Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. Contextualized sequence likelihood: Enhanced confidence","arxiv_match":false},{"id":"WGJJwO1uwj","title":"Dispersion Is Not Doubt: Rethinking LLM Confidence Estimation When Multiple Answers Are Valid","forum_url":"https://openreview.net/forum?id=WGJJwO1uwj","excerpt":"ation for Computational Linguistics, 7:453–466, 2019. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium on operating systems principles, pp. 611–626, 2023. Stephanie Lin, Jacob Hilton, and Owain Evans. Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334, 2022. Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao O","arxiv_match":true}],"id":"2205.14334","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":736},{"title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","arxiv_id":"1803.03635","year":2018,"url":"https://arxiv.org/abs/1803.03635","citing_submissions":141,"explicit_arxiv_matches":41,"examples":[{"id":"HsVELVeqrx","title":"Relative Weight Scales Control Feature Specialization in ReLU Networks","forum_url":"https://openreview.net/forum?id=HsVELVeqrx","excerpt":"hmic regularization in learning deep homogeneous models: Layers are automatically balanced. In Advances in Neural Information Processing Systems, volume 31, 2018. Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. In International Conference on Learning Representations, 2019. URL https:// openreview.net/forum?id=rJl-b3RcF7. Arthur Jacot, Franck Gabriel, and Clément Hongler. Neural tangent kernel: Convergence ","arxiv_match":false},{"id":"9VVpHOaYp9","title":"Towards Building Non-Fine-Tunable Foundation Models","forum_url":"https://openreview.net/forum?id=9VVpHOaYp9","excerpt":"tion agentic capabilities. arXiv preprint arXiv:2507.06261, 2025. Jiangyi Deng, Shengyuan Pang, Yanjiao Chen, Liangming Xia, Yijie Bai, Haiqin Weng, and Wenyuan Xu. Sophon: Non-fine-tunable learning to restrain task transferability for pre-trained models. In Proceedings of the IEEE Symposium on Security and Privacy, pp. 2553–2571, 2024. Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635, 2018. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin. Stabilizing ","arxiv_match":true},{"id":"JNo1goQAdN","title":"GRIK: Grouped Relevance-Integrated Key Pruning for Grouped-Query Attention LLMs","forum_url":"https://openreview.net/forum?id=JNo1goQAdN","excerpt":"-KV: Optimizing KV cache eviction by adaptive budget allocation for efficient LLM inference. In Advances in Neural Information Processing Systems (NeurIPS), 2025. Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. In International Conference on Learning Representations (ICLR), 2019. Elias Frantar and Dan Alistarh. SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 4","arxiv_match":false}],"id":"1803.03635","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":737},{"title":"Transfusion: Predict the next token and diffuse images with one multi-modal model","arxiv_id":"2408.11039","year":2024,"url":"https://arxiv.org/abs/2408.11039","citing_submissions":141,"explicit_arxiv_matches":53,"examples":[{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"anding of scenes through the ade20k dataset. IJCV, 2019. Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. In ICLR, 2025. Liu Ziyin and Isaac Chuang. Proof of a perfect platonic representation hypothesis. arXiv preprint arXiv:2507.01098, 2025. A R ELATED W ORK A.1 U NIFIED MODELS Multimodal models have r","arxiv_match":false},{"id":"KHODv83hPB","title":"IntReal: A Real-World Dataset and Benchmark for Multimodal Interleaved Generation","forum_url":"https://openreview.net/forum?id=KHODv83hPB","excerpt":"nal Conference on Learning Representations (ICLR), 2026. Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. In International Conference on Learning Representations, volume 2025, pp. 6446–6469, 2025a. Pengfei Zhou, Xiaopeng Peng, Jiajun Song, Chuanhao Li, Zhaopan Xu, Yue Yang, Ziyao Guo, Hao Zhang, Yuqi Li","arxiv_match":false},{"id":"DmH3HpzlV6","title":"From Parts to Sim: Bottom-Up Autoregressive Generation of  Sim-Ready 3D Assets","forum_url":"https://openreview.net/forum?id=DmH3HpzlV6","excerpt":" generation, 2025. URL https://arxiv.org/abs/2501.12202. Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. In International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=SI2hI0frk6. Matt Zhou, Ruining Li, Xiaoyang Lyu, Zhaomou Song, Zhening Huang, Chuanxia Zheng, Chris","arxiv_match":false}],"id":"2408.11039","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":738},{"title":"VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness","arxiv_id":"2503.21755","year":2025,"url":"https://arxiv.org/abs/2503.21755","citing_submissions":141,"explicit_arxiv_matches":133,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":", Akide Liu, Yinghao Yu, Jiasheng Tang, Fan Wang, Wei Wang, and Bohan Zhuang. WorldOlympiad: Can your world model survive a triathlon? arXiv preprint arXiv:2606.11129, 2026. Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Lulu Gu, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng, Yu Qiao, and Ziwei Liu. VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness. arXiv preprint arXiv:2503.21755, 2025. URL https: //arxiv.org/abs/2503.21755. Haoyi Zhu, Haozhe Liu, Yuyang Zhao, Tian Ye, Junsong Chen, Jincheng Yu, Tong He, Song Han, and Enz","arxiv_match":true},{"id":"UmYBTBeBpA","title":"Timing Is Not Enough: Mitigating Concurrent Action Omission in Video Generation","forum_url":"https://openreview.net/forum?id=UmYBTBeBpA","excerpt":"e/ 6427302778de982f3bb70fa73dfc7861-Paper-Conference.pdf. Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Lu-Lu Gu, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng, Yu Qiao, and Ziwei Liu. Vbench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness. ArXiv, abs/2503.21755, 2025. URL https://api. semanticscholar.org/CorpusID:277350030. A Splits. Our benchmark consists of 84 prompts in two splits, which Table 5 summarizes together with the single-","arxiv_match":false},{"id":"bI2MFCxHdB","title":"Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion","forum_url":"https://openreview.net/forum?id=bI2MFCxHdB","excerpt":"Information Processing Systems, 2025a. Tianyuan Zhang, Sai Bi, Yicong Hong, Kai Zhang, Fujun Luan, Songlin Yang, Kalyan Sunkavalli, William T Freeman, and Hao Tan. Test-time training done right. arXiv preprint arXiv:2505.23884, 2025b. Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng, Yu Qiao, and Ziwei Liu. VBench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness. arXiv preprint arXiv:2503.21755, 2025. Jinsong Zhou, Yihua Du, Xinli Xu, Luozhou Wang, Zijie Zhuang, Yehang Zhang, Shuaibo Li, Xiao","arxiv_match":true}],"id":"2503.21755","metadata_source":"Cached arXiv API metadata","rank":739},{"title":"CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models","arxiv_id":"2503.22020","year":2025,"url":"https://arxiv.org/abs/2503.22020","citing_submissions":140,"explicit_arxiv_matches":27,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":" Information Processing Systems, 37:105345– 105374, 2024. Michał Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, and Sergey Levine. Robotic control via embodied chain-of-thought reasoning. arXiv preprint arXiv:2407.08693, 2024. Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma, Song Han, Chelsea Finn, et al. Cot-vla: Visual chain-of-thought reasoning for visionlanguage-action models. arXiv preprint arXiv:2503.22020, 2025. Meng Zheng, Samhita Marri, Anwesa Choudhuri, Benjamin Planche, Zhongpai Gao, Van Nguyen Nguy","arxiv_match":true},{"id":"2MLZ8LegrJ","title":"Gated Depth Attention: Preserving Vision-Language Understanding for Generalist Robot Policies","forum_url":"https://openreview.net/forum?id=2MLZ8LegrJ","excerpt":"F international conference on computer vision, pp. 11975–11986, 2023. Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma, Song Han, Chelsea Finn, et al. CoT-VLA: Visual chain-of-thought reasoning for vision-language-action models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 1702–1713, 2025. Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan W","arxiv_match":false},{"id":"wEc8DpSJ41","title":"GF-VLA: Dual Geometry Forcing for Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=wEc8DpSJ41","excerpt":"Comprehensive World Knowledge. arXiv preprint arXiv:2507.04447, 2025. Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma, Song Han, Chelsea Finn, et al. Cot-vla: Visual chain-of-thought reasoning for visionlanguage-action models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1702–1713. IEEE, 2025. A This section specifies how the geometric targets are constructed and summarizes the traini","arxiv_match":false}],"id":"2503.22020","metadata_source":"Cached arXiv API metadata","rank":740},{"title":"Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models","arxiv_id":"2502.14819","year":2025,"url":"https://arxiv.org/abs/2502.14819","citing_submissions":140,"explicit_arxiv_matches":48,"examples":[{"id":"YAg8ELQnSW","title":"Parallel Predictive World Models for Accurate and Efficient Long-Horizon Planning","forum_url":"https://openreview.net/forum?id=YAg8ELQnSW","excerpt":"nt-space collocation. In International Conference on Machine Learning, pages 9190–9201. PMLR, 2021. Uladzislau Sobal, Wancong Zhang, Kyunghyun Cho, Randall Balestriero, Tim GJ Rudner, and Yann LeCun. Learning from reward-free offline data: A case for planning with latent dynamics models. Advances in Neural Information Processing Systems, 38:43905–43941, 2026. Anne Somalwar, Bruce D Lee, George J Pappas, and Nikolai Matni. Learning with imperfect models: When multi-step prediction mi","arxiv_match":false},{"id":"vBuP2bGyw9","title":"Post-Training Multimodal Embedding Models towards Physical Dynamics Awareness","forum_url":"https://openreview.net/forum?id=vBuP2bGyw9","excerpt":"is Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. arXiv preprint arXiv:1911.08265, 2019. URL https://arxiv.org/abs/1911.08265. Vlad Sobal, Wancong Zhang, Kyunghyun Cho, Randall Balestriero, Tim G. J. Rudner, and Yann LeCun. Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models. arXiv preprint arXiv:2502.14819, 2025. URL https://arxiv.org/abs/2502. 14819. Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikh","arxiv_match":true},{"id":"G8WCdaDci4","title":"JEPA-x: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics","forum_url":"https://openreview.net/forum?id=G8WCdaDci4","excerpt":"planning with a learned model. Nature, 588(7839):604–609, 2020. doi: 10.1038/s41586-020-03051-4. Uladzislau Sobal, Wancong Zhang, Kyunghyun Cho, Randall Balestriero, Tim G. J. Rudner, and Yann LeCun. Learning from reward-free offline data: A case for planning with latent dynamics models. In Advances in Neural Information Processing Systems, volume 38, pp. 43905–43941. Curran Associates, Inc., 2025. doi: 10.52202/085713-1465. Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe","arxiv_match":false}],"id":"2502.14819","metadata_source":"Cached arXiv API metadata","rank":741},{"title":"Message passing neural pde solvers","arxiv_id":"2202.03376","year":2022,"url":"https://arxiv.org/abs/2202.03376","citing_submissions":140,"explicit_arxiv_matches":43,"examples":[{"id":"XXNQ4STUz3","title":"CEGINO: CONTINUOUS EQUIVARIANT GEOMETRY- INFORMED NEURAL OPERATORS FOR 3D PDES","forum_url":"https://openreview.net/forum?id=XXNQ4STUz3","excerpt":"nsky. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, 13(1):2453, 2022. Johannes Brandstetter, Daniel E. Worrall, and Max Welling. Message passing neural PDE solvers. In International Conference on Learning Representations, 2022. URL https://openreview. net/forum?id=vSix3HPYKSU. Shuhao Cao. Choose a transformer: Fourier or Galerkin. Advances in Neural Information","arxiv_match":false},{"id":"LAt8avZY0Y","title":"Governing Equation Discovery from Data Based on Differential Invariants","forum_url":"https://openreview.net/forum?id=LAt8avZY0Y","excerpt":"ations, volume 154. Springer Science & Business Media, 2008. George W Bluman. Applications of symmetry methods to partial differential equations. Springer, 2010. Johannes Brandstetter, Max Welling, and Daniel E Worrall. Lie point symmetry data augmentation for neural PDE solvers. In International Conference on Machine Learning, pp. 2241–2256. PMLR, 2022a. Johannes Brandstetter, Daniel Worrall, and Max Welling. Message passing neural PDE solvers. arXiv preprint arXiv:2202.03376, 2022b. Steven L Brunton, Joshua L Proctor, and J Nathan Kutz. Discovering governing equations from","arxiv_match":true},{"id":"LwwQcY50WQ","title":"Modelling Slow and Fast Variables for Subseasonal-to-Seasonal Forecasting","forum_url":"https://openreview.net/forum?id=LwwQcY50WQ","excerpt":"arth system. Nature, 641(8065):1180–1187, 2025. Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D Collins, Michael S Pritchard, and Alexander Keller. FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale. arXiv preprint arXiv:2507.12144, 2025. Johannes Brandstetter, Daniel Worrall, and Max Welling. Message passing neural PDE solvers. arXiv preprint arXiv:2202.03376, 2022. Lei Chen, Xiaohui Zhong, Feng Zhang, Yuan Cheng, Yinghui Xu, Yuan Qi, and Hao Li. FuXi: A Ca","arxiv_match":true}],"id":"2202.03376","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":742},{"title":"Minigpt-4: Enhancing vision-language understanding with advanced large language models","arxiv_id":"2304.10592","year":2023,"url":"https://arxiv.org/abs/2304.10592","citing_submissions":140,"explicit_arxiv_matches":61,"examples":[{"id":"aVu1Zo8kCI","title":"Beyond Holistic Representations: Dual Distribution Modeling for Test-Time Adaptation of Vision–Language Models","forum_url":"https://openreview.net/forum?id=aVu1Zo8kCI","excerpt":"y, and Ziwei Liu. Learning to prompt for visionlanguage models. International journal of computer vision, 130(9):2337–2348, 2022b. Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. MiniGPT-4: Enhancing vision-language understanding with advanced large language models. In The Twelfth International Conference on Learning Representations, 2024. A ","arxiv_match":false},{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"arios that are difficult for humans?, 2025d. URL https://arxiv.org/abs/2408.13257. Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. Deepeyes: Incentivizing\" thinking with images\" via reinforcement learning. arXiv preprint arXiv:2505.14362, 2025. Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing visionlanguage understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023. Table 4 reports low cosine similarity between the two selected latent states within each que","arxiv_match":true},{"id":"bp0HEA9om7","title":"Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters","forum_url":"https://openreview.net/forum?id=bp0HEA9om7","excerpt":"r bronze inscription recognition. arXiv preprint arXiv:2510.01651, 2025. Wenhui Zhou, Jinyu Liu, Jiefeng Li, Jiyi Li, Lili Lin, Fumiyo Fukumoto, and Guojun Dai. Styleindependent radical sequence learning for zero-shot recognition of small seal script. Journal of the Franklin Institute, 2023. Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. MiniGPT-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023. Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chi","arxiv_match":true}],"id":"2304.10592","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":743},{"title":"World Simulation with Video Foundation Models for Physical AI","arxiv_id":"2511.00062","year":2025,"url":"https://arxiv.org/abs/2511.00062","citing_submissions":140,"explicit_arxiv_matches":134,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"iamby, Junyi Zhang, Ziteng Ji, Trevor Darrell, and Roei Herzig. Pre-training auto-regressive robotic models with 4d representations. In Fortysecond International Conference on Machine Learning, 2025. URL https://openreview. net/forum?id=2FDsh5D2Th. NVIDIA. Cosmos world foundation model platform for physical ai, 2025a. URL https:// arxiv.org/abs/2501.03575. NVIDIA. World simulation with video foundation models for physical ai, 2025b. URL https: //arxiv.org/abs/2511.00062. Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros. Context enco","arxiv_match":true},{"id":"DcJkY91azC","title":"NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters","forum_url":"https://openreview.net/forum?id=DcJkY91azC","excerpt":"os 3: Omnimodal world models for physical ai. arXiv, 2026. Arslan Ali, Junjie Bai, Maciej Bala, Yogesh Balaji, Aaron Blakeman, Tiffany Cai, Jiaxin Cao, Tianshi Cao, Elizabeth Cha, Yu-Wei Chao, et al. World simulation with video foundation models for physical ai. arXiv, 2025. Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. In ICLR, 2024. Andreas Blattmann, Tim Dockhorn, Sumith K","arxiv_match":false},{"id":"KP1C5Vu5Nr","title":"Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction","forum_url":"https://openreview.net/forum?id=KP1C5Vu5Nr","excerpt":" Arslan Ali, Junjie Bai, Maciej Bala, Yogesh Balaji, Aaron Blakeman, Tiffany Cai, Jiaxin Cao, Tianshi Cao, Elizabeth Cha, Yu-Wei Chao, et al. World simulation with video foundation models for physical ai. arXiv preprint arXiv:2511.00062, 2025. Yutong Bai, Danny Tran, Amir Bar, Yann LeCun, Trevor Darrell, and Jitendra Malik. Whole-body","arxiv_match":true}],"id":"2511.00062","metadata_source":"Cached arXiv API metadata","rank":744},{"title":"An explanation of in-context learning as implicit bayesian inference","arxiv_id":"2111.02080","year":2021,"url":"https://arxiv.org/abs/2111.02080","citing_submissions":139,"explicit_arxiv_matches":48,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":"hoo. Unified Training of Universal Time Series Forecasting Transformers. In International Conference on Machine Learning (ICML), 2024. Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. An Explanation of in-Context Learning as Implicit Bayesian Inference. In International Conference on Learning Representations (ICLR), 2022. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond Efficient Transf","arxiv_match":false},{"id":"lmr3hyZrpJ","title":"ContextDepth: Boosting Metric Depth Estimation via Richer Context","forum_url":"https://openreview.net/forum?id=lmr3hyZrpJ","excerpt":"ght Prompting Elicits Reasoning in Large Language Models. In Conference on Neural Information Processing Systems (NeurIPS), 2022. [36] Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. An Explanation of In-context Learning as Implicit Bayesian Inference. In International Conference on Learning Representations (ICLR), 2022. 13 [37] Zhen Xu, Hongyu Zhou, Sida Peng, Haotong Lin, Haoyu Guo, Jiahao Shao, Peishan Yang, Qinglin Yang, Sheng Miao, Xingyi He,","arxiv_match":false},{"id":"G4yS4Va5AZ","title":"Sequential Stability Bounds for Order-Dependent In-Context Learning","forum_url":"https://openreview.net/forum?id=G4yS4Va5AZ","excerpt":", Yoav Levine, and Amnon Shashua. The learnability of in-context learning. In Advances in Neural Information Processing Systems, 2023. Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. An explanation of in-context learning as implicit Bayesian inference. In International Conference on Learning Representations, 2022. Ruiqi Zhang, Spencer Frei, and Peter L Bartlett. Trained transformers learn linear models in-context. Journal of Machine Learning Resea","arxiv_match":false}],"id":"2111.02080","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":745},{"title":"Gemma Scope: Open sparse autoencoders everywhere all at once on Gemma 2","arxiv_id":"2408.05147","year":2024,"url":"https://arxiv.org/abs/2408.05147","citing_submissions":139,"explicit_arxiv_matches":59,"examples":[{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"-faithfulness-in-chain-of-thought-reasoning. Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, Janos Kramar, Anca Dragan, Rohin Shah, and Neel Nanda. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen (eds.), Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP","arxiv_match":false},{"id":"89cECgVi4S","title":"RealControlBench: A Comprehensive Benchmark for Fine-Grained LLM and LLM Agent steer in the Wild","forum_url":"https://openreview.net/forum?id=89cECgVi4S","excerpt":"https://openreview.net/ forum?id=aLLuYpn83y. Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. In Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 278–300, 2024. Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Te","arxiv_match":false},{"id":"eqmbSYhaLy","title":"Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility","forum_url":"https://openreview.net/forum?id=eqmbSYhaLy","excerpt":" in language model interpretability. In International Conference on Machine Learning (ICML), 2025. Harold W. Kuhn. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1–2):83–97, 1955. Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. Gemma Scope: Open sparse autoencoders everywhere all at once on Gemma 2. arXiv preprint arXiv:2408.05147, 2024. Michael Lan, Philip Torr, Austin Meek, Ashkan Khakzar, David Krueger, and Fazl Barez. Quanti","arxiv_match":true}],"id":"2408.05147","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":746},{"title":"Measuring coding challenge competence with APPS","arxiv_id":"2105.09938","year":2021,"url":"https://arxiv.org/abs/2105.09938","citing_submissions":139,"explicit_arxiv_matches":57,"examples":[{"id":"hoti3ZXTRG","title":"PuzzleCodeBench: A Visual-to-Code Benchmark for Executable Puzzle Solving","forum_url":"https://openreview.net/forum?id=hoti3ZXTRG","excerpt":".0. https://deepmind.google/models/gemini, 2026. Alex Gu, Baptiste Rozière, Hugh Leather, Armando Solar-Lezama, Gabriel Synnaeve, and Sida I Wang. Cruxeval: A benchmark for code reasoning, understanding and execution. arXiv preprint arXiv:2401.03065, 2024. Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al. Measuring coding challenge competence with apps. arXiv preprint arXiv:2105.09938, 2021. Wenyi Hong, Xiaotao Gu, Ziyang Pan, Zhen Yang, Yuting Wang, Yue Wang, Yuanchang Yue, Yu Wang","arxiv_match":true},{"id":"sFUbqvhx3m","title":"SyntaxBPE: Learning Which Code Boundaries to Cross","forum_url":"https://openreview.net/forum?id=sFUbqvhx3m","excerpt":"rence on Learning Representations, 2021. Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. Measuring coding challenge competence with APPS. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1, 2021. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armand","arxiv_match":false},{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":"ion for Computational Linguistics, 2026. Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. Measuring coding challenge competence with APPS. In Advances in Neural Information Processing Systems Datasets and Benchmarks Track, 2021. Jaeeun Jang, Hansle Lee, and Sangmin Kim. A few bad apples spoil the bunch: Preventing global entropy collap","arxiv_match":false}],"id":"2105.09938","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":747},{"title":"Git re-basin: Merging models modulo permutation symmetries","arxiv_id":"2209.04836","year":2022,"url":"https://arxiv.org/abs/2209.04836","citing_submissions":138,"explicit_arxiv_matches":41,"examples":[{"id":"JBSbtgytUW","title":"SuperAI: Merging Capabilities Across Heterogeneous Foundation Models","forum_url":"https://openreview.net/forum?id=JBSbtgytUW","excerpt":"omputational Linguistics. doi: 10.18653/v1/2025.sdp-1.5. URL https://aclanthology.org/ 2025.sdp-1.5/. Akshit Achara, Tatiana Gaintseva, Mateo Mahaut, Pritish Chakraborty, Viktor Stenby Johansson, Melih Barsbey, Emanuele Rodolà, and Donato Crisostomi. Multi-way representation alignment. arXiv preprint arXiv:2602.06205, 2026. Samuel K Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa. Git re-basin: Merging models modulo permutation symmetries. arXiv preprint arXiv:2209.04836, 2022. Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, and David Ha. Evolutionary optimization of m","arxiv_match":true},{"id":"OFVWcaGfdi","title":"The Delta Is Not the Skill: Zero-Prior Elicitation Transfers LoRA Behavior Across Model Families","forum_url":"https://openreview.net/forum?id=OFVWcaGfdi","excerpt":" Samuel K Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa. Git re-basin: Merging models modulo permutation symmetries. arXiv preprint arXiv:2209.04836, 2022. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, E","arxiv_match":true},{"id":"gRg6FaJTPM","title":"When Preservation Misleads: Scale-Dependent Audits of Frozen Video-Language Interfaces","forum_url":"https://openreview.net/forum?id=gRg6FaJTPM","excerpt":" Samuel K. Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa. Git re-basin: Merging models modulo permutation symmetries. In International Conference on Learning Representations, 2023. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, Wenbin Ge","arxiv_match":false}],"id":"2209.04836","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":748},{"title":"Verbalizable Representations Form a Global Workspace in Language Models","arxiv_id":"2607.15495","year":2026,"url":"https://arxiv.org/abs/2607.15495","citing_submissions":138,"explicit_arxiv_matches":58,"examples":[{"id":"W1xeiKanFu","title":"Do Correct Answers Have Stable Latent Knowledge Support?","forum_url":"https://openreview.net/forum?id=W1xeiKanFu","excerpt":"Gu, Oyvind Tafjord, Bailey Kuehl, Dany Haddad, Jesse Dodge, and Hannaneh Hajishirzi. Olmes: A standard for language model evaluations. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 5020–5048, 2025. Wes Gurnee, Nicholas Sofroniew, Adam Pearce, Mateusz Piotrowski, Isaac Kauvar, Runjin Chen, Anna Soligo, Paul Bogdan, Euan Ong, Rowan Wang, et al. Verbalizable representations form a global workspace in language models. arXiv preprint arXiv:2607.15495, 2026. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob S","arxiv_match":true},{"id":"fNrQjgb4Ch","title":"DV-Lens: Revealing the Functional Organization of Language Model Parameters","forum_url":"https://openreview.net/forum?id=fNrQjgb4Ch","excerpt":" Mateusz Piotrowski, Isaac Kauvar, Runjin Chen, Anna Soligo, Paul Bogdan, Euan Ong, Rowan Wang, T. Ben Thompson, David Abrahams, Subhash Kantamneni, Emmanuel Ameisen, Joshua Batson, and Jack Lindsey. Verbalizable representations form a global workspace in language models. Transformer Circuits Thread, 2026. URL https: //transformer-circuits.pub/2026/workspace/index.html. Published July 6, 2026; accessed September 10, 2026. Dan Hendrycks, Collin Burns, Saurav Kadavath,","arxiv_match":false},{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":" metrics don’t measure faithfulness: A meta-evaluation with ground truth, 2026. URL https://arxiv.org/abs/2605.25052. Wes Gurnee, Nicholas Sofroniew, Adam Pearce, Mateusz Piotrowski, Isaac Kauvar, Runjin Chen, Anna Soligo, Paul Bogdan, Euan Ong, Rowan Wang, Ben Thompson, David Abrahams, Subhash Kantamneni, Emmanuel Ameisen, Joshua Batson, and Jack Lindsey. Verbalizable representations form a global workspace in language models, 2026. URL https://arxiv.org/abs/ 2607.15495. Zhengfu He, Wentao Shu, Xuyang Ge, Lingjie Chen, Junxuan Wang, Yunhua Zhou, Frances Liu, Qipeng Gu","arxiv_match":true}],"id":"2607.15495","metadata_source":"Cached arXiv API metadata","rank":749},{"title":"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution","arxiv_id":"2202.10054","year":2022,"url":"https://arxiv.org/abs/2202.10054","citing_submissions":137,"explicit_arxiv_matches":34,"examples":[{"id":"lMGk9GrpHK","title":"Enabling Validation for Robust Few-Shot Recognition","forum_url":"https://openreview.net/forum?id=lMGk9GrpHK","excerpt":"engan: Open-set recognition via open data generation. In IEEE/CVF International Conference on Computer Vision, 2021. Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang. Finetuning can distort pretrained features and underperform out-of-distribution. In International Conference on Learning Representations, 2022. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Y","arxiv_match":false},{"id":"YedwDyhTVC","title":"Why Ghost Outputs Teach: A Kernel-Based Understanding of Subliminal Learning","forum_url":"https://openreview.net/forum?id=YedwDyhTVC","excerpt":"ti-step setting: Evidence from mnist auxiliary logit distillation experiment. arXiv preprint arXiv:2604.25779, 2026. Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang. Finetuning can distort pretrained features and underperform out-of-distribution. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum? id=UYneFzXSJWh. Zhe Li, Bicheng Ying, Zidong Liu, and Haibo Yang. Learning dynamics of zeroth-order o","arxiv_match":false},{"id":"veTQX3F4Hg","title":"Hidden in the Features: Semantic Backdoor Watermarking for Large Vision-Language Models","forum_url":"https://openreview.net/forum?id=veTQX3F4Hg","excerpt":"R_2022_paper.html. Gemini Team. Gemini: A family of highly capable multimodal models. arXiv:2312.11805, 2023. arXiv preprint Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. Fine-tuning can distort pretrained features and underperform out-of-distribution. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id= UYneFzXSJWh. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language","arxiv_match":false}],"id":"2202.10054","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":750},{"title":"R-Zero: Self-Evolving Reasoning LLM from Zero Data","arxiv_id":"2508.05004","year":2025,"url":"https://arxiv.org/abs/2508.05004","citing_submissions":137,"explicit_arxiv_matches":55,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"r large language models. In International Conference on Machine Learning, 2025. Chengsong Huang, Wenhao Yu, Xiaoyang Wang, Hongming Zhang, Zongxia Li, Ruosen Li, Jiaxin Huang, Haitao Mi, and Dong Yu. R-zero: Self-evolving reasoning llm from zero data. In International Conference on Learning Representations, volume 2026, pp. 130770–130790, 2026. Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. Large language m","arxiv_match":false},{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"aptation of large language models, 2021. URL https: //arxiv.org/abs/2106.09685. Chengsong Huang, Wenhao Yu, Xiaoyang Wang, Hongming Zhang, Zongxia Li, Ruosen Li, Jiaxin Huang, Haitao Mi, and Dong Yu. R-zero: Self-evolving reasoning llm from zero data. In International Conference on Learning Representations, volume 2026, pp. 130770–130790, 2026. Huipeng Huang, Wenbo Liao, Huajun Xi, Hao Zeng, Mengchen Zhao, and Hongxin Wei. Modelagnostic selective","arxiv_match":false},{"id":"07pvXM8POP","title":"Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills","forum_url":"https://openreview.net/forum?id=07pvXM8POP","excerpt":"lms in execution feedback with reinforcement learning. arXiv preprint arXiv:2410.02089, 2024. Zefeng He, Siyuan Huang, Xiaoye Qu, Yafu Li, Tong Zhu, Yu Cheng, and Yang Yang. Gems: Agent-native multimodal generation with memory and skills. arXiv preprint arXiv:2603.28088, 2026. Chengsong Huang, Wenhao Yu, Xiaoyang Wang, Hongming Zhang, Zongxia Li, Ruosen Li, Jiaxin Huang, Haitao Mi, and Dong Yu. R-zero: Self-evolving reasoning llm from zero data. arXiv preprint arXiv:2508.05004, 2025a. Siyuan Huang, Xiaoye Qu, Yafu Li, Yun Luo, Zefeng He, Daizong Liu, and Yu Cheng. Spotlight ","arxiv_match":true}],"id":"2508.05004","metadata_source":"Cached arXiv API metadata","rank":751},{"title":"Search-o1: Agentic Search-Enhanced Large Reasoning Models","arxiv_id":"2501.05366","year":2025,"url":"https://arxiv.org/abs/2501.05366","citing_submissions":137,"explicit_arxiv_matches":34,"examples":[{"id":"BOyTp7MiOV","title":"CAFE: Self-Improving Search Agents Need Co-Evolving Feedback","forum_url":"https://openreview.net/forum?id=BOyTp7MiOV","excerpt":"3871, 2026a. doi: 10.48550/ARXIV. 2605.03871. URL https://doi.org/10.48550/arXiv.2605.03871. Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-o1: Agentic search-enhanced large reasoning models. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, EMNLP 2025, Suzhou, ","arxiv_match":false},{"id":"ZSRMEDG6G8","title":"Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=ZSRMEDG6G8","excerpt":"Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems 33, pp. 9459–9474, 2020. Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-o1: Agentic search-enhanced large reasoning models. arXiv preprint arXiv:2501.05366, 2025a. Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, S","arxiv_match":true},{"id":"r9NBqCe7RJ","title":"Don’t Waste Your Search-and-Visit Signals in Deep Research","forum_url":"https://openreview.net/forum?id=r9NBqCe7RJ","excerpt":"deep research agents via rubrics from expert report. arXiv preprint arXiv:2601.08536, 2026a. Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-o1: Agentic search-enhanced large reasoning models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 5420–5438, 2025. Zijian Li, Xin Guan, Bo Zhang, Shen Huang, Houquan Zhou, Shaopeng Lai, Ming Yan, Yong Jiang","arxiv_match":false}],"id":"2501.05366","metadata_source":"Cached arXiv API metadata","rank":752},{"title":"Humanity's Last Exam","arxiv_id":"2501.14249","year":2025,"url":"https://arxiv.org/abs/2501.14249","citing_submissions":136,"explicit_arxiv_matches":136,"examples":[{"id":"iksnFm6vFW","title":"Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage","forum_url":"https://openreview.net/forum?id=iksnFm6vFW","excerpt":" Simone Caldarella, Davide Talon, Rahaf Aljundi, Elisa Ricci, and Massimiliano Mancini. Thinking past the answer: Evaluating harmful overthinking in large reasoning models. arXiv preprint arXiv:2606.02835, 2026. Center for AI Safety, Scale AI, and HLE Contributors Consortium. A benchmark of expert-level academic questions to assess AI capabilities. Nature, 649:1139–1146, 2026. doi: 10.1038/s41586 -025-09962-4. URL https://arxiv.org/abs/2501.14249. Benteng Chen, Weida Wang, Shufei Zhang, Mingbao Lin, and Min Zhang. Step-grpo: Internalizing dynam","arxiv_match":true},{"id":"8zRnR4jkT9","title":"HealthCoreBench: Measuring Evaluation Artifacts for Reliable Medical LLM Evaluation","forum_url":"https://openreview.net/forum?id=8zRnR4jkT9","excerpt":"ucinations in large language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2025. URL https: //aclanthology.org/2025.emnlp-main.143/. Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Sean Shi, Michael Choi, Anish An, et al. Humanity’s last exam. arXiv preprint arXiv:2501.14249, 2025. doi: 10.48550/arXiv.2501.14249. URL https://arxiv.org/abs/2501.14249. Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin. tiny","arxiv_match":true},{"id":"GGUmZybpbd","title":"AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?","forum_url":"https://openreview.net/forum?id=GGUmZybpbd","excerpt":"asoning memory. In Proc. ICLR, 2026b. Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E. Gonzalez. The berkeley function calling leaderboard (BFCL): From tool use to agentic evaluation of large language models. In Proc. ICML, 2025. Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, et al. Humanity’s last exam. arXiv preprint arXiv:2501.14249, 2025. Shuai Shao, Qihan Ren, Chen Qian, Boyi Wei, Dadi Guo, Jingyi Yang, Xinhao Song, Linfeng Zhan","arxiv_match":true}],"id":"2501.14249","metadata_source":"Cached arXiv API metadata","rank":753},{"title":"Self-Harness: Harnesses That Improve Themselves","arxiv_id":"2606.09498","year":2026,"url":"https://arxiv.org/abs/2606.09498","citing_submissions":136,"explicit_arxiv_matches":104,"examples":[{"id":"aoenJrugDU","title":"Ecdysis: Rethinking Failure-Driven Evolution of LLM Agent Harnesses","forum_url":"https://openreview.net/forum?id=aoenJrugDU","excerpt":"8-89176251-0. doi: 10.18653/v1/2025.acl-long.853. URL https://aclanthology.org/2025. acl-long.853/. Hangfan Zhang, Shao Zhang, Kangcong Li, Chen Zhang, Yang Chen, Yiqun Zhang, Lei Bai, and Shuyue Hu. Self-harness: Harnesses that improve themselves, 2026a. URL https://arxiv. org/abs/2606.09498. Jenny Zhang, Shengran Hu, Cong Lu, Robert Tjarko Lange, and Jeff Clune. Darwin gödel machine: Open-ended evolution of self-improving agents. In The Four","arxiv_match":false},{"id":"1YacpejHL6","title":"miniFlux: Exploring Agent-Directed Harness Evolution for Online Adaptation","forum_url":"https://openreview.net/forum?id=1YacpejHL6","excerpt":"trin, et al. Learning to discover at test time. arXiv preprint arXiv:2601.16175, 2026. Aohan Zeng, Xin Lv, Zhenyu Hou, Zhengxiao Du, Qinkai Zheng, Bin Chen, Da Yin, Chendi Ge, Chenghua Huang, Chengxing Xie, et al. Glm-5: from vibe coding to agentic engineering. arXiv preprint arXiv:2602.15763, 2026. Hangfan Zhang, Shao Zhang, Kangcong Li, Chen Zhang, Yang Chen, Yiqun Zhang, Lei Bai, and Shuyue Hu. Self-harness: Harnesses that improve themselves. arXiv preprint arXiv:2606.09498, 2026a. Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, and Jeff Clune. Darwin gödel machine: open","arxiv_match":true},{"id":"bgSGfY1hQE","title":"Agentic DAgger: Self-Improving Interactive Imitation Learning via Agentic Harness","forum_url":"https://openreview.net/forum?id=bgSGfY1hQE","excerpt":"ia Xie, Tonghe Zhang, Haotian Lin, Letian Fu, Haoru Xue, Jalen Lu, Yi Yang, Cunxi Dai, Zi Wang, Jimmy Wu, Guanzhi Wang, S. Shankar Sastry, Ken Goldberg, Linxi Fan, Yuke Zhu, and Guanya Shi. ENPIRE: Agentic robot policy self-improvement in the real world. In Conference on Robot Learning (CoRL), 2026. Hangfan Zhang, Shao Zhang, Kangcong Li, Chen Zhang, Yang Chen, Yiqun Zhang, Lei Bai, and Shuyue Hu. Self-Harness: Harnesses that improve themselves. arXiv preprint arXiv:2606.09498, 2026a. Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, and Jeff Clune. Darwin Gödel Machine: Open","arxiv_match":true}],"id":"2606.09498","metadata_source":"Cached arXiv API metadata","rank":754},{"title":"Evoprompt: Connecting llms with evolutionary algorithms yields powerful prompt optimizers","arxiv_id":"2309.08532","year":2023,"url":"https://arxiv.org/abs/2309.08532","citing_submissions":135,"explicit_arxiv_matches":36,"examples":[{"id":"wbQy4kEPym","title":"CoEvolve: Traceable Reflective Memory Evolution for Prompt Optimization","forum_url":"https://openreview.net/forum?id=wbQy4kEPym","excerpt":"lf-correct with tool-interactive critiquing. In The Twelfth International Conference on Learning Representations, 2024. URL https://arxiv.org/abs/2305. 11738. Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. In The Twelfth International Conference on Learning Representations, 2024. URL https://arxiv.org/abs/2309.08532. Priyanshu Gupta, Shashank Kirtania, Ananya Singha, Sumit Gulwani, Arjun Radhakrishna, Gustavo Soar","arxiv_match":true},{"id":"sK5qhF83pP","title":"EvoSCM: Scientific Belief Revision Through Causal Model Evolution and Experimentation","forum_url":"https://openreview.net/forum?id=sK5qhF83pP","excerpt":"oinspired multi-agent intelligent graph reasoning. Advanced Materials, 37(22):2413523, 2025. Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. In International Conference on Learning Representations, 2024. Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan ","arxiv_match":false},{"id":"CkuA2Se9vX","title":"MODEPrompt: Taxonomy-Guided Pareto Evolution for Prompt Optimization","forum_url":"https://openreview.net/forum?id=CkuA2Se9vX","excerpt":" Forty-first International Conference on Machine Learning, ICML 2024, pp. 13481–13544, 2024. Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. In The Twelfth International Conference on Learning Representations, ICLR, 2024. Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeat","arxiv_match":false}],"id":"2309.08532","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":755},{"title":"Intriguing properties of neural networks","arxiv_id":"1312.6199","year":2013,"url":"https://arxiv.org/abs/1312.6199","citing_submissions":134,"explicit_arxiv_matches":79,"examples":[{"id":"ZLwbkenbR8","title":"Belief-Based Risk Reduction for Robust Reinforcement Learning under Adversarial Observation Perturbations","forum_url":"https://openreview.net/forum?id=ZLwbkenbR8","excerpt":"//openreview.net/forum?id=7gDENzTzw1. Yanchao Sun, Ruijie Zheng, Yongyuan Liang, and Furong Huang. Who is the strongest enemy? towards optimal and efficient evasion attacks in deep RL. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=JM2kFbJvvI. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013. Yiping Wang, Qing Yang, Zhiyuan Zeng, Liliang Ren, Liyuan Liu, Baolin Peng, Hao Cheng, Xueha","arxiv_match":true},{"id":"U0Jc1LoPZM","title":"Adversarial Attacks on World Action Models via Manipulating Imagination--Action Causal Consistency","forum_url":"https://openreview.net/forum?id=U0Jc1LoPZM","excerpt":"hen, Mingyue Cui, and Xingyi Yang. BadWorld: Adversarial attacks on world models, 2026. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. In International Conference on Learning Representations, 2014. Yang Tian et al. Predictive inverse dynamics models are scalable learners for robotic manipulation, 2024. David Wang et al. Adversarial","arxiv_match":false},{"id":"6ak6A7Jakd","title":"Certified Diffusion Smoothing for Robust Reinforcement Learning","forum_url":"https://openreview.net/forum?id=6ak6A7Jakd","excerpt":"ence, volume 34, pp. 5883–5891, 2020. Yanchao Sun, Ruijie Zheng, Yongyuan Liang, and Furong Huang. Who is the strongest enemy? towards optimal and efficient evasion attacks in deep RL. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=JM2kFbJvvI. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013. Václav Voráček. Treatment of statistical estimation problems in randomized smoothing for ","arxiv_match":true}],"id":"1312.6199","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":756},{"title":"Memp: Exploring Agent Procedural Memory","arxiv_id":"2508.06433","year":2025,"url":"https://arxiv.org/abs/2508.06433","citing_submissions":134,"explicit_arxiv_matches":44,"examples":[{"id":"Up2L3BO62X","title":"CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection","forum_url":"https://openreview.net/forum?id=Up2L3BO62X","excerpt":"Xiv preprint arXiv:2504.19413, 2025. Guanting Dong, Yifei Chen, Xiaoxi Li, Jiajie Jin, Hongjin Qian, Yutao Zhu, Hangyu Mao, Guorui Zhou, Zhicheng Dou, and Ji-Rong Wen. Tool-star: Empowering llm-brained multi-tool reasoner via reinforcement learning. arXiv preprint arXiv:2505.16410, 2025. Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. Memp: Exploring agent procedural memory. arXiv preprint arXiv:2508.06433, 2025. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Gr","arxiv_match":true},{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"r observation-centric interaction. arXiv preprint arXiv:2510.24168, 2025. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36:28091–28114, 2023. Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. Memp: Exploring agent procedural memory. arXiv preprint arXiv:2508.06433, 2025. Changlong Gao, Zhangxuan Gu, Yulin Liu, Xinyu Qiu, Shuheng Shen, Yue Wen, Tianyu Xia, Zhenyu","arxiv_match":true},{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":"i agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. Memp: Exploring agent procedural memory. In Findings of the Association for Computational Linguistics: ACL 2026, pp. 17490–17502, 2026. Tao Feng, Chongrui Ye, Tianyang Luo, Jingjun Xu, Xueqiang Xu, Haozhen Zhang, Ge Liu, and Jiaxuan You. E","arxiv_match":false}],"id":"2508.06433","metadata_source":"Cached arXiv API metadata","rank":757},{"title":"Retentive network: A successor to transformer for large language models","arxiv_id":"2307.08621","year":2023,"url":"https://arxiv.org/abs/2307.08621","citing_submissions":134,"explicit_arxiv_matches":123,"examples":[{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"nal memory. In Proceedings of the 40th International Conference on Machine Learning, ICML’23, 2023. Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models, 2023. Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, and Song Han. Quest: Query-aware sparsity for efficient long-context llm inference. arXiv preprint arXiv:2406.10774, 2024. Weizhi W","arxiv_match":false},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"enghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, Tatsunori Hashimoto, and Carlos Guestrin. Learning to (learn at test time): RNNs with expressive hidden states. In Proceedings of the 42nd International Conference on Machine Learning, pp. 57503–57522, 2025. Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models. arXiv preprint arXiv:2307.08621, 2023. Sho Takase and Shun Kiyono. Lessons on parameter sharing across layers in transformers. In P","arxiv_match":true},{"id":"b47jrUdTcU","title":"StacKV: Spatio-Temporally Aware Compression of KV Caches in Hybrid Language Models","forum_url":"https://openreview.net/forum?id=b47jrUdTcU","excerpt":"xt language modeling, 2025. URL https: //arxiv.org/abs/2406.07522. Jiwon Song, Dongwon Jo, Yulhwa Kim, and Jae-Joon Kim. Reasoning path compression: Compressing generation trajectories for efficient llm reasoning, 2025. URL https://arxiv.org/ abs/2505.13866. Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models, 2023. URL https://arxiv.org/abs/2307.08621. Hanlin Tang, Yang Lin, Jing Lin, Qingsen Han, Shikuan Hong, Yiwu Yao, and Gongyi Wang. Razorattent","arxiv_match":true}],"id":"2307.08621","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":758},{"title":"UltraFeedback: Boosting language models with scaled AI feedback","arxiv_id":"2310.01377","year":2023,"url":"https://arxiv.org/abs/2310.01377","citing_submissions":134,"explicit_arxiv_matches":64,"examples":[{"id":"8298ipdPC6","title":"Reward Shaping to Mitigate Reward Hacking in RLHF","forum_url":"https://openreview.net/forum?id=8298ipdPC6","excerpt":"mlr.press/v235/chen24bn.html. Thomas Coste, Usman Anwar, Robert Kirk, and David Krueger. Reward model ensembles help mitigate overoptimization. In International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=dcjtMYkpXx. Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. UltraFeedback: Boosting language models with high-quality feedback, 2023. URL https://arxiv.org/abs/2310.01377. Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yan","arxiv_match":true},{"id":"fNWmDATSTU","title":"LLMs Get Lost in Personal Intelligence: Personalization Overreach in Memory-Augmented Models","forum_url":"https://openreview.net/forum?id=fNWmDATSTU","excerpt":"nce, pp. 1–10, 2026. doi: 10.1109/TAI.2026.3689823. Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, Zhiyuan Liu, and Maosong Sun. ULTRAFEEDBACK: Boosting language models with scaled AI feedback. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Proceedings of the 41st International Conference on Machine Learn","arxiv_match":false},{"id":"zSLAIavoJp","title":"Reward Differences Matter for Robust LLM Alignment under Noisy Preferences","forum_url":"https://openreview.net/forum?id=zSLAIavoJp","excerpt":"ces in neural information processing systems, 2017. Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, Zhiyuan Liu, and Maosong Sun. UltraFeedback: Boosting language models with scaled AI feedback. In Proceedings of the 41st International Conference on Machine Learning, 2024. John C. Duchi and Hongseok Namkoong. Learning models with uniform performance via distributionally robust optimization.","arxiv_match":false}],"id":"2310.01377","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":759},{"title":"WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models","arxiv_id":"2401.13919","year":2024,"url":"https://arxiv.org/abs/2401.13919","citing_submissions":134,"explicit_arxiv_matches":31,"examples":[{"id":"GGUmZybpbd","title":"AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?","forum_url":"https://openreview.net/forum?id=GGUmZybpbd","excerpt":"Evotest: Evolutionary test-time learning for self-improving agentic systems. In Proc. ICLR, 2026. Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu. Webvoyager: Building an end-to-end web agent with large multimodal models. In Proc. ACL, 2024. Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can language models resolve real-world github issues? In Proc","arxiv_match":false},{"id":"ZSRMEDG6G8","title":"Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=ZSRMEDG6G8","excerpt":" tool embeddings. In Advances in Neural Information Processing Systems 36, New Orleans, LA, 2023. Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu. Webvoyager: Building an end-to-end web agent with large multimodal models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, pp. 6864–6890, Bangkok, Thailand, 2024. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. C","arxiv_match":false},{"id":"jbl4qCAXc4","title":"Agents Recognize Source Inconsistency but Rarely Act on It: Decision-Time Mediation for Grounded Resolution","forum_url":"https://openreview.net/forum?id=jbl4qCAXc4","excerpt":"ed multi-agents: A survey of progress and challenges, 2024. URL https://arxiv.org/abs/2402.01680. Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu. WebVoyager: Building an end-to-end web agent with large multimodal models. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6864–6890, Bangkok, Thai","arxiv_match":false}],"id":"2401.13919","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":760},{"title":"Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data","arxiv_id":"2406.03736","year":2024,"url":"https://arxiv.org/abs/2406.03736","citing_submissions":134,"explicit_arxiv_matches":33,"examples":[{"id":"xs3B5JItLk","title":"PACE: Prune Aggressively and Commit Early for Diffusion VLM Inference Acceleration","forum_url":"https://openreview.net/forum?id=xs3B5JItLk","excerpt":"i. Large language diffusion models. Advances in Neural Information Processing Systems, 38:50608–50646, 2026. Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, and Chongxuan Li. Your absorbing discrete diffusion secretly models the conditional distributions of clean data. In International Conference on Learning Representations, volume 2025, pp. 64972–65009, 2025. Subham S Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander ","arxiv_match":false},{"id":"Q4SXwLfo0U","title":"PulseCol: Periodically Refreshed Column-Sparse Attention for Accelerating Diffusion Language Models","forum_url":"https://openreview.net/forum?id=Q4SXwLfo0U","excerpt":"e for diffusion language models. arXiv preprint arXiv:2505.15781, 2025. Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, JiRong Wen, and Chongxuan Li. Large language diffusion models. arXiv preprint arXiv:2502.09992, 2025. Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, and Chongxuan Li. Your absorbing discrete diffusion secretly models the conditional distributions of clean data. arXiv preprint arXiv:2406.03736, 2024. Maximo Eduardo Rulli, Simone Petruzzi, Edoardo Michielon, Fabrizio Silvestri, Simone Scardap","arxiv_match":true},{"id":"IhXXms16ty","title":"A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models","forum_url":"https://openreview.net/forum?id=IhXXms16ty","excerpt":"usion-state policy optimization for masked diffusion language models. arXiv preprint arXiv:2602.06462, 2026. Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, and Chongxuan Li. Your absorbing discrete diffusion secretly models the conditional distributions of clean data. In International Conference on Learning Representations, volume 2025, pp. 64972–65009, 2025. Jingyang Ou, Jiaqi Han, Minkai Xu, Shaoxuan Xu, Jianwen Xie, Stefano Ermon, Yi Wu, and Chongxuan Li. Prin","arxiv_match":false}],"id":"2406.03736","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":761},{"title":"A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence","arxiv_id":"2507.21046","year":2025,"url":"https://arxiv.org/abs/2507.21046","citing_submissions":133,"explicit_arxiv_matches":70,"examples":[{"id":"y5X5ZaufZQ","title":"Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections","forum_url":"https://openreview.net/forum?id=y5X5ZaufZQ","excerpt":"eural Information Processing Systems, 2026. URL https://openreview. net/forum?id=QINnsnppv8. Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web, 2023. Huan-ang Gao, Jiayi Geng, Wenyue Hua, Mengkang Hu, Xinzhe Juan, Hongzhang Liu, Shilong Liu, Jiahao Qiu, Xuan Qi, Yiran Wu, et al. A survey of self-evolving agents: On path to artificial super intelligence. arXiv preprint arXiv:2507.21046, 1, 2025. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mari","arxiv_match":true},{"id":"PjUHgdvzhE","title":"A Recursive Self-Improving Harness for Training-Free Generalist Navigation","forum_url":"https://openreview.net/forum?id=PjUHgdvzhE","excerpt":"ng, Han Xiao, Yuhang Zhou, Shaokun Zhang, Jiayi Zhang, Jinyu Xiang, Yixiong Fang, Qiwen Zhao, Dongrui Liu, Cheng Qian, Zhenhailong Wang, Minda Hu, Huazheng Wang, Qingyun Wu, Heng Ji, and Mengdi Wang. A survey of self-evolving agents: What, when, how, and where to evolve on the path to artificial super intelligence. Transactions on Machine Learning Research, 2026b. Peng Gao, Peng Wang, Feng Gao, Fei Wang, and Ruyue Yuan. Vision-language navigation with embodied intelligence: A survey. TMLR, 2024. Zeying Gong, R","arxiv_match":false},{"id":"w88HAucqab","title":"Evolving Procedural Memory from User Traffic for Agentic Graphic Design","forum_url":"https://openreview.net/forum?id=w88HAucqab","excerpt":"ng, Han Xiao, Yuhang Zhou, Shaokun Zhang, Jiayi Zhang, Jinyu Xiang, Yixiong Fang, Qiwen Zhao, Dongrui Liu, Cheng Qian, Zhenhailong Wang, Minda Hu, Huazheng Wang, Qingyun Wu, Heng Ji, and Mengdi Wang. A survey of self-evolving agents: What, when, how, and where to evolve on the path to artificial super intelligence. Transactions on Machine Learning Research, 2026. ISSN 2835-8856. URL https://openreview.net/forum?id=CTr3bovS5F. Anthropic. Claude 4 system card, May 2025. URL https://www-cdn. anthropic.com/4263b94","arxiv_match":false}],"id":"2507.21046","metadata_source":"Cached arXiv API metadata","rank":762},{"title":"Lmms-eval: Reality check on the evaluation of large multimodal models","arxiv_id":"2407.12772","year":2024,"url":"https://arxiv.org/abs/2407.12772","citing_submissions":133,"explicit_arxiv_matches":36,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"lm agents. arXiv preprint arXiv:2608.27260, 2026. Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, and Ziwei Liu. LMMs-eval: Reality check on the evaluation of large multimodal models. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 881–916, April 2025a. Qingyang Zhang, Haitao Wu, Changqing Zhang, Peilin Zhao, and Yatao Bian. Right question is already","arxiv_match":false},{"id":"ZQOtaCagPN","title":"EviQ: Hierarchical Query-Conditioned Evidence Selection for Efficient VideoLLMs","forum_url":"https://openreview.net/forum?id=ZQOtaCagPN","excerpt":"ral Information Processing Systems, 37:28828–28857, 2024. Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, et al. Lmms-eval: Reality check on the evaluation of large multimodal models. In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 881–916, 2025a. Qizhe Zhang, Mengzhen Liu, Lichen Li, Ming Lu, Yuan Zhang, Junwen Pan, Qi She, and Shanghang Zhang. Beyo","arxiv_match":false},{"id":"YoY3Gk7Syu","title":"Region-Level Policy Optimization for Fine-grained MLLM Perception","forum_url":"https://openreview.net/forum?id=YoY3Gk7Syu","excerpt":"t arXiv:2605.18740, 2026. Jiarui Zhang, Mahyar Khayatkhoei, Prateek Chhikara, and Filip Ilievski. Mllms know where to look: Training-free perception of small visual details with multimodal llms. In ICLR, 2025a. Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, and Ziwei Liu. Lmms-eval: Reality check on the evaluation of large multimodal models, 2024. URL https://arxiv.org/abs/2407. 12772. Yi-Fan Zhang, Xingyu Lu, Shukang Yin, Chaoyou Fu, Wei Chen, Xiao Hu, Bin Wen, Kaiyu Jiang, Changyi","arxiv_match":true}],"id":"2407.12772","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":763},{"title":"PixArt-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis","arxiv_id":"2310.00426","year":2023,"url":"https://arxiv.org/abs/2310.00426","citing_submissions":133,"explicit_arxiv_matches":32,"examples":[{"id":"41Axobxx7a","title":"Scaling Muon for Diffusion Transformers","forum_url":"https://openreview.net/forum?id=41Axobxx7a","excerpt":"nked momentum orthogonalization. arXiv preprint arXiv:2608.02502, 2026a. Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. In International conference on learning representations, volume 2024, pp. 57611–57640, 2024. Vincent Chen, Starrick Liu, Regis Cheng, Dance Yang, Shalfun Li, Ryan Yu, Lucy Liang, Hang Su, Roy Gan, H","arxiv_match":false},{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"s Gokhale, Ludwig Schmidt, Hannaneh Hajishirzi, Vasudev Lal, Chitta Baral, et al. Getting it right: Improving spatial consistency in text-to-image models. In European Conference on Computer Vision, pp. 204–222. Springer, 2024. Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep r","arxiv_match":true},{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"ith latent consistency models. arXiv preprint arXiv:2401.05252, 2024c. Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Zhongdao Wang, James T Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. In ICLR, 2024d. Junsong Chen, Shuchen Xue, Yuyang Zhao, Jincheng Yu, Sayak Paul, Junyu Chen, Han Cai, Enze Xie, and Song Han. Sana-sprint: One-step diffusion with continuous-time consistency distill","arxiv_match":false}],"id":"2310.00426","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":764},{"title":"DPM-Solver++: Fast solver for guided sampling of diffusion probabilistic models","arxiv_id":"2211.01095","year":2022,"url":"https://arxiv.org/abs/2211.01095","citing_submissions":132,"explicit_arxiv_matches":37,"examples":[{"id":"IdwA2KX2G3","title":"StateWise: From Noise Awareness to State-Calibrated MLLM Conditioning for Unified Generation","forum_url":"https://openreview.net/forum?id=IdwA2KX2G3","excerpt":"dels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15740–15751, June 2026b. Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. Machine Intelligence Research, 22 (4):730–751, 2025. Zhengyao Lv, Tianlin Pan, Chenyang Si, Zhaoxi Chen, Wangmeng Zuo, Ziwei Liu, and KwanYee K. Wong. Rethinking cross-modal interaction in multimoda","arxiv_match":false},{"id":"mWR71nELqS","title":"One Budget, Many Requests: Joint Compute Allocation for Diffusion Caching","forum_url":"https://openreview.net/forum?id=mWR71nELqS","excerpt":"Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. Machine Intelligence Research, 22 (4):730–751, 2025a. Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling","arxiv_match":false},{"id":"6tyKsvLVg6","title":"Understanding and Mitigating State Exposure Mismatch in Flow Model Reward Alignment","forum_url":"https://openreview.net/forum?id=6tyKsvLVg6","excerpt":", Xintao Wang, Pengfei Wan, Di ZHANG, and Wanli Ouyang. Flow-GRPO: Training flow matching models via online RL. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=oCBKGw5HNf. Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. DPM-Solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095, 2022. URL https://arxiv.org/abs/2211.01095. Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, and Rita Cucchiara. Input perturbat","arxiv_match":true}],"id":"2211.01095","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":765},{"title":"HPSv3: Towards Wide-Spectrum Human Preference Score","arxiv_id":"2508.03789","year":2025,"url":"https://arxiv.org/abs/2508.03789","citing_submissions":132,"explicit_arxiv_matches":30,"examples":[{"id":"a8kXHXfoY7","title":"Learning Anisotropic Prediction Targets for Flow Models","forum_url":"https://openreview.net/forum?id=a8kXHXfoY7","excerpt":"Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. In ECCV, 2024. Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li. Hpsv3: Towards wide-spectrum human preference score. In ICCV, 2025. Zehong Ma, Longhui Wei, Shuai Wang, Shiliang Zhang, and Qi Tian. Deco: Frequency-decoupled pixel diffusion for end-to-end image generation. In CVPR, 2026b. Zehong Ma, Ruihan Xu, and S","arxiv_match":false},{"id":"JVzG5egPvz","title":"Decoding Without a Decoder: Direct Pixel Prediction from Diffusion Transformers","forum_url":"https://openreview.net/forum?id=JVzG5egPvz","excerpt":" Longhui Wei, Shuai Wang, Shiliang Zhang, and Qi Tian. DeCo: Frequency-decoupled pixel diffusion for end-to-end image generation. In CVPR, 2026a. Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li. Hpsv3: Towards wide-spectrum human preference score. In ICCV, 2025. Zehong Ma, Ruihan Xu, and Shiliang Zhang. Pixelgen: Pixel diffusion beats latent diffusion with perceptual loss. arXiv preprint arXiv:2602.02493, 2026b. Björn Ottosson. A perceptual ","arxiv_match":false},{"id":"tcF72Ul9NA","title":"Arena as Offline Reward: Efficient Fine-Grained Preference Optimization for Diffusion Models","forum_url":"https://openreview.net/forum?id=tcF72Ul9NA","excerpt":"e Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li. Hpsv3: Towards wide-spectrum human preference score. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15086–15095, 2025. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the","arxiv_match":false}],"id":"2508.03789","metadata_source":"Cached arXiv API metadata","rank":766},{"title":"OmniGen2: Towards Instruction-Aligned Multimodal Generation","arxiv_id":"2506.18871","year":2025,"url":"https://arxiv.org/abs/2506.18871","citing_submissions":132,"explicit_arxiv_matches":88,"examples":[{"id":"IdwA2KX2G3","title":"StateWise: From Noise Awareness to State-Calibrated MLLM Conditioning for Unified Generation","forum_url":"https://openreview.net/forum?id=IdwA2KX2G3","excerpt":" Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, et al. Janus: Decoupling visual encoding for unified multimodal understanding and generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 12966–12977, 2025b. Chenyuan Wu, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, Junjie Zhou, et al. Omnigen2: Exploration to advanced multimodal generation. arXiv preprint arXiv:2506.18871, 2025c. Qiucheng Wu, Yujian Liu, Handong Zhao, Trung Bui, Zhe Lin, Yang Zhang, and Shiyu Chang. Har","arxiv_match":true},{"id":"ItanNdt5x7","title":"WithEveryone: Unified Planning and Identity Grounding for Group Image Generation","forum_url":"https://openreview.net/forum?id=ItanNdt5x7","excerpt":" Yongqiang Zhu, Yujia Wu, Yuxuan Cai, and Zenan Liu. Qwen-image technical report, 2025a. URL https://arxiv.org/abs/ 2508.02324. Chenyuan Wu, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, Junjie Zhou, Ze Liu, Ziyi Xia, Chaofan Li, Haoge Deng, Jiahao Wang, Kun Luo, Bo Zhang, Defu Lian, Xinlong Wang, Zhongyuan Wang, Tiejun Huang, and Zheng Liu. Omnigen2: Exploration to advanced multimodal generation. arXiv preprint arXiv:2506.18871, 2025b. Shaojin Wu, Mengqi Huang, Yufeng Cheng, Wenxu Wu, Jiahe Tian, Yiming Luo, Fei Ding, and Qia","arxiv_match":true},{"id":"wm97mtaTWL","title":"SolveEdit: Benchmarking Visual Problem Solving in Generative Models","forum_url":"https://openreview.net/forum?id=wm97mtaTWL","excerpt":"n-image technical report, 2025b. URL https://arxiv.org/abs/ 2508.02324. Chenyuan Wu, Jiahao Wang, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, et al. Omnigen2: Towards instruction-aligned multimodal generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21964–21975, 2026a. Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Chunyi Li,","arxiv_match":false}],"id":"2506.18871","metadata_source":"Cached arXiv API metadata","rank":767},{"title":"Persona Vectors: Monitoring and Controlling Character Traits in Language Models","arxiv_id":"2507.21509","year":2025,"url":"https://arxiv.org/abs/2507.21509","citing_submissions":132,"explicit_arxiv_matches":120,"examples":[{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":" Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J Pappas, Florian Tramer, et al. Jailbreakbench: An open robustness benchmark for jailbreaking large language models. Advances in Neural Information Processing Systems, 37:55005–55029, 2024. Runjin Chen, Andy Arditi, Henry Sleight, Owain Evans, and Jack Lindsey. Persona vectors: Monitoring and controlling character traits in language models. arXiv preprint arXiv:2507.21509, 2025. Zachary Coalson, Beth Sohler, Aiden Gabriel, and Sanghyun Hong. Fail-closed alignment for la","arxiv_match":true},{"id":"gRDe98c41c","title":"The Model’s Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges","forum_url":"https://openreview.net/forum?id=gRDe98c41c","excerpt":"l. In Proceedings of the 38th European Conference on Information Retrieval, 2016. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. Runjin Chen, Andy Arditi, Henry Sleight, Owain Evans, and Jack Lindsey. Persona vectors: Monitoring and controlling character traits in language models. arXiv preprint arXiv:2507.21509, 2025. Sahana Chennabasappa, Cyrus Nikolaidis, Daniel Song, David Molnar, Stephanie Ding, Shengye W","arxiv_match":true},{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":". URL https://blogs.bing.com/search/february-2023/The-new-Bing-Edge-L earning-from-our-first-week. Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong. Jailbreaking black box large language models in twenty queries. arXiv preprint arXiv:2310.08419, 2023. Runjin Chen, Andy Arditi, Henry Sleight, Owain Evans, and Jack Lindsey. Persona vectors: Monitoring and controlling character traits in language models. arXiv preprint arXiv:2507.21509, 2025. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, a","arxiv_match":true}],"id":"2507.21509","metadata_source":"Cached arXiv API metadata","rank":768},{"title":"Privileged Information Distillation for Language Models","arxiv_id":"2602.04942","year":2026,"url":"https://arxiv.org/abs/2602.04942","citing_submissions":132,"explicit_arxiv_matches":94,"examples":[{"id":"ZFCw6a3QKH","title":"Not All Privilege Is Distillable: Calibrating Teacher Corrections for Few-Step Video Generation","forum_url":"https://openreview.net/forum?id=ZFCw6a3QKH","excerpt":"–4655, 2026. Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. Emiliano Penaloza, Dheeraj Vattikonda, Nicolas Gontier, Alexandre Lacoste, Laurent Charlin, and Massimo Caccia. Privileged information distillation for language models. arXiv preprint arXiv:2602.04942, 2026. Neta Shaul, Chao Liu, Arash Vahdat, and Julius Berner. Parallel decoding distillation for fa","arxiv_match":true},{"id":"50kJuTajGS","title":"ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation","forum_url":"https://openreview.net/forum?id=50kJuTajGS","excerpt":"rXiv:2601.20802, 2026. Siru Ouyang, Jun Yan, I-Hung Hsu, Yanfei Chen, Ke Jiang, Zifeng Wang, Rujun Han, Long T. Le, Samira Daruki, Xiangru Tang, et al. Reasoningbank: Scaling agent self-evolving with reasoning memory, 2026. URL https://arxiv.org/abs/2509.25140. Emiliano Penaloza, Dheeraj Vattikonda, Nicolas Gontier, Alexandre Lacoste, Laurent Charlin, and Massimo Caccia. Privileged information distillation for language models, 2026. URL https: //arxiv.org/abs/2602.04942. Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss, Peter van der Putten, and Kees Joost Baten","arxiv_match":true},{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":"e models. arXiv preprint arXiv:2604.08527, 2026. URL https://arxiv.org/abs/ 2604.08527. Emiliano Penaloza, Dheeraj Vattikonda, Nicolas Gontier, Alexandre Lacoste, Laurent Charlin, and Massimo Caccia. Privileged information distillation for language models. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum? id=ebZcMQImhG. Qwen Team. Qwen3 technical report. arXiv preprint arXiv:2505.09388, 2025. Qwen Tea","arxiv_match":false}],"id":"2602.04942","metadata_source":"Cached arXiv API metadata","rank":769},{"title":"RL's Razor: Why Online Reinforcement Learning Forgets Less","arxiv_id":"2509.04259","year":2025,"url":"https://arxiv.org/abs/2509.04259","citing_submissions":132,"explicit_arxiv_matches":51,"examples":[{"id":"24BVafKc1c","title":"Continually Post-training Small Models via Synthetic Self-distillation","forum_url":"https://openreview.net/forum?id=24BVafKc1c","excerpt":"ation enables continual learning. In Forty-third International Conference on Machine Learning, 2026a. URL https://openreview.net/forum?id=qA6FgH0nnZ. Idan Shenfeld, Jyothish Pari, and Pulkit Agrawal. RL’s razor: Why online reinforcement learning forgets less. In The Fourteenth International Conference on Learning Representations, 2026b. URL https://openreview.net/forum?id=7HNRYT4V44. Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang","arxiv_match":false},{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":": Scaling automated process verifiers for llm reasoning. In International Conference on Learning Representations, volume 2025, pp. 60808–60838, 2025. Idan Shenfeld, Jyothish Pari, and Pulkit Agrawal. Rl’s razor: Why online reinforcement learning forgets less. In International Conference on Learning Representations, volume 2026, pp. 59839–59864, 2026. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language ag","arxiv_match":false},{"id":"vTUJnLXc91","title":"Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation","forum_url":"https://openreview.net/forum?id=vTUJnLXc91","excerpt":"tion enables continual learning. In Forty-third International Conference on Machine Learning, 2026a. URL https: //openreview.net/forum?id=qA6FgH0nnZ. Idan Shenfeld, Jyothish Pari, and Pulkit Agrawal. Rl's razor: Why online reinforcement learning forgets less. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 59839–59864, 2026b. URL https://proceedings.ic","arxiv_match":false}],"id":"2509.04259","metadata_source":"Cached arXiv API metadata","rank":770},{"title":"ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution","arxiv_id":"2509.19349","year":2025,"url":"https://arxiv.org/abs/2509.19349","citing_submissions":132,"explicit_arxiv_matches":54,"examples":[{"id":"P9bJmoIx5X","title":"Controlling False Discoveries via Auditing Scientific Agents","forum_url":"https://openreview.net/forum?id=P9bJmoIx5X","excerpt":"e and false discovery exceedance. The Annals of Statistics, 46(2):526–554, 2018. doi: 10.1214/17-AOS1559. URL https://doi.org/10.1214/17-AOS1559. Robert Tjarko Lange, Yuki Imajuku, and Edoardo Cetin. ShinkaEvolve: Towards open-ended and sample-efficient program evolution. In International Conference on Learning Representations, 2026. URL https://iclr.cc/virtual/2026/poster/10007692. Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, Michaela M. Hinks, Michael J. Hammer","arxiv_match":false},{"id":"ZIVFDKAh8p","title":"Automated Algorithm Discovery through Mathematical Reformulation: A Study in Visual-Token Reduction","forum_url":"https://openreview.net/forum?id=ZIVFDKAh8p","excerpt":"an Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV, pp. 235–251. Springer, 2016. URL https://arxiv.org/abs/1603.07396. Robert Lange, Yuki Imajuku, and Edoardo Cetin. ShinkaEvolve: Towards open-ended and sampleefficient program evolution. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 74026– 74078, 2026. URL https://proceedings.ic","arxiv_match":false},{"id":"Y3u3Z56znq","title":"Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization","forum_url":"https://openreview.net/forum?id=Y3u3Z56znq","excerpt":" arXiv:1412.6980, 2014. Siheng Li, Zhanhui Zhou, Wai Lam, Chao Yang, and Chaochao Lu. Repo: Replay-enhanced policy optimization. arXiv:2506.09340, 2025. Robert Lange, Yuki Imajuku, and Edoardo Cetin. Shinkaevolve: Towards open-ended and sampleefficient program evolution. In Proceedings of the 14th International Conference on Learning Representations, Rio de Janeiro, Brazil, 2026. Xiaoya Li, Xiaofei Sun, Albert Wang, Jiwei Li, and Chris Shum. Cuda-l1: Improving CUDA ","arxiv_match":false}],"id":"2509.19349","metadata_source":"Cached arXiv API metadata","rank":771},{"title":"DuoAttention: Efficient long-context LLM inference with retrieval and streaming heads","arxiv_id":"2410.10819","year":2024,"url":"https://arxiv.org/abs/2410.10819","citing_submissions":131,"explicit_arxiv_matches":44,"examples":[{"id":"okGS6velwf","title":"Focus-KV: Head-Wise KV Cache Compression Focus On Reasoning Heads","forum_url":"https://openreview.net/forum?id=okGS6velwf","excerpt":"kv cache compression for efficient llm inference, 2025. URL https://arxiv.org/abs/2504.09936. Hao Wang, Ligong Han, Kai Xu, and Akash Srivastava. Squat: Subspace-orthogonal kv cache quantization, 2025. URL https://arxiv.org/abs/2503.24358. Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Junxian Guo, Shang Yang, Haotian Tang, Yao Fu, and Song Han. Duoattention: Efficient long-context llm inference with retrieval and streaming heads, 2024a. URL https://arxiv.org/abs/2410.10819. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language ","arxiv_match":true},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"th attention sinks. In The Twelfth International Conference on Learning Representations, 2024b. Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Junxian Guo, Shang Yang, Haotian Tang, Yao Fu, and Song Han. DuoAttention: Efficient long-context LLM inference with retrieval and streaming heads. In The Thirteenth International Conference on Learning Representations, 2025. Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xiong, and Doyen Sahoo. ThinK:","arxiv_match":false},{"id":"10fWGFFHX7","title":"Forcing-KV: Hybrid KV Cache Compression for Efficient Autoregressive Video Diffusion","forum_url":"https://openreview.net/forum?id=10fWGFFHX7","excerpt":"e Models with Attention Sinks. In The Twelfth International Conference on Learning Representations, 2023. Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Shang Yang, Haotian Tang, Yao Fu, Song Han, et al. DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads. In The Thirteenth International Conference on Learning Representations, 2024. Steven Xiao, XIndi Zhang, Dechao Meng, Qi Wang, Peng Zhang, and Bang Zhang. Knot Forcing: Taming Autoregressive Video Di","arxiv_match":false}],"id":"2410.10819","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":772},{"title":"How to use and interpret activation patching","arxiv_id":"2404.15255","year":2024,"url":"https://arxiv.org/abs/2404.15255","citing_submissions":131,"explicit_arxiv_matches":121,"examples":[{"id":"ncOxUOWsiS","title":"Frequency-Steered VAEs: Restoring Fine Details in Low-Resolution Latents","forum_url":"https://openreview.net/forum?id=ncOxUOWsiS","excerpt":"ansactions on Pattern Analysis and Machine Intelligence, 13(9):891–906, 1991. doi: 10.1109/34.93808. Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzyńska, and David Bau. Unified concept editing in diffusion models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5111–5120, January 2024. Stefan Heimersheim and Neel Nanda. How to use and interpret activation patching, 2024. URL https://arxiv.org/abs/2404.15255. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans tr","arxiv_match":true},{"id":"CONFJT1Bkx","title":"Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning","forum_url":"https://openreview.net/forum?id=CONFJT1Bkx","excerpt":"are Karolina Dziugaite. Mechanistic unlearning: Robust knowledge unlearning and editing via mechanistic localization. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=92oBV5HAGl. Stefan Heimersheim and Jett Janiak. A circuit for python docstrings in a 4-layer attention-only transformer. In Alignment Forum, 2023. Stefan Heimersheim and Neel Nanda. How to use and interpret activation patching. arXiv preprint arXiv:2404.15255, 2024. Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang,","arxiv_match":true},{"id":"aE2Bm53N5i","title":"Behaviorally Silent Now, Causally Active Later: Deferred State Effects in Recurrent Policies for Reinforcement Learning","forum_url":"https://openreview.net/forum?id=aE2Bm53N5i","excerpt":"Potts, Thomas Icard, and Noah Goodman. Finding alignments between interpretable causal variables and distributed neural representations. In Causal Learning and Reasoning, pp. 160–187. PMLR, 2024. Bear Häon, Kaylene Stocking, Ian Chuang, and Claire Tomlin. Mechanistic interpretability for steering vision-language-action models. arXiv preprint arXiv:2509.00328, 2025. Stefan Heimersheim and Neel Nanda. How to use and interpret activation patching. arXiv preprint arXiv:2404.15255, 2024. Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel. Adversarial atta","arxiv_match":true}],"id":"2404.15255","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":773},{"title":"The Vendi Score: A diversity evaluation metric for machine learning","arxiv_id":"2210.02410","year":2022,"url":"https://arxiv.org/abs/2210.02410","citing_submissions":131,"explicit_arxiv_matches":50,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":", Yuanhao Ban, and ChoJui Hsieh. Self-forcing++: Towards minute-scale high-quality video generation. In International Conference on Learning Representations, 2026. Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning. Transactions on Machine Learning Research, 2023. Junyao Gao, Jiaxing Li, Wenran Liu, Yanhong Zeng, Fei Shen, Kai Chen, Yanan Sun, and Cairong Zhao. Charactershot: Controllable and consistent 4d char","arxiv_match":false},{"id":"7x4AAlYBDM","title":"Stabilizing Multi-Step Diffusion Distillation with Student Perturbation Consistency","forum_url":"https://openreview.net/forum?id=7x4AAlYBDM","excerpt":"hao Gong, and Lei Yang. Phased dmd: Few-step distribution matching distillation via score matching within subintervals, 2025. URL https://arxiv.org/abs/2510.27684. Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimen","arxiv_match":false},{"id":"g0vDUvCHlP","title":"Video-Index: A Curated Meta-Benchmark for Video Understanding Evaluation","forum_url":"https://openreview.net/forum?id=g0vDUvCHlP","excerpt":"llm benchmarks: Knowledge, spatial perception, or true temporal understanding? arXiv preprint arXiv:2505.14321, 2025. Marc Finzi, Shikai Qiu, Yiding Jiang, Pavel Izmailov, J Zico Kolter, and Andrew Gordon Wilson. From entropy to epiplexity: Rethinking information for computationally bounded intelligence. arXiv preprint arXiv:2601.03220, 2026. Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning. arXiv preprint arXiv:2210.02410, 2022. Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Z","arxiv_match":true}],"id":"2210.02410","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":774},{"title":"DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning","arxiv_id":"2504.11456","year":2025,"url":"https://arxiv.org/abs/2504.11456","citing_submissions":130,"explicit_arxiv_matches":74,"examples":[{"id":"Oou4LAMTNI","title":"HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training","forum_url":"https://openreview.net/forum?id=Oou4LAMTNI","excerpt":"bhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, et al. The llama 3 herd of models, 2024. URL https://arxiv.org/abs/2407.21783. Zhiwei He, Tian Liang, Jiahao Xu, Qiuzhi Liu, Xingyu Chen, Yue Wang, Linfeng Song, Dian Yu, Zhenwen Liang, Wenxuan Wang, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. Deepmath-103k: A large-scale, challenging, decontaminated, and verifiable mathematical dataset for advancing reasoning, 2025. URL https://arxiv.org/abs/2504.11456. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and","arxiv_match":true},{"id":"IkzBak1MYU","title":"RL Starts Before RL: Rethinking Policy Initialization for Outcome-Based RL","forum_url":"https://openreview.net/forum?id=IkzBak1MYU","excerpt":"haoqun He, Renjie Luo, Yuzhuo Bai, et al. OlympiadBench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems. arXiv preprint arXiv:2402.14008, 2024. URL https://arxiv.org/abs/2402.14008. Zhiwei He, Tian Liang, Jiahao Xu, et al. DeepMath-103K: A large-scale, challenging, decontaminated, and verifiable mathematical dataset for advancing reasoning. arXiv preprint arXiv:2504.11456, 2025. URL https://arxiv.org/abs/2504.11456. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv","arxiv_match":true},{"id":"ndjcK7Ozn2","title":"Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation","forum_url":"https://openreview.net/forum?id=ndjcK7Ozn2","excerpt":"038/S41586-025-09422-Z. URL https://doi.org/10.1038/s41586-025-09422-z. Zhiwei He, Tian Liang, Jiahao Xu, Qiuzhi Liu, Xingyu Chen, Yue Wang, Linfeng Song, Dian Yu, Zhenwen Liang, Wenxuan Wang, et al. Deepmath-103k: A large-scale, challenging, decontaminated, and verifiable mathematical dataset for advancing reasoning. In International Conference on Learning Representations, volume 2026, pp. 138306–138322, 2026. Byeongho Heo, Jaehui Hwang, Sangdoo Yun, and Dongyoon Han. On-policy delta distillation. arXiv preprint","arxiv_match":false}],"id":"2504.11456","metadata_source":"Cached arXiv API metadata","rank":775},{"title":"Densely Connected Convolutional Networks","arxiv_id":null,"year":2016,"url":null,"citing_submissions":130,"explicit_arxiv_matches":0,"examples":[{"id":"uTIoY3tprZ","title":"More Information, Less Uncertainty? Monotonic Uncertainty Regularization under Progressive Information","forum_url":"https://openreview.net/forum?id=uTIoY3tprZ","excerpt":"genette: A smaller subset of 10 easily classified classes from imagenet, March 2019. URL https://github.com/fastai/imagenette. Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269, 2016. URL https://api.semanticscholar.org/ CorpusID:9433631. Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laur","arxiv_match":false},{"id":"Eqb6662X3z","title":"Multi-Stream Attention Residuals","forum_url":"https://openreview.net/forum?id=Eqb6662X3z","excerpt":"and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021. [16] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708, 2017. [17] Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu","arxiv_match":false},{"id":"BD6Ba3FgW7","title":"CLAIR-Med: Selective Neural–Symbolic Reasoning with Weakly Supervised Concepts for Medical Image Classification","forum_url":"https://openreview.net/forum?id=BD6Ba3FgW7","excerpt":"rning and Knowledge Discovery in Databases (ECML PKDD 2025), volume 16015 of Lecture Notes in Computer Science. Springer, 2025. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp. 2261–2269. Ieee, 2017. Daniel S Kermany, Michael H Goldbaum, et al. Identifying medical diagnoses and treatable disease","arxiv_match":false}],"id":"73382a35f6304230","metadata_source":"Curated bibliography catalog / publisher metadata","rank":776},{"title":"Exploration by random network distillation","arxiv_id":"1810.12894","year":2018,"url":"https://arxiv.org/abs/1810.12894","citing_submissions":130,"explicit_arxiv_matches":56,"examples":[{"id":"cKgE4AvBla","title":"Exploring What Matters: Value-Aware Exploration for Dreamer","forum_url":"https://openreview.net/forum?id=cKgE4AvBla","excerpt":", Tom Schaul, David Saxton, and Remi Munos. Unifying count-based exploration and intrinsic motivation. Advances in neural information processing systems, 29, 2016. Lukas Bierling, Davide Pasero, Jan-Henrik Bertrand, and Kiki Van Gerwen. DreamerV3-XP: Optimizing exploration through uncertainty estimation. arXiv preprint arXiv:2510.21418, 2025. Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov. Exploration by random network distillation. arXiv preprint arXiv:1810.12894, 2018. Alexandra Carpentier and Rémi Munos. Finite time analysis of stratified sampling for Monte C","arxiv_match":true},{"id":"mbmr0VIBUV","title":"Joint Agent Memory and Exploration Learning via Novelty Signals","forum_url":"https://openreview.net/forum?id=mbmr0VIBUV","excerpt":"52202/ 068431-0805. URL https://proceedings.neurips.cc/paper_files/paper/ 2022/file/47e288629a6996a17ce50b90a056a0e1-Paper-Conference.pdf. Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov. Exploration by random network distillation. In International Conference on Learning Representations, 2019. URL https: //openreview.net/forum?id=H1lJJnR5Ym. Thibault Le Sellier de Chezelles, Maxime Gasse, Alexandre Lacoste, Massimo Caccia, Ale","arxiv_match":false},{"id":"iBXhdO0M1o","title":"EDGE: RESIDUAL-CONDITIONED CORRECTION ACQUISITION FOR VLA POST-TRAINING","forum_url":"https://openreview.net/forum?id=iBXhdO0M1o","excerpt":"ert Equi, Chelsea Finn, et al. π0 : A vision-language-action flow model for general robot control. In Robotics: Science and Systems, 2025. Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov. Exploration by random network distillation. In International Conference on Learning Representations, 2019. Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine. Deep reinforcement learning in a handful of trials using probabil","arxiv_match":false}],"id":"1810.12894","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":777},{"title":"Gemma: Open models based on Gemini research and technology","arxiv_id":"2403.08295","year":2024,"url":"https://arxiv.org/abs/2403.08295","citing_submissions":130,"explicit_arxiv_matches":121,"examples":[{"id":"bp0HEA9om7","title":"Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters","forum_url":"https://openreview.net/forum?id=bp0HEA9om7","excerpt":" 2025. Rui Song, Lida Shi, Ruihua Qi, Yingji Li, and Hao Xu. Enhancing multimodal large language models for ancient chinese character evolution analysis via glyph-driven fine-tuning. arXiv preprint arXiv:2604.11299, 2026. Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. Gemma: Open models based on Gemini research and technology. arXiv preprint arXiv:2403.08295, 2024. Hunyuan Vision Team, Pengyuan Lyu, Xingyu Wan, Gengluo Li, Shangpin Peng, Weinong Wang, Lian","arxiv_match":true},{"id":"5RupDwqAIh","title":"CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence","forum_url":"https://openreview.net/forum?id=5RupDwqAIh","excerpt":"stian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295, 2024. Rubèn Tito, Dimosthenis Karatzas, and Ernest Valveny. Hierarchical multimodal transformers f","arxiv_match":true},{"id":"K9wqTXAYns","title":"LLARA: Low-Rank Linear Attention Residual Adaptation for Large Language Models","forum_url":"https://openreview.net/forum?id=K9wqTXAYns","excerpt":"raining verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. Róbert Csordás, Christopher D. Manning, and Christopher Potts. Do language models use their depth efficiently? In Advances in Neural Information Processing Systems, 2025. DeepSeek-AI. DeepSeek-V3.2: Pushing the frontier of open large language models. arXiv preprint arXiv:2512.02556, 2025. Gemma Team. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295, 2024. Andrey Gromov, Kushal Tirumala, Hassan Shapourian, Paolo Glorioso, and Daniel A. Roberts. Th","arxiv_match":true}],"id":"2403.08295","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":778},{"title":"KernelBench: Can LLMs Write Efficient GPU Kernels?","arxiv_id":"2502.10517","year":2025,"url":"https://arxiv.org/abs/2502.10517","citing_submissions":130,"explicit_arxiv_matches":49,"examples":[{"id":"fmF0tgNvsX","title":"MetaKernelBench: Measuring GPU Kernel Knowledge Transfer Beyond Code","forum_url":"https://openreview.net/forum?id=fmF0tgNvsX","excerpt":"Frontier intelligence that scales with your ambition. https://openai.com/ index/gpt-5-6/, 2026. Anne Ouyang, Simon Guo, Simran Arora, Alex L Zhang, William Hu, Christopher Ré, and Azalia Mirhoseini. Kernelbench: Can llms write efficient gpu kernels? In ICML, 2025. Qwen Team. Qwen3.8-flash-next: A new architecture, towards ultimate cost-efficiency. https: //qwen.ai/blog?id=qwen3.8-flash-next, 2026. Qitong Sun, Jun Han, Tianlin Li, Zhe Tang, Shen","arxiv_match":false},{"id":"dqnEeuOxg0","title":"PTXBench: Interpretable Evaluation of LLMs for Architecture-Specific GPU Programming","forum_url":"https://openreview.net/forum?id=dqnEeuOxg0","excerpt":"6 knowledge cutoff is documented at https://developers.openai.com/api/docs/models/gpt-5. 6-sol. Anne Ouyang, Simon Guo, Simran Arora, Alex L Zhang, William Hu, Christopher Ré, and Azalia Mirhoseini. KernelBench: Can LLMs write efficient GPU kernels? In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 47356–47415. PMLR, 2025. URL https://proceedings.mlr.press/ v267/","arxiv_match":false},{"id":"SaboBZsQiW","title":"KernelWeaver: A Shape-Aware Kernel Agent for GPU Kernel Optimization","forum_url":"https://openreview.net/forum?id=SaboBZsQiW","excerpt":"ernational Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=GmqZ3WvkeV. Anne Ouyang, Simon Guo, Simran Arora, Alex L Zhang, William Hu, Christopher Re, and Azalia Mirhoseini. Kernelbench: Can LLMs write efficient GPU kernels? In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id= yeoN1iQT1x. Haolin Pan, Hongyu Lin, Haoran Luo, Yang Liu, Kaichun Yao, Libo Zhang, Mingjie Xi","arxiv_match":false}],"id":"2502.10517","metadata_source":"Cached arXiv API metadata","rank":779},{"title":"Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models","arxiv_id":"2503.06749","year":2025,"url":"https://arxiv.org/abs/2503.06749","citing_submissions":130,"explicit_arxiv_matches":71,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Wenxuan Huang, Bohan Jia, Shaosheng Cao, Zheyu Ye, Zhe Xu, Yao Hu, Shaohui Lin, et al. Visionr1: Incentivizing reasoning capability in multimodal large language models. In International Conference on Learning Representations, volume 2026, pp. 63794–63812, 2026a. Zihan Huang, Junda Wu, Tong Yu, Qianqi Yan, Rohan Surana, Uttaran Bhattacharya, Lina Yao, Xin Eric Wang,","arxiv_match":false},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"tanding. arXiv preprint arXiv:2501.05452, 2025. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space, 2025. URL https: //arxiv.org/abs/2412.06769. Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, and Shaohui Lin. Vision-r1: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749, 2025. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostro","arxiv_match":true},{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":" Ji Qi, Junhui Ji, Lihang Pan, et al. Glm-4.1v-thinking: Towards versatile multimodal reasoning with scalable reinforcement learning. arXiv preprint arXiv:2507.01006v1, 2025. URL https: //arxiv.org/abs/2507.01006v1. Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Yao Hu, and Shaohui Lin. Vision-r1: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749v1, 2025. URL https://arxiv.org/abs/2503.06749v1. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Aki","arxiv_match":true}],"id":"2503.06749","metadata_source":"Cached arXiv API metadata","rank":780},{"title":"Agent-safetybench: Evaluating the safety of llm agents","arxiv_id":"2412.14470","year":2024,"url":"https://arxiv.org/abs/2412.14470","citing_submissions":129,"explicit_arxiv_matches":114,"examples":[{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":"mary/model, 2026. Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik R Narasimhan. τ -bench: A benchmark for tool-agent-user interaction in real-world domains. In The Thirteenth International Conference on Learning Representations, 2025. Z.ai. GLM-5.3. https://docs.z.ai/guides/llm/glm-5.3, 2026. Zhexin Zhang, Shiyao Cui, Yida Lu, Jingzhuo Zhou, Junxiao Yang, Hongning Wang, and Minlie Huang. Agent-safetybench: Evaluating the safety of llm agents. arXiv preprint arXiv:2412.14470, 2024. Zhiping Zhang, Yi Evie Zhang, Freda Shi, and Tianshi Li. Autonomy matters: A study on person","arxiv_match":true},{"id":"IKUjfAknOl","title":"Agent Hacks Agents: Autoresearch Discovers Vulnerabilities in Production Agents","forum_url":"https://openreview.net/forum?id=IKUjfAknOl","excerpt":"ool use. In Findings of the Association for Computational Linguistics: ACL 2026, pp. 25105– 25129, 2026. Zheng Zhang, Jiarui He, Yuchen Cai, Deheng Ye, Peilin Zhao, Ruili Feng, and Hao Wang. Genesis: Evolving attack strategies for llm web agent red-teaming. arXiv preprint arXiv:2510.18314, 2025. Zhexin Zhang, Shiyao Cui, Yida Lu, Jingzhuo Zhou, Junxiao Yang, Hongning Wang, and Minlie Huang. Agent-safetybench: Evaluating the safety of llm agents. arXiv preprint arXiv:2412.14470, 2024. Andy Zhou, Kevin Wu, Francesco Pinto, Zhaorun Chen, Yi Zeng, Yu Yang, Shuang Yang, Sanmi Koy","arxiv_match":true},{"id":"EChjbaBYts","title":"Surface Cues Are Not Enough: Towards Content-Grounded Defenses for Tool-Using Agents","forum_url":"https://openreview.net/forum?id=EChjbaBYts","excerpt":"nergizing reasoning and acting in language models. In ICLR, 2023. Zheng-Xin Yong, Parv Mahajan, Andy Wang, Ida Caspary, Yernat Yestekov, Zora Che, Mosh Levy, Elle Najt, Dennis Murphy, Prashant Kulkarni, et al. An independent safety evaluation of kimi k2. 5. arXiv preprint arXiv:2604.03121, 2026. Zhexin Zhang, Shiyao Cui, Yida Lu, Jingzhuo Zhou, Junxiao Yang, Hongning Wang, and Minlie Huang. Agent-safetybench: Evaluating the safety of llm agents. arXiv preprint arXiv:2412.14470, 2024a. Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui","arxiv_match":true}],"id":"2412.14470","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":781},{"title":"AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems","arxiv_id":"2503.06669","year":2025,"url":"https://arxiv.org/abs/2503.06669","citing_submissions":129,"explicit_arxiv_matches":90,"examples":[{"id":"B65JGdo51z","title":"Distilling World Understanding from Video Generators for Robot Control","forum_url":"https://openreview.net/forum?id=B65JGdo51z","excerpt":" et al. π0 : A Vision-LanguageAction Flow Model for General Robot Control, 2024. Qingwen Bu, Jisong Cai, Li Chen, Xiuqi Cui, Yan Ding, Siyuan Feng, Shenyuan Gao, Xindong He, Xuan Hu, Xu Huang, et al. AgiBot World Colosseo: A Large-Scale Manipulation Platform for Scalable and Intelligent Embodied Systems, 2025. Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, et al. WorldVLA: Towards Autoregressive Action World Model, 2025. Yu-Wei Chao","arxiv_match":false},{"id":"Y8naMKuOst","title":"TARGET-VLA: Mask-Guided Visual Retrieval for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=Y8naMKuOst","excerpt":"bajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817, 2022. Qingwen Bu, Jisong Cai, Li Chen, Xiuqi Cui, Yan Ding, Siyuan Feng, Shenyuan Gao, Xindong He, Xuan Hu, Xu Huang, et al. Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems. arXiv preprint arXiv:2503.06669, 2025a. Qingwen Bu, Yanting Yang, Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, an","arxiv_match":true},{"id":"M3vheUan4w","title":"Ordered Action Transport for Generative Robot Policies","forum_url":"https://openreview.net/forum?id=M3vheUan4w","excerpt":", Yu Qiao, Guanghui Ren, Cheng Ruan, Jiaqi Shan, Yongjian Shen, Chengshi Shi, Mingkang Shi, Modi Shi, Chonghao Sima, Jianheng Song, Huijie Wang, Wenhao Wang, Dafeng Wei, Chengen Xie, Guo Xu, Junchi Yan, Cunbiao Yang, Lei Yang, Shukai Yang, Maoqing Yao, Jia Zeng, Chi Zhang, Qinglin Zhang, Bin Zhao, Chengyue Zhao, Jiaqi Zhao, and Jianchao Zhu. AgiBot World Colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems. arXiv preprint arXiv:2503.06669, 2025. URL https://arxiv. org/abs/2503.06669. Homanga Bharadhwaj, Debidatta Dwibedi, Abhinav Gupta,","arxiv_match":true}],"id":"2503.06669","metadata_source":"Cached arXiv API metadata","rank":782},{"title":"Debertav3: Improving DeBERTa using electra-style pre-training with gradient-disentangled embedding sharing","arxiv_id":"2111.09543","year":2021,"url":"https://arxiv.org/abs/2111.09543","citing_submissions":129,"explicit_arxiv_matches":56,"examples":[{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"l learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016. doi: 10.1109/CVPR.2016.90. Pengcheng He, Jianfeng Gao, and Weizhu Chen. DeBERTav3: Improving deBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview. net/forum?id=sE7-XhLxHA. Youngkyu Hong, Seungju Han, Kwanghee Choi, Seokjun Seo, Beomsu Kim, and B","arxiv_match":false},{"id":"9cpSUDXx9d","title":"LLM Uncertainty Revealed in Behavioral Boundary Effects","forum_url":"https://openreview.net/forum?id=9cpSUDXx9d","excerpt":"uncertainty quantification. In The Fourteenth International Conference on Learning Representations, 2026. URL https: //openreview.net/forum?id=lOoRJo8xWy. Pengcheng He, Jianfeng Gao, and Weizhu Chen. DeBERTaV3: Improving DeBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview. net/forum?id=sE7-XhLxHA. Xanh Ho, Jiahao Huang, Florian Boudin, and Akiko Aizawa. LLM-as-a-Judge: ","arxiv_match":false},{"id":"1kdyECT6ST","title":"Credit Follows Use: Used-Grouped Policy Optimization  for Long-Context Memory Agents","forum_url":"https://openreview.net/forum?id=1kdyECT6ST","excerpt":" Hipporag: Neurobiologically inspired long-term memory for large language models. Advances in neural information processing systems, 37:59532–59569, 2024. Pengcheng He, Jianfeng Gao, and Weizhu Chen. DeBERTaV3: improving DeBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. URL https://openreview.net/forum?id= sE7-XhLxHA. Xanh Ho, Anh-Kh","arxiv_match":false}],"id":"2111.09543","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":783},{"title":"MUSE: Machine unlearning sixway evaluation for language models","arxiv_id":"2407.06460","year":2024,"url":"https://arxiv.org/abs/2407.06460","citing_submissions":129,"explicit_arxiv_matches":37,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"ns, Danqi Chen, and Luke Zettlemoyer. Detecting pretraining data from large language models. In The Twelfth International Conference on Learning Representations, 2024a. URL https://openreview. net/forum?id=zWqr3MQuNs. Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, and Chiyuan Zhang. Muse: Machine unlearning six-way evaluation for language models, 2024b. URL https://arxiv.org/abs/2407.06460. Weijia Shi, Akshita Bhagia, Kevin Farhat, Niklas Muennighoff, Jacob Morrison, Evan Pete Walsh, Dus","arxiv_match":true},{"id":"bdZAOqTSho","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","forum_url":"https://openreview.net/forum?id=bdZAOqTSho","excerpt":"s. Transactions on Machine Learning Research, 2025. Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, and Chiyuan Zhang. MUSE: Machine unlearning six-way evaluation for language models. In ICLR, 2025. Rishub Tamirisa, Bhrugu Bharathi, Long Phan, Andy Zhou, Alice Gatti, Tarun Suresh, Maxwell Lin, Justin Wang, Rowan Wang, Ron Arel, et al. Tamper-resistant safeguards for open-weight L","arxiv_match":false},{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":"uage models. arXiv preprint arXiv:2308.03825, 2024. Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, and Chiyuan Zhang. MUSE: Machine unlearning six-way evaluation for language models. In ICLR, 2025. Nicholas Sofroniew, Isaac Kauvar, William Saunders, Runjin Chen, Tom Henighan, Sasha Hydrie, Craig Citro, Adam Pearce, Julius Tarng, Wes Gurnee, et al. Emotion concepts and their func","arxiv_match":false}],"id":"2407.06460","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":784},{"title":"Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks","arxiv_id":"2211.12588","year":2022,"url":"https://arxiv.org/abs/2211.12588","citing_submissions":129,"explicit_arxiv_matches":47,"examples":[{"id":"x6oIXJYZ7g","title":"LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs","forum_url":"https://openreview.net/forum?id=x6oIXJYZ7g","excerpt":"ion-answer dataset for common sense. In Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP, pp. 63–69, 2019. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. Transactions on Machine Learning Research, 2023. Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral Kumar, and Aleksand","arxiv_match":false},{"id":"AaaogEZLc6","title":"Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization","forum_url":"https://openreview.net/forum?id=AaaogEZLc6","excerpt":" Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, and Xiaoyu Shen. Reasoning ","arxiv_match":false},{"id":"dvyMwimtP5","title":"Beyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training","forum_url":"https://openreview.net/forum?id=dvyMwimtP5","excerpt":" Frugalgpt: How to use large language models while reducing cost and improving performance. Transactions on Machine Learning Research, 2024. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. Transactions on Machine Learning Research, 2023. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, A","arxiv_match":false}],"id":"2211.12588","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":785},{"title":"Archer: Training language model agents via hierarchical multi-turn rl","arxiv_id":"2402.19446","year":2024,"url":"https://arxiv.org/abs/2402.19446","citing_submissions":128,"explicit_arxiv_matches":46,"examples":[{"id":"z9LtbKknbT","title":"Beyond Retrieval and Fine-tuning: Learning from Scientific Execution Experience for Self-Improving Agents","forum_url":"https://openreview.net/forum?id=z9LtbKknbT","excerpt":" Yong-Jin Liu, and Gao Huang. ExpeL: LLM agents are experiential learners. In AAAI Conference on Artificial Intelligence, 2024. Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, and Aviral Kumar. ArCHer: Training language model agents via hierarchical multi-turn RL. In Proceedings of the 41st International Conference on Machine Learning, 2024. Stage or object Table 2: Search and organization scale of the LKM pipeline. Count Meaning Initial domain seeds Expansio","arxiv_match":false},{"id":"iwLcGvjXHs","title":"Credit-Aware Memory Policy Optimization for Self-Evolving Black-Box Agents","forum_url":"https://openreview.net/forum?id=iwLcGvjXHs","excerpt":"ting and planning in language models. arXiv preprint arXiv:2310.04406, 2023. Huichi Zhou, Yihang Chen, Siyuan Guo, Xue Yan, Kin Hei Lee, Zihan Wang, Ka Yiu Lee, Guchun Zhang, Kun Shao, Linyi Yang, et al. Memento: Fine-tuning llm agents without fine-tuning llms, 2025. URL https://arxiv. org/abs/2508.16153. Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, and Aviral Kumar. Archer: Training language model agents via hierarchical multi-turn rl. arXiv preprint arXiv:2402.19446, 2024. Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Bryan Kian Hsiang ","arxiv_match":true},{"id":"XelnGzgYgI","title":"Stable Temporal Abstraction for Hierarchical Reinforcement Learning in Language Model Agents","forum_url":"https://openreview.net/forum?id=XelnGzgYgI","excerpt":"dings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7035–7053, 2026. Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, and Aviral Kumar. ArCHer: Training language model agents via hierarchical multi-turn RL. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 62178–62209. PMLR, 2024. URL https://proceedings.mlr.press/v235/z","arxiv_match":false}],"id":"2402.19446","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":786},{"title":"Ctrl-World: A Controllable Generative World Model for Robot Manipulation","arxiv_id":"2510.10125","year":2025,"url":"https://arxiv.org/abs/2510.10125","citing_submissions":128,"explicit_arxiv_matches":56,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"essing Systems, 2024. URL https://proceedings.neurips.cc/paper_files/paper/2024/hash/ cbe25fa0e7c7084049276888a09acc8d-Abstract-Conference.html. Yanjiang Guo, Lucy Shi, Jianyu Chen, and Chelsea Finn. Ctrl-World: A Controllable Generative World Model for Robot Manipulation. In International Conference on Learning Representations, pp. 6121–6138, 2026b. URL https://proceedings.iclr.cc/paper_files/paper/2026/hash/ 0ae94013da7cd459402fd77874e09ee3-Abstract-Conference.html.","arxiv_match":false},{"id":"rtlzkXdJe9","title":"PhysiCalWorld: In-Context Physics Calibration for World Models","forum_url":"https://openreview.net/forum?id=rtlzkXdJe9","excerpt":" Mo, Chen-Hsuan Lin, et al. Dreamdojo: A generalist robot world model from large-scale human videos. arXiv preprint arXiv:2602.06949, 2026. 1, 2 Yanjiang Guo, Lucy Shi, Jianyu Chen, and Chelsea Finn. Ctrl-world: A controllable generative world model for robot manipulation. In International Conference on Learning Representations, volume 2026, pp. 6121–6138, 2026. 2 Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson","arxiv_match":false},{"id":"xE1Rd5Luma","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","forum_url":"https://openreview.net/forum?id=xE1Rd5Luma","excerpt":"ey Levine. Unsupervised learning for physical interaction through video prediction. Advances in neural information processing systems, 29, 2016. Yanjiang Guo, Lucy Shi, Jianyu Chen, and Chelsea Finn. Ctrl-world: A controllable generative world model for robot manipulation. In International Conference on Learning Representations, volume 2026, pp. 6121–6138, 2026. Kyle B Hatch, Ashwin Balakrishna, Oier Mees, Suraj Nair, Seohong Park, Blake Wulfe, Masha Itkina, Benjamin ","arxiv_match":false}],"id":"2510.10125","metadata_source":"Cached arXiv API metadata","rank":787},{"title":"In-context autoencoder for context compression in a large language model","arxiv_id":"2307.06945","year":2023,"url":"https://arxiv.org/abs/2307.06945","citing_submissions":128,"explicit_arxiv_matches":49,"examples":[{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":"aoye Qu, Yu Cheng, and Yang Yang. Latentmem: Customizing latent memory for multi-agent systems. arXiv preprint arXiv:2602.03036, 2026. Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, and Furu Wei. In-context autoencoder for context compression in a large language model. In International Conference on Learning Representations, 2024. Google DeepMind. Gemini 3.8 flash: Our most intelligent flash model for long-horizon engineering and agents, 2026. URL https://ai.googl","arxiv_match":false},{"id":"N6ehRHGZfr","title":"STAMP: Learning Cross-Session Memory in Native Recurrent States","forum_url":"https://openreview.net/forum?id=N6ehRHGZfr","excerpt":"usion in question answering. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10), pp. 152–164, 2024. Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, and Furu Wei. In-context autoencoder for context compression in a large language model. In International Conference on Learning Representations, 2024. Paolo Glorioso, Quentin Anthony, Yury Tokpanov, James Whittington, Jonathan Pilault, Adam Ibrahim, and Beren Millidge. Zamba: A compact","arxiv_match":false},{"id":"WaEf3RXP7M","title":"Less Text, More Evidence: Static Pruning and the Coverage Paradox in Language Models","forum_url":"https://openreview.net/forum?id=WaEf3RXP7M","excerpt":"t of skim reading and navigation when reading hyperlinks on the web. PLOS ONE, 15(9):1–23, 09 2020. doi: 10.1371/journal.pone.0239134. Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, and Furu Wei. In-context autoencoder for context compression in a large language model. In International Conference on Learning Representations (ICLR), 2024. Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel. ImageNet-trained C","arxiv_match":false}],"id":"2307.06945","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":788},{"title":"JudgeBench: A benchmark for evaluating LLMbased judges","arxiv_id":"2410.12784","year":2024,"url":"https://arxiv.org/abs/2410.12784","citing_submissions":128,"explicit_arxiv_matches":35,"examples":[{"id":"M752QnEbXC","title":"CoPE: Co-Evolving Policy and Experience via Context Distillation for Open-Ended Generation","forum_url":"https://openreview.net/forum?id=M752QnEbXC","excerpt":"g. arXiv:2209.15189, 2022. Learning by distilling context. arXiv preprint Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Yuan Tang, Alejandro Cuadron, Chenguang Wang, Raluca Popa, and Ion Stoica. Judgebench: A benchmark for evaluating LLM-based judges. In The Thirteenth International Conference on Learning Representations, 2025. URL https: //openreview.net/forum?id=G0dksFayVq. Tiannan Wang, Jiamin Chen, Qingrui Jia, Shuai Wang, Ruoyu Fang, Huilin ","arxiv_match":false},{"id":"Be4m9EtEed","title":"Locally Sound, Globally Insufficient: The Local-Global Gap in Multi-Hop Reasoning","forum_url":"https://openreview.net/forum?id=Be4m9EtEed","excerpt":"ndings-emnlp.552. URL https://aclanthology.org/2024. findings-emnlp.552/. Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Yuan Tang, Alejandro Cuadron, Chenguang Wang, Raluca Popa, and Ion Stoica. JudgeBench: A Benchmark for Evaluating LLM-Based Judges. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=G0dksFayVq. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharw","arxiv_match":false},{"id":"m4Hk5im5oE","title":"Meta-MemGran: Rethinking Pixel-Level Visual Encoder via Meta-Memory for MLLMs","forum_url":"https://openreview.net/forum?id=m4Hk5im5oE","excerpt":" Tao, Huan Liu, Guanghua Gu, Baoyuan Wu, Yao Zhao, and Yunchao Wei. C2p-clip: Injecting category common prompt in clip to enhance generalization in deepfake detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 7184–7192, 2025. Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, and Ion Stoica. Judgebench: A benchmark for evaluating llm-based judges. arXiv preprint arXiv:2410.12784, 2024. Weijie Tu, Weijian Deng, and Tom Gedeon. Toward a holistic evaluation of robustness in clip ","arxiv_match":true}],"id":"2410.12784","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":789},{"title":"Advancing Open-source World Models","arxiv_id":"2601.20540","year":2026,"url":"https://arxiv.org/abs/2601.20540","citing_submissions":127,"explicit_arxiv_matches":123,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"26. URL https://huggingface.co/Qwen/Qwen3. 5-9B. Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3C: 3d-informed world-consistent video generation with precise camera control. arXiv preprint arXiv:2503.03751, 2025. URL https: //arxiv.org/abs/2503.03751. Robbyant Team, Zelin Gao, Qiuyu Wang, Yanhong Zeng, et al. Advancing open-source world models. arXiv preprint arXiv:2601.20540, 2026. Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Chert","arxiv_match":true},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"g. Motionstream: Real-time video generation with interactive motion controls. In International Conference on Learning Representations, 2026. Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. In International Conference on Machine Learning, 2023. Robbyant Team, Zelin Gao, Qiuyu Wang, Yanhong Zeng, Jiapeng Zhu, Ka Leong Cheng, Yixuan Li, Hanlin Wang, Yinghao Xu, Shuailei Ma, et al. Advancing open-source world models. arXiv preprint arXiv:2601.20540, 2026. Wan Team. Wan: Open and advanced large-scale video generative models. arXiv:2503.20314, 2025","arxiv_match":true},{"id":"3nSV2brMqb","title":"PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives","forum_url":"https://openreview.net/forum?id=3nSV2brMqb","excerpt":"Wang, Yinghao Xu, Shuailei Ma, Yihang Chen, Jie Liu, Yansong Cheng, Yao Yao, Jiayi Zhu, Yihao Meng, Kecheng Zheng, Qingyan Bai, Jingye Chen, Zehong Shen, Yue Yu, Xing Zhu, Yujun Shen, and Hao Ouyang. Advancing Open-source World Models, January 2026. Team Seedance, De Chen, Liyang Chen, Xin Chen, Ying Chen, Zhuo Chen, Zhuowei Chen, Feng Cheng, Tianheng Cheng, Yufeng Cheng, Mojie Chi, Xuyan Chi, Jian Cong, Qinpeng Cui, Fei Ding, Qid","arxiv_match":false}],"id":"2601.20540","metadata_source":"Cached arXiv API metadata","rank":790},{"title":"Diffusion models are real-time game engines","arxiv_id":"2408.14837","year":2024,"url":"https://arxiv.org/abs/2408.14837","citing_submissions":127,"explicit_arxiv_matches":41,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"n, Shuai Shao, Linfeng Zhang, and Qinglin Lu. Hunyuan-GameCraft-2: Instructionfollowing Interactive Game World Model. arXiv preprint arXiv:2511.23429, 2025. URL https: //arxiv.org/abs/2511.23429. Tencent Hunyuan. HunyuanVideo 1.5. https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5, 2025. Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. Diffusion Models Are Real-Time Game Engines. arXiv preprint arXiv:2408.14837, 2024. URL https://arxiv.org/abs/2408.14837. Wan Team, Ang Wang, Baole Ai, Bin Wen, et al. Wan: Open and advanced large-scale video generative ","arxiv_match":true},{"id":"9kxTLJwbxB","title":"TraceWorld: Trajectory-Indexed Memory for Spatially Consistent Video World Model","forum_url":"https://openreview.net/forum?id=9kxTLJwbxB","excerpt":" Gu, Siyu Hong, Jiachen Lei, et al. Dreamx-world 1.0: A general-purpose interactive world model. arXiv preprint arXiv:2606.16993, 2026. Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. Diffusion models are real-time game engines. In International Conference on Learning Representations (ICLR), volume 2025, pp. 73754–73776, 2025. Basile Van Hoorick, Rundi Wu, Ege Ozguroglu, Kyle Sargent, Ruoshi Liu, Pavel Tokmakov, Achal Dave,","arxiv_match":false},{"id":"GM9LePOwrX","title":"Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=GM9LePOwrX","excerpt":"oyu Yang, Xin Song, Xuan Hu, Yankai Zhang, and Yuqiao Li. MAGI-1: autoregressive video generation at scale. CoRR, abs/2505.13211, 2025. Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. Diffusion models are real-time game engines. In ICLR. OpenReview.net, 2025. Vikram Voleti, Alexia Jolicoeur-Martineau, and Chris Pal. MCVD - masked conditional video diffusion for prediction, generation, and interpolation. In NeurIPS, 2022. An","arxiv_match":false}],"id":"2408.14837","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":791},{"title":"Fast model editing at scale","arxiv_id":"2110.11309","year":2021,"url":"https://arxiv.org/abs/2110.11309","citing_submissions":127,"explicit_arxiv_matches":46,"examples":[{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":"cial model card, 2024. URL https://github.com/meta-l lama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md. Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning. Fast model editing at scale. In ICLR, 2022a. Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, and Chelsea Finn. Memorybased model editing at scale. In ICML, 2022b. OpenAI. Sycophancy in GPT-4o: What happene","arxiv_match":false},{"id":"JXRY0RDZSR","title":"EngramEdit: Decoupled Knowledge Updates in LLMs through Conditional Memory","forum_url":"https://openreview.net/forum?id=JXRY0RDZSR","excerpt":"ss-editing memory in a transformer. In The Eleventh International Conference on Learning Representations, 2023. Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning. Fast model editing at scale. In International Conference on Learning Representations, 2022a. Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn. Memorybased model editing at scale. In Proceedi","arxiv_match":false},{"id":"oauW9dlXcI","title":"Generalizable Model Editing Could Be More Than Direct Generalization","forum_url":"https://openreview.net/forum?id=oauW9dlXcI","excerpt":"ass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations, 2023. Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. Fast model editing at scale. In International Conference on Learning Representations, 2022. URL https://openreview.net/pdf?id=0DcZxeWfOPt. OpenAI. Gpt-4 technical report. arXiv, pp. 2303–08774, 2023. Rahul Ramesh, Ekdeep Singh ","arxiv_match":false}],"id":"2110.11309","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":792},{"title":"Large language models are not robust multiple choice selectors","arxiv_id":"2309.03882","year":2023,"url":"https://arxiv.org/abs/2309.03882","citing_submissions":127,"explicit_arxiv_matches":36,"examples":[{"id":"g0vDUvCHlP","title":"Video-Index: A Curated Meta-Benchmark for Video Understanding Evaluation","forum_url":"https://openreview.net/forum?id=g0vDUvCHlP","excerpt":" in a video haystack: A scalable synthetic evaluator for video MLLMs. In International Conference on Learning Representations, 2025d. Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. Large language models are not robust multiple choice selectors. In International Conference on Learning Representations, volume 2024, pp. 19426–19454, 2024a. Haizhong Zheng, Rui Liu, Fan Lai, and Atul Prakash. Coverage-centric coreset selection for high pruning ","arxiv_match":false},{"id":"Xn3b8YgKIg","title":"Delegated-Authority Underuse in Large Language Models","forum_url":"https://openreview.net/forum?id=Xn3b8YgKIg","excerpt":"oceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 24669–24697, 2025. Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. Large language models are not robust multiple choice selectors. In International Conference on Learning Representations, volume 2024, pp. 19426–19454, 2024. Jingting Zheng, Yuqi Ren, Linhao Yu, Yongqi Leng, and Deyi Xiong. Beyond value benchmarks: Measuring valu","arxiv_match":false},{"id":"n9DEtZxswi","title":"The Hidden Role of Candidate-Pool Provenance in Selector Evaluation","forum_url":"https://openreview.net/forum?id=n9DEtZxswi","excerpt":"gs of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). ACM, 2024. doi: 10.1145/3650212.3680384. Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. Large language models are not robust multiple choice selectors. In The Twelfth International Conference on Learning Representations (ICLR), 2024. URL https://openreview.net/forum?id=shr9PXz7T0. Jian Zhou, Hongyu Zhang, and David Lo. Where should the bugs be fixe","arxiv_match":false}],"id":"2309.03882","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":793},{"title":"Or-bench: An over-refusal benchmark for large language models","arxiv_id":"2405.20947","year":2024,"url":"https://arxiv.org/abs/2405.20947","citing_submissions":127,"explicit_arxiv_matches":58,"examples":[{"id":"yECLVPwR3R","title":"Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation","forum_url":"https://openreview.net/forum?id=yECLVPwR3R","excerpt":"sociation for Computational Linguistics, 2024. doi: 10.18653/v1/2024.findings-acl.340. URL https://aclanthology. org/2024.findings-acl.340/. Justin Cui, Wei-Lin Chiang, Ion Stoica, and Cho-Jui Hsieh. OR-Bench: An Over-Refusal Benchmark for Large Language Models. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 11515–11542. PMLR, 2025. URL https://proceedings.mlr.press/v267/c","arxiv_match":false},{"id":"I35nG4HR52","title":"When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making","forum_url":"https://openreview.net/forum?id=I35nG4HR52","excerpt":"hen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, et al. Reasoning models don’t always say what they think. arXiv preprint arXiv:2505.05410, 2025. Justin Cui, Wei-Lin Chiang, Ion Stoica, and Cho-Jui Hsieh. Or-bench: An over-refusal benchmark for large language models. In Proceedings of the 42nd International Conference on Machine Learning, 2025. URL https://arxiv.org/abs/2405.20947. Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yan","arxiv_match":true},{"id":"UgRMbBbm5R","title":"IntentEdit: Intent-Level Lifelong Editing for VLM Jailbreak Defense","forum_url":"https://openreview.net/forum?id=UgRMbBbm5R","excerpt":"preprint arXiv:2308.14132, 2023. Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, and Ajay Divakaran. Dress: Instructing large vision-language models to align and interact with humans via natural language feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14239–14250, 2024. Justin Cui, Wei-Lin Chiang, Ion Stoica, and Cho-Jui Hsieh. Or-bench: An over-refusal benchmark for large language models. arXiv preprint arXiv:2405.20947, 2024. Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing. Multilingual jailbreak challenge","arxiv_match":true}],"id":"2405.20947","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":794},{"title":"Step1X-Edit: A Practical Framework for General Image Editing","arxiv_id":"2504.17761","year":2025,"url":"https://arxiv.org/abs/2504.17761","citing_submissions":127,"explicit_arxiv_matches":121,"examples":[{"id":"BGtau6E80m","title":"Image Editing without Typing","forum_url":"https://openreview.net/forum?id=BGtau6E80m","excerpt":"mulation. https://github.com/boriskravtsov/h and-drawn-shape-simulation, 2025. Accessed: 2026-09-22. Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, Guopeng Li, Yuang Peng, Quan Sun, Jingwei Wu, Yan Cai, Zheng Ge, Ranchen Ming, Lei Xia, Xianfang Zeng, Yibo Zhu, Binxing Jiao, Xiangyu Zhang, Gang Yu, and Daxin Jiang. Step1X-Edit: A Practical Framework for General Image Editing. arXiv preprint arXiv:2504.17761, 2025. Meituan LongCat Team, Hanghang Ma, Haoxian Tan, Jiale Huang, Junqiang Wu, Jun-Yan He, Lishua","arxiv_match":true},{"id":"7x4AAlYBDM","title":"Stabilizing Multi-Step Diffusion Distillation with Student Perturbation Consistency","forum_url":"https://openreview.net/forum?id=7x4AAlYBDM","excerpt":"Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. In ICLR, 2023. Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, Guopeng Li, Yuang Peng, Quan Sun, Jingwei Wu, Yan Cai, Zheng Ge, Ranchen Ming, Lei Xia, Xianfang Zeng, Yibo Zhu, Binxing Jiao, Xiangyu Zhang, Gang Yu, and Daxin Jiang. Step1x-edit: A practical framework for general image editing. arXiv preprint arXiv:2504.17761, 2025. Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and","arxiv_match":true},{"id":"IdwA2KX2G3","title":"StateWise: From Noise Awareness to State-Calibrated MLLM Conditioning for Unified Generation","forum_url":"https://openreview.net/forum?id=IdwA2KX2G3","excerpt":"rXiv preprint arXiv:2506.03147, 2025a. Han Lin, Jaemin Cho, Amir Zadeh, Chuan Li, and Mohit Bansal. Bifrost-1: Bridging multimodal llms and diffusion models with patch-level clip latents. Advances in Neural Information Processing Systems, 38:69700–69725, 2025b. Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, et al. Step1x-edit: A practical framework for general image editing. arXiv preprint arXiv:2504.17761, 2025. Zeyu Liu, Zanlin Ni, Yang Yue, Cheng Da, Huan Yang, Di Zhang, Kun Gai, and Gao Huang. Steeri","arxiv_match":true}],"id":"2504.17761","metadata_source":"Cached arXiv API metadata","rank":795},{"title":"AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning","arxiv_id":"2506.13757","year":2025,"url":"https://arxiv.org/abs/2506.13757","citing_submissions":126,"explicit_arxiv_matches":39,"examples":[{"id":"cbEkVPsGSJ","title":"DriveBeyond: Rethinking Group Advantage for Driving Policy Optimization","forum_url":"https://openreview.net/forum?id=cbEkVPsGSJ","excerpt":" DAPO: An open-source LLM reinforcement learning system at scale. In NeurIPS, volume 38, pp. 113222–113244, 2025. Zewei Zhou, Tianhui Cai, Seth Zhao, Yun Zhang, Zhiyu Huang, Bolei Zhou, and Jiaqi Ma. AutoVLA: A vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine-tuning. In NeurIPS, volume 38, pp. 27920–27956, 2025. Zewei Zhou, Ruining Yang, Xuewei Qi, Yiluan Guo, Sherry X Chen, Tao Feng, Kateryna Pistunova, Yishan Shen, Lili Su, Jiaqi Ma, et al. SpanVLA: Efficient ","arxiv_match":false},{"id":"4mnt3eA9ir","title":"ActionGuide: Learning Transferable Action-Grounded Reasoning for Closed-Loop Driving","forum_url":"https://openreview.net/forum?id=4mnt3eA9ir","excerpt":"ion model for autonomous driving. arXiv preprint arXiv:2609.00111, 2026. URL https://arxiv.org/abs/2609.00111. Zewei Zhou, Tianhui Cai, Seth Z. Zhao, Yun Zhang, Zhiyu Huang, Bolei Zhou, and Jiaqi Ma. AutoVLA: A vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine-tuning. In Advances in Neural Information Processing Systems, 2025. URL https://papers.nips.cc/paper_files/paper/2025/hash/ 2843fccca5bedd369a4764848b9bd546-Abstract-Conference.html. ","arxiv_match":false},{"id":"8ytqtZr2QM","title":"NavSafe-$\\infty$: Benchmarking Closed-Loop Driving Safety in Photorealistic Environments","forum_url":"https://openreview.net/forum?id=8ytqtZr2QM","excerpt":"ctions on Pattern Analysis and Machine Intelligence, 48(4): 4673–4691, 2026a. doi: 10.1109/TPAMI.2025.3647952. Zewei Zhou, Tianhui Cai, Seth Z. Zhao, Yun Zhang, Zhiyu Huang, Bolei Zhou, and Jiaqi Ma. AutoVLA: A vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine-tuning. In D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, and N. Chen (eds.), Advances in Neural Information Processing Systems, volume 38, pp. 27920–27956. Curran Associates, Inc., 202","arxiv_match":false}],"id":"2506.13757","metadata_source":"Cached arXiv API metadata","rank":796},{"title":"Transformers are sample-efficient world models","arxiv_id":"2209.00588","year":2022,"url":"https://arxiv.org/abs/2209.00588","citing_submissions":126,"explicit_arxiv_matches":47,"examples":[{"id":"xjw6eZxgow","title":"When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning","forum_url":"https://openreview.net/forum?id=xjw6eZxgow","excerpt":"ble end-to-end joint-embedding predictive architecture from pixels. arXiv preprint arXiv:2603.19312, 2026b. URL https://arxiv.org/abs/2603.19312. Vincent Micheli, Eloi Alonso, and Fran ccois Fleuret. Transformers are sample-efficient world models. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=vhFu1Acb0xb. Vivek Myers, Chongyi Zheng, Anca Dragan, Sergey Levine, and Benjamin Eysenbach. Learni","arxiv_match":false},{"id":"YAg8ELQnSW","title":"Parallel Predictive World Models for Accurate and Efficient Long-Horizon Planning","forum_url":"https://openreview.net/forum?id=YAg8ELQnSW","excerpt":"ng, and Sergey Levine. When to trust your model: Model-based policy optimization. Advances in neural information processing systems, 32, 2019. Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, and Randall Balestriero. Leworldmodel: Stable end-to-end joint-embedding predictive architecture from pixels. arXiv preprint arXiv:2603.19312, 2026. Vincent Micheli, Eloi Alonso, and François Fleuret. Transformers are sample-efficient world models. arXiv preprint arXiv:2209.00588, 2022. Nikhil Mishra, Pieter Abbeel, and Igor Mordatch. Prediction and control with temporal segmen","arxiv_match":true},{"id":"bUZRHzie4Z","title":"LensDreamer: Exploring Optical Design Basins via World-Model Imagination","forum_url":"https://openreview.net/forum?id=bUZRHzie4Z","excerpt":"rpusID:53592270. Joseph Meiron. Automatic lens design by the least squares method. Journal of the Optical Society of America, 49(3):293–298, 1959. Vincent Micheli, Eloi Alonso, and François Fleuret. Transformers are sample-efficient world models. In International Conference on Learning Representations, 2023. URL https://openreview. net/forum?id=vhFu1Acb0xb. Naoki Morihira, Amal Nahar, Kartik Bharadwaj, Yasuhiro Kato, Akinobu Hayashi, and Tat","arxiv_match":false}],"id":"2209.00588","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":797},{"title":"A gentle introduction to conformal prediction and distribution-free uncertainty quantification","arxiv_id":"2107.07511","year":2021,"url":"https://arxiv.org/abs/2107.07511","citing_submissions":125,"explicit_arxiv_matches":106,"examples":[{"id":"EUwi8JmVHj","title":"Evaluating Vision-Language Models on Images of Individuals with Limb Deficiencies","forum_url":"https://openreview.net/forum?id=EUwi8JmVHj","excerpt":" Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele. 2d human pose estimation: New benchmark and state of the art analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2014. Anastasios N. Angelopoulos and Stephen Bates. A gentle introduction to conformal prediction and distribution-free uncertainty quantification. arXiv preprint arXiv:2107.07511, 2021. Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Z","arxiv_match":true},{"id":"hj4i75KSaF","title":"FS-Cert-U: Risk Certification Before Utility Optimization for Non-Monotonic Two-Stage Model Routing","forum_url":"https://openreview.net/forum?id=hj4i75KSaF","excerpt":" Anastasios N. Angelopoulos. Conformal risk control for non-monotonic losses. arXiv preprint arXiv:2602.20151, 2026. URL https://arxiv.org/abs/2602.20151. Anastasios N. Angelopoulos and Stephen Bates. A gentle introduction to conformal prediction and distributionfree uncertainty quantification. arXiv preprint arXiv:2107.07511, 2021. URL https://arxiv.org/ abs/2107.07511. Anastasios N. Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, and Tal Schuster. Conformal risk","arxiv_match":true},{"id":"QGMsQCSLE9","title":"Variance-Reduced Conformal Prediction via Shrinkage for Long-Tailed Recognition","forum_url":"https://openreview.net/forum?id=QGMsQCSLE9","excerpt":" Anastasios Angelopoulos, Stephen Bates, Jitendra Malik, and Michael I Jordan. Uncertainty sets for image classifiers using conformal prediction. In International Conference on Learning Representations, 2021. Anastasios N Angelopoulos and Stephen Bates. A gentle introduction to conformal prediction and distribution-free uncertainty quantification. arXiv preprint arXiv:2107.07511, 2021. Rina Foygel Barber, Emmanuel J Candès, Aaditya Ramdas, and Ryan J Tibshirani. The limits of ","arxiv_match":true}],"id":"2107.07511","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":798},{"title":"Agent Laboratory: Using LLM Agents as Research Assistants","arxiv_id":"2501.04227","year":2025,"url":"https://arxiv.org/abs/2501.04227","citing_submissions":125,"explicit_arxiv_matches":33,"examples":[{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":" tools. In Advances in Neural Information Processing Systems, volume 36, pp. 68539–68551, 2023. URL https://proceedings.neurips.cc/paper/2023/ hash/d842425e4bf79ba039352da0f658a906-Abstract-Conference.html. Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, and Emad Barsoum. Agent laboratory: Using LLM agents as research assistants. arXiv preprint arXiv:2501.04227, 2025. URL https://arxiv.org/abs/ 2501.04227. Timothy M. Schmidl and Donald C. Cox. Robust frequency and timing synchronization for OFDM. IEEE T","arxiv_match":true},{"id":"Xq1WyguERb","title":"Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery","forum_url":"https://openreview.net/forum?id=Xq1WyguERb","excerpt":"s from program search with large language models. In Nature, 2024. Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, and Emad Barsoum. Agent laboratory: Using llm agents as research assistants. EMNLP (Findings), 2025. Mohamed Shamseldein. Grid-mind: An LLM-orchestrated multi-fidelity agent for automated connection impact assessment. arXiv preprint arXiv:2602.20683, 2026. Chao Shen, Zihan G","arxiv_match":false},{"id":"ZIVFDKAh8p","title":"Automated Algorithm Discovery through Mathematical Reformulation: A Study in Visual-Token Reduction","forum_url":"https://openreview.net/forum?id=ZIVFDKAh8p","excerpt":"tical discoveries from program search with large language models. Nature, 625:468–475, 2024. doi: 10.1038/ s41586-023-06924-6. URL https://doi.org/10.1038/s41586-023-06924-6. Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, and Emad Barsoum. Agent laboratory: Using LLM agents as research assistants. arXiv preprint arXiv:2501.04227, 2025. doi: 10.48550/arXiv.2501.04227. URL https://arxiv.org/abs/2501.04227. Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and ","arxiv_match":true}],"id":"2501.04227","metadata_source":"Cached arXiv API metadata","rank":799},{"title":"Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation","arxiv_id":"2411.19650","year":2024,"url":"https://arxiv.org/abs/2411.19650","citing_submissions":125,"explicit_arxiv_matches":120,"examples":[{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"601.16163, 2026. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, et al. Causal world modeling for robot control. arXiv preprint arXiv:2601.21998, 2026. Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, et al. Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation. arXiv preprint arXiv:2411.19650, 2024. Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Si ","arxiv_match":true},{"id":"eANQWkH13Z","title":"PriGo: Primitive Guidance to Diffusion and Flow Policies for Adaptive Robotic Manipulation","forum_url":"https://openreview.net/forum?id=eANQWkH13Z","excerpt":"ce on machine learning, pp. 19730–19742. PMLR, 2023. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, et al. Causal world modeling for robot control. arXiv preprint arXiv:2601.21998, 2026. Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, et al. Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation. arXiv:2411.19650, 2024. Xuanlin Li, Kyle Hsu, Jiayuan Gu, Oier Mees, Karl Pertsch, Homer Rich Walke, Chuyuan Fu, Ish","arxiv_match":true},{"id":"Y8naMKuOst","title":"TARGET-VLA: Mask-Guided Visual Retrieval for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=Y8naMKuOst","excerpt":"ls. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 19730–19742. PMLR, 2023. URL https://proceedings.mlr.press/ v202/li23q.html. Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, et al. Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation. arXiv preprint arXiv:2411.19650, 2024a. Xiaoqi Li, Liang Heng, Jiaming Liu, Yan Shen, Chenyang Gu, Zhuoyang Liu, Hao Chen, Nuowei H","arxiv_match":true}],"id":"2411.19650","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":800},{"title":"Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free","arxiv_id":"2505.06708","year":2025,"url":"https://arxiv.org/abs/2505.06708","citing_submissions":125,"explicit_arxiv_matches":41,"examples":[{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"75, 2026. NVIDIA. Cosmos 3: Omnimodal World Models for Physical AI. arXiv preprint arXiv:2606.02800, 2026. William Peebles and Saining Xie. Scalable Diffusion Models with Transformers. In ICCV, 2023. Zihan Qiu, Zekun Wang, Bo Zheng, Zeyu Huang, Kaiyue Wen, Songlin Yang, Rui Men, Le Yu, Fei Huang, Suozhi Huang, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free. arXiv preprint arXiv:2505.06708, 2025. Qwen Team. Qwen3-Next: Towards Ultimate Training & Inference Efficiency. Qwen Team blog, htt","arxiv_match":true},{"id":"w3eqY0WKjo","title":"Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head","forum_url":"https://openreview.net/forum?id=w3eqY0WKjo","excerpt":"pi.semanticscholar.org/CorpusID:278237209. Zihan Qiu, Zekun Wang, Bo Zheng, Zeyu Huang, Kaiyue Wen, Songlin Yang, Rui Men, Le Yu, Fei Huang, Suozhi Huang, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free. ArXiv, abs/2505.06708, 2025. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande. Communications of the ACM, 64:99 – 106, 2019. Noam M. Shazeer, Azalia Mirhoseini, Krzy","arxiv_match":false},{"id":"KwJvnZFdu6","title":"Grounding Visual Evidence for Long-Range Language-Conditioned Navigation","forum_url":"https://openreview.net/forum?id=KwJvnZFdu6","excerpt":", Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. Zihan Qiu, Zekun Wang, Bo Zheng, Zeyu Huang, Kaiyue Wen, Songlin Yang, Rui Men, Le Yu, Fei Huang, Suozhi Huang, et al. Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free. arXiv preprint arXiv:2505.06708, 2025. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Gi","arxiv_match":true}],"id":"2505.06708","metadata_source":"Cached arXiv API metadata","rank":801},{"title":"LightRAG: Simple and fast retrievalaugmented generation","arxiv_id":"2410.05779","year":2024,"url":"https://arxiv.org/abs/2410.05779","citing_submissions":125,"explicit_arxiv_matches":36,"examples":[{"id":"BcOYpbPnS1","title":"What Retrieval Buys Depends on Who Reads: Splitting Multi-Hop RAG Accuracy into Assembly and Reading","forum_url":"https://openreview.net/forum?id=BcOYpbPnS1","excerpt":"ss, and Jonathan Larson. From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024. Zirui Guo, Lianghao Xia, Yanhua Yu, Tian Ao, and Chao Huang. Lightrag: Simple and fast retrievalaugmented generation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pp. 10746–10761, 2025. Bernal J Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically in","arxiv_match":false},{"id":"xGbelToSJh","title":"Cued Recall: Making Long-Term Memory Accessible with Relationally Conditioned Cues","forum_url":"https://openreview.net/forum?id=xGbelToSJh","excerpt":". In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1762–1777, 2023. Zirui Guo, Lianghao Xia, Yanhua Yu, Tian Ao, and Chao Huang. Lightrag: Simple and fast retrievalaugmented generation. In EMNLP (Findings), pp. 10746–10761, 2025. Bernal J Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspired long-term memory for large language models. Ad","arxiv_match":false},{"id":"1cNggKmyeh","title":"DeferMem: Query-Time Evidence Distillation via Reinforcement Learning for Long-Term Agent Memory","forum_url":"https://openreview.net/forum?id=1cNggKmyeh","excerpt":"h reinforcement learning. Nature, 645(8081):633–638, 2025a. doi: 10.1038/s41586-025-09422-z. URL https://doi.org/10.1038/s41586-025-09422-z. Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. LightRAG: Simple and fast retrieval-augmented generation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pp. 10746–10761, Suzhou, China, 2025b. Association for Computational Linguistics. doi: 10.18653/v1/2025.findings-emnlp.568. ","arxiv_match":false}],"id":"2410.05779","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":802},{"title":"Matterport3d: Learning from rgb-d data in indoor environments","arxiv_id":"1709.06158","year":2017,"url":"https://arxiv.org/abs/1709.06158","citing_submissions":125,"explicit_arxiv_matches":53,"examples":[{"id":"ukP6owZeZe","title":"SphereBridge: Single-Panorama 3D Gaussian Reconstruction for High-Resolution Real-Time Multi-Sphere Rendering","forum_url":"https://openreview.net/forum?id=ukP6owZeZe","excerpt":"DoF video view synthesis using multi-sphere images. In ECCV, 2020. Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3D: Learning from RGB-D data in indoor environments. In 3DV, 2017. Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, TatJen Cham, and Jianfei Cai. MVSplat: Efficient 3D Gaussian splatting from sparse multi-view ima","arxiv_match":false},{"id":"TA0thWBRuQ","title":"ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence","forum_url":"https://openreview.net/forum?id=TA0thWBRuQ","excerpt":"achine Learning, 28(1):41–75, 1997. doi: 10.1023/A: 1007379606734. Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3D: Learning from RGB-D data in indoor environments. In International Conference on 3D Vision, pp. 667–676. IEEE, 2017. Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. SpatialVLM: Endowing vision-language ","arxiv_match":false},{"id":"PjUHgdvzhE","title":"A Recursive Self-Improving Harness for Training-Free Generalist Navigation","forum_url":"https://openreview.net/forum?id=PjUHgdvzhE","excerpt":"on Robotics, 37(6):1874–1890, 2021. doi: 10.1109/TRO.2021.3075644. Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3D: Learning from RGB-D data in indoor environments. In 2017 International Conference on 3D Vision (3DV), pp. 667–676, 2017. Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. Spatialvlm: Endowing vision-lang","arxiv_match":false}],"id":"1709.06158","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":803},{"title":"MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory","arxiv_id":"2601.03192","year":2026,"url":"https://arxiv.org/abs/2601.03192","citing_submissions":125,"explicit_arxiv_matches":117,"examples":[{"id":"y5X5ZaufZQ","title":"Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections","forum_url":"https://openreview.net/forum?id=y5X5ZaufZQ","excerpt":"rum? id=WE_vluYUL-X. Kaiyuan Zhang, Mark Tenenholtz, Kyle Polley, Jerry Ma, Denis Yarats, and Ninghui Li. Browsesafe: Understanding and preventing prompt injection within ai browser agents, 2025. URL https: //arxiv.org/abs/2511.20597. Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou, Junwei Liao, Yuchen Feng, Zhuo Li, Yujie Zheng, Weinan Zhang, Ying Wen, Zhiyu Li, et al. Memrl: Self-evolving agents via runtime reinforcement learning on episodic memory. arXiv preprint arXiv:2601.03192, 2026. Yucheng Zhang, Qinfeng Li, Tianyu Du, Xuhong Zhang, Xinkui Zhao, Zhengwen Feng, and Jianwei ","arxiv_match":true},{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":" Xiang, Zhitao Wang, Caiyin Yang, Yixi Ouyang, Bang Liu, Chenglin Wu, and Yuyu Luo. Harnessing Agentic Evolution, 2026c. URL https://arxiv.org/abs/2605.13821. arXiv preprint. Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou, Junwei Liao, Yuchen Feng, Zhuo Li, Yujie Zheng, Weinan Zhang, Ying Wen, Zhiyu Li, Feiyu Xiong, Yutao Qi, Bo Tang, and Muning Wen. MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory, 2026d. URL https://arxiv.org/abs/2601.03192. arXiv preprint. Yifan Zhang and Team Math-AI. American invitational mathematics examination (aime)","arxiv_match":true},{"id":"D0cLnmHjUF","title":"Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction","forum_url":"https://openreview.net/forum?id=D0cLnmHjUF","excerpt":"ru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, et al. Agentic context engineering: Evolving contexts for self-improving language models. In International Conference on Learning Representations, volume 2026, pp. 86069–86100, 2026a. Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou, Junwei Liao, Yuchen Feng, Zhuo Li, Yujie Zheng, Weinan Zhang, Ying Wen, Zhiyu Li, et al. Memrl: Self-evolving agents via runtime reinforcement learning on episodic memory. arXiv preprint arXiv:2601.03192, 2026b. Xing Zhang, Guanghui Wang, Yanwei Cui, Wei Qiu, Ziyuan Li, Bing Zhu, and Peiyang He. Experi","arxiv_match":true}],"id":"2601.03192","metadata_source":"Cached arXiv API metadata","rank":804},{"title":"OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?","arxiv_id":"2501.05510","year":2025,"url":"https://arxiv.org/abs/2501.05510","citing_submissions":125,"explicit_arxiv_matches":19,"examples":[{"id":"tSF2DDsC2y","title":"ALIVE: Making Streaming Video Models Interactive","forum_url":"https://openreview.net/forum?id=tSF2DDsC2y","excerpt":" Qingyu Zhang, Yansong Shi, et al. Videochat3: Fully open video mllm for efficient and generalist video understanding. arXiv preprint arXiv:2607.14935, 2026b. Yifei Li, Junbo Niu, Ziyang Miao, Chunjiang Ge, Yuanhang Zhou, Qihao He, Xiaoyi Dong, Haodong Duan, Shuangrui Ding, Rui Qian, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, and Jiaqi Wang. Ovo-bench: How far is your video-llms from real-world online video understanding?, 2025. URL https://arxiv.org/abs/2501.05510. Shuo Liang, Yiwu Zhong, Zi-Yuan Hu, Yeyao Tao, and Liwei Wang. Fine-grained spatiotemporal groundi","arxiv_match":true},{"id":"Ps7rUvLZGb","title":"MetricKV: Decoded-Space Geometry for Visual KV Compression in Streaming Video Understanding","forum_url":"https://openreview.net/forum?id=Ps7rUvLZGb","excerpt":" quantization for kv cache. arXiv preprint arXiv:2402.02750, 2024. Junbo Niu, Yifei Li, Ziyang Miao, Chunjiang Ge, Yuanhang Zhou, Qihao He, Xiaoyi Dong, Haodong Duan, Shuangrui Ding, Rui Qian, et al. Ovo-bench: How far is your video-llms from real-world online video understanding? In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18902–18913. IEEE, 2025. Rui Qian, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Shuangrui Ding, Dahua Lin, and Jiaqi Wang. ","arxiv_match":false},{"id":"KY3xZQ7lP8","title":"ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding","forum_url":"https://openreview.net/forum?id=KY3xZQ7lP8","excerpt":"271719914. Yifei Li, Junbo Niu, Ziyang Miao, Chunjiang Ge, Yuanhang Zhou, Qi He, Xiao wen Dong, Haodong Duan, Shuangrui Ding, Rui Qian, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, and Jiaqi Wang. Ovo-bench: How far is your video-llms from real-world online video understanding? 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18902–18913, 2025. URL https://api.semanticscholar.org/CorpusID:275458756. Yucheng Li, Huiqiang Jiang, Qianhui Wu, Xuf","arxiv_match":false}],"id":"2501.05510","metadata_source":"Cached arXiv API metadata","rank":805},{"title":"Scaling relationship on learning mathematical reasoning with large language models","arxiv_id":"2308.01825","year":2023,"url":"https://arxiv.org/abs/2308.01825","citing_submissions":125,"explicit_arxiv_matches":114,"examples":[{"id":"50kJuTajGS","title":"ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation","forum_url":"https://openreview.net/forum?id=50kJuTajGS","excerpt":"arXiv preprint arXiv:2603.16856, 2026. Siyu Yuan, Zehui Chen, Zhiheng Xi, Junjie Ye, Zhengyin Du, and Jiecao Chen. Agent-r: Training language model agents to reflect via iterative self-training, 2025. URL https://arxiv.org/ abs/2501.11425. Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Keming Lu, Chuanqi Tan, Chang Zhou, and Jingren Zhou. Scaling relationship on learning mathematical reasoning with large language models, 2023. URL https://arxiv.org/abs/2308.01825. Jiwen Zhang, Jihao Wu, Teng Yihua, Minghui Liao, Nuo Xu, Xiao Xiao, Zhongyu Wei, and Duyu Tang. An","arxiv_match":true},{"id":"dHl5b1GjT0","title":"ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience","forum_url":"https://openreview.net/forum?id=dHl5b1GjT0","excerpt":"frey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR), 2023. Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Keming Lu, Chuanqi Tan, Chang Zhou, and Jingren Zhou. Scaling Relationship on Learning Mathematical Reasoning with Large Language Models. arXiv preprint arXiv:2308.01825, 2023. URL https://arxiv.org/abs/ 2308.01825. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Pan Lu, Zhi Huang, Carlos Guestrin, an","arxiv_match":true},{"id":"vTUJnLXc91","title":"Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation","forum_url":"https://openreview.net/forum?id=vTUJnLXc91","excerpt":" 2025. URL https://openreview. net/forum?id=2a36EMSSTp. Yufeng Yuan, Yu Yue, Ruofei Zhu, Tiantian Fan, and Lin Yan. What’s behind ppo’s collapse in long-cot? value optimization holds the secret, 2025. URL https://arxiv.org/abs/2503. 01491. Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Keming Lu, Chuanqi Tan, Chang Zhou, and Jingren Zhou. Scaling relationship on learning mathematical reasoning with large language models, 2023. URL https://arxiv.org/abs/2308.01825. Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Yang Yue, Shiji Song, and Gao Huang. Does","arxiv_match":true}],"id":"2308.01825","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":806},{"title":"Unified Video Action Model","arxiv_id":"2503.00200","year":2025,"url":"https://arxiv.org/abs/2503.00200","citing_submissions":125,"explicit_arxiv_matches":60,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"ujun Shen, and Yinghao Xu. Causal world modeling for robot control. arXiv preprint arXiv:2601.21998, 2026a. URL https://arxiv.org/abs/2601.21998. Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model. In Proceedings of Robotics: Science and Systems, 2025. doi: 10.15607/RSS.2025.XXI.074. URL https://www.roboticsproceedings.org/rss21/p074.html. Ying Li, Xiaobao Wei, Jiajun Cao, Hao Wang, Xiaowei Ch","arxiv_match":false},{"id":"Bd1yTJg9o3","title":"Iterative Self-Improvement with World-Expectation Optimization for World-Action Model","forum_url":"https://openreview.net/forum?id=Bd1yTJg9o3","excerpt":" Jiale Yu, et al. SimpleVLA-RL: Scaling VLA training via reinforcement learning, 2025a. URL https://arxiv.org/abs/2509.09674. Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, Yujun Shen, and Yinghao Xu. Causal world modeling for robot control, 2026. URL https://arxiv.org/abs/2601.21998. Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model, 2025b. URL https://arxiv.org/abs/2503.00200. Junbang Liang, Pavel Tokmakov, Ruoshi Liu, Sruthi Sudhakar, Paarth Shah, Rares Ambrus, and Carl Vo","arxiv_match":true},{"id":"hjPoL9WGRc","title":"AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents","forum_url":"https://openreview.net/forum?id=hjPoL9WGRc","excerpt":"un Shen, and Yinghao Xu. Causal world modeling for robot control. In Robotics: Science and Systems (RSS), 2026. doi: 10.15607/RSS.2 026.XXII.016. Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model. In Robotics: Science and Systems (RSS), 2025. doi: 10.15607/RSS.2025.XXI.074. Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet. Learning laten","arxiv_match":false}],"id":"2503.00200","metadata_source":"Cached arXiv API metadata","rank":807},{"title":"WorldMem: Long-term Consistent World Simulation with Memory","arxiv_id":"2504.12369","year":2025,"url":"https://arxiv.org/abs/2504.12369","citing_submissions":125,"explicit_arxiv_matches":41,"examples":[{"id":"eg3Sq9zM6j","title":"World Agent: Can Language Models Keep a World Running?","forum_url":"https://openreview.net/forum?id=eg3Sq9zM6j","excerpt":"ring: A set of prerequisite toy tasks. In International Conference on Learning Representations (ICLR), 2016. Zeqi Xiao, Yushi Lan, Yifan Zhou, Wenqi Ouyang, Shuai Yang, Yanhong Zeng, and Xingang Pan. WorldMem: Long-term consistent world simulation with memory. In Advances in Neural Information Processing Systems, volume 38, pp. 55348–55368, 2025. doi: 10.52202/085713-1659. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, T","arxiv_match":false},{"id":"7DrkJNzqyF","title":"LongLive-RAG: A General Retrieval-Augmented Framework for Long Video Generation","forum_url":"https://openreview.net/forum?id=7DrkJNzqyF","excerpt":" memory. arXiv preprint arXiv:2602.02393, 2026. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=NG7sS51zVF. Zeqi Xiao, Yushi Lan, Yifan Zhou, Wenqi Ouyang, Shuai Yang, Yanhong Zeng, and Xingang Pan. Worldmem: Long-term consistent world simulation with memory. arXiv preprint arXiv:2504.12369, 2025. Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation us","arxiv_match":true},{"id":"hZ6pYxr69u","title":"BoxWorld: Let World Engines Control and Video Generators Render","forum_url":"https://openreview.net/forum?id=hZ6pYxr69u","excerpt":"//proceedings.neurips.cc/paper_files/paper/ 2024/file/7dbb5bfab324e3b86af9bd0df15498dd-Paper-Conference.pdf. Zeqi Xiao, Yushi Lan, Yifan Zhou, Wenqi Ouyang, Shuai Yang, Yanhong Zeng, and Xingang Pan. WorldMem: Long-term Consistent World Simulation with Memory. In Advances in Neural Information Processing Systems, volume 38, Main Conference, pp. 49632–49652. Curran Associates, Inc., 2025. doi: 10.52202/ 085713-1659. URL https://proceedings.neurips.cc/paper","arxiv_match":false}],"id":"2504.12369","metadata_source":"Cached arXiv API metadata","rank":808},{"title":"Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety","arxiv_id":"2507.11473","year":2025,"url":"https://arxiv.org/abs/2507.11473","citing_submissions":124,"explicit_arxiv_matches":110,"examples":[{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"eoffrey Irving, Erik Jenner, Daniel Kokotajlo, Victoria Krakovna, Shane Legg, David Lindner, David Luan, Aleksander M˛ adry, Julian Michael, Neel Nanda, Dave Orr, Jakub Pachocki, Ethan Perez, Mary Phuong, Fabien Roger, Joshua Saxe, Buck Shlegeris, Martín Soto, Eric Steinberger, Jasmine Wang, Wojciech Zaremba, Bowen Baker, Rohin Shah, and Vlad Mikulik. Chain of thought monitorability: A new and fragile opportunity for ai safety, 2025. URL https://arxiv.org/abs/2507.11473. Vedang Lad, Jin Hwa Lee, Wes Gurnee, and Max Tegmark. Remarkable robustness of LLMs: Stages of inf","arxiv_match":true},{"id":"Dy3RZfaeEs","title":"Do What You Say: Towards Verifiable Reasoning for Scientific Editing","forum_url":"https://openreview.net/forum?id=Dy3RZfaeEs","excerpt":"offrey Irving, Erik Jenner, Daniel Kokotajlo, Victoria Krakovna, Shane Legg, David Lindner, David Luan, Aleksander M ˛ adry, Julian Michael, Neel Nanda, Dave Orr, Jakub Pachocki, Ethan Perez, Mary Phuong, Fabien Roger, Joshua Saxe, Buck Shlegeris, Martín Soto, Eric Steinberger, Jasmine Wang, Wojciech Zaremba, Bowen Baker, Rohin Shah, and Vlad Mikulik. Chain of thought monitorability: A new and fragile opportunity for ai safety, 2025. URL https://arxiv.org/abs/2507.11473. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Le","arxiv_match":true},{"id":"I35nG4HR52","title":"When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making","forum_url":"https://openreview.net/forum?id=I35nG4HR52","excerpt":"nce on Learning Representations, 2024. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 2022. Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen, Alan Cooney, Allan Dafoe, Anca Dragan, et al. Chain of thought monitorability: A new and fragile opportunity for ai safety. arXiv preprint arXiv:2507.11473, 2025. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al. Measuring faithfulness in chain-of-thou","arxiv_match":true}],"id":"2507.11473","metadata_source":"Cached arXiv API metadata","rank":809},{"title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","arxiv_id":null,"year":2014,"url":null,"citing_submissions":124,"explicit_arxiv_matches":0,"examples":[{"id":"R80bVPjG4H","title":"TDE: Target-conditioned Decision Evidence for Generative Recommendation","forum_url":"https://openreview.net/forum?id=R80bVPjG4H","excerpt":" recommendations. In Proceedings of the 18th ACM Conference on Recommender Systems, pp. 1039–1044, 2024. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929–1958, 2014. Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. Bert4rec: Sequential recommendation with bidirectional e","arxiv_match":false},{"id":"BBn3Q6dIzT","title":"History Dropout for Long-Horizon Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=BBn3Q6dIzT","excerpt":"ss Tedrake, and Vincent Sitzmann. History-guided video diffusion. arXiv preprint arXiv:2502.06764, 2025. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(56):1929–1958, 2014. Mingzhen Sun, Weining Wang, Gen Li, Jiawei Liu, Jiahui Sun, Wanquan Feng, Shanshan Lao, Siyu Zhou, Qian He, and Jing Liu. AR-Diffusion: ","arxiv_match":false},{"id":"hjPoL9WGRc","title":"AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents","forum_url":"https://openreview.net/forum?id=hjPoL9WGRc","excerpt":"water manipulators: A review. Ocean Engineering, 163:431–450, 2018. doi: 10.1016/j.oceaneng.2018.06.018. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(56):1929–1958, 2014. Edoardo Topini, Alberto Topini, Matteo Franchi, Alessandro Bucci, Nicola Secciani, Alessandro Ridolfi, and Benedetto Allotta. LSTM-based","arxiv_match":false}],"id":"ede296e70d296b4d","metadata_source":"Curated bibliography catalog / publisher metadata","rank":810},{"title":"Fast transformer decoding: One write-head is all you need","arxiv_id":"1911.02150","year":2019,"url":"https://arxiv.org/abs/1911.02150","citing_submissions":124,"explicit_arxiv_matches":119,"examples":[{"id":"Y00XNwRb0F","title":"CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache","forum_url":"https://openreview.net/forum?id=Y00XNwRb0F","excerpt":"ion: Bandwidth-efficient LLM inference. In 5th Workshop on practical ML for limited/low resource settings, 2024. URL https://openreview.net/forum?id= cp1hJ67l3M. Shuvendu Roy, Mengyao Zhai, Hossein Hajimirsadeghi, and Golnoosh Samei. Coverage-driven kv cache eviction for efficient and improved inference of llm, 2026. URL https://arxiv.org/ abs/2606.29563. Noam Shazeer. Fast transformer decoding: One write-head is all you need, 2019. URL https: //arxiv.org/abs/1911.02150. Jack Shi and Jerry Gu. Trust the mass: Forced weights in kv-cache eviction, 2026. URL https: //arx","arxiv_match":true},{"id":"jjf3FKJp3k","title":"MetaBrainPro: A Graph Learning Approach for Enterprise Metadata Lineage Retrieval","forum_url":"https://openreview.net/forum?id=jjf3FKJp3k","excerpt":"ask learning. arXiv preprint arXiv:1907.01864, 2019. Sebastian Ruder, Bailey Cronin, and David Wingate. Learning many-to-many learning strategies. arXiv preprint arXiv:1907.01864, 2019. Noam Shazeer. Fast transformer decoding: One write-head is all you need. arXiv:1911.03854, 2019. arXiv preprint Noam Shazeer, Ann Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoff Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely gated mixtu","arxiv_match":false},{"id":"GSq4UfduA7","title":"One Trajectory, Many Targets: Supervision Density and the Compute-Optimal Regime in Sequential Recommendation","forum_url":"https://openreview.net/forum?id=GSq4UfduA7","excerpt":"ed ranking from implicit feedback. In Jeff A. Bilmes and Andrew Y. Ng (eds.), UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009, pp. 452–461. AUAI Press, 2009. URL https://www.auai.org/ uai2009/papers/UAI2009_0139_48141db02b9f0b02bc7158819ebfa2c7.pdf. Noam Shazeer. Fast transformer decoding: One write-head is all you need. CoRR, abs/1911.02150, 2019. URL https://arxiv.org/abs/1911.02150. Yehjin Shin, Jeongwhan Choi, Hyowon Wi, and Noseong Park. An attentive inductive bias for sequenti","arxiv_match":true}],"id":"1911.02150","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":811},{"title":"Hierarchical Reasoning Model","arxiv_id":"2506.21734","year":2025,"url":"https://arxiv.org/abs/2506.21734","citing_submissions":124,"explicit_arxiv_matches":114,"examples":[{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":"odel parameters. arXiv preprint arXiv:2408.03314, 2024. URL https://arxiv.org/abs/2408.03314. David Sussillo and Omri Barak. Opening the black box: Low-dimensional dynamics in highdimensional recurrent neural networks. Neural Computation, 25(3):626–649, 2013. Guan Wang, Jin Li, Yuhao Sun, Xing Chen, Changling Liu, Yue Wu, Meng Lu, Sen Song, and Yasin Abbasi Yadkori. Hierarchical reasoning model. arXiv preprint arXiv:2506.21734, 2025. URL https://arxiv.org/abs/2506.21734. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and ","arxiv_match":true},{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"s/2505.09388. Changxin Tian, Kunlong Chen, Jia Liu, Ziqi Liu, Zhiqiang Zhang, and Jun Zhou. Towards greater leverage: Scaling laws for efficient mixture-of-experts language models. In International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id= 7r2lkhDGUj. Guan Wang, Jin Li, Yuhao Sun, Xing Chen, Changling Liu, Yue Wu, Meng Lu, Sen Song, and Yasin Abbasi Yadkori. Hierarchical reasoning model, 2025. URL https://arxiv.org/ abs/2506.21734. Siqi Wang, Zhengyu Chen, Bei Li, Keqing He, Min Zhang, and Jingang Wang. Scaling laws across model","arxiv_match":true},{"id":"m61N49mzzc","title":"Improving Test-Time Scaling with Adaptive Looped Transformers","forum_url":"https://openreview.net/forum?id=m61N49mzzc","excerpt":"n, Yihao Xiao, Xinyu Yang, Ran Tao, Joey Zhou, and Bryan Dai. Loop the Loopies! arXiv preprint arXiv:2607.16051, 2026. URL https://arxiv.org/abs/2607. 16051. Renee Ge, Qianli Liao, and Tomaso Poggio. Hierarchical Reasoning Models: Perspectives and Misconceptions. arXiv preprint arXiv:2510.00355, 2025. URL https://arxiv.org/abs/ 2510.00355. Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. B","arxiv_match":false}],"id":"2506.21734","metadata_source":"Cached arXiv API metadata","rank":812},{"title":"Old optimizer, new norm: An anthology","arxiv_id":"2409.20325","year":2024,"url":"https://arxiv.org/abs/2409.20325","citing_submissions":124,"explicit_arxiv_matches":89,"examples":[{"id":"LxwIDZer16","title":"When Cleaner Caches Break: Training Recipes Shape MLA Cache Quantizability","forum_url":"https://openreview.net/forum?id=LxwIDZer16","excerpt":" Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. QuaRot: Outlier-free 4-bit inference in rotated LLMs. NeurIPS, 2024. Pierfrancesco Beneventano, Mahmoud Abdelmoneum, and Tomaso Poggio. The spectral dynamics and noise geometry of muon. arXiv preprint arXiv:2606.08388, 2026. Jeremy Bernstein and Laker Newhouse. Old optimizer, new norm: An anthology. arXiv preprint arXiv:2409.20325, 2024. Albert Catalan-Tatjer, Niccolò Ajroldi, and Jonas Geiping. Training dynamics impact post-tra","arxiv_match":true},{"id":"W1kmyDnBQE","title":"DAWN: Scalable Matrix-Whitening Optimization via Spectral Selectivity","forum_url":"https://openreview.net/forum?id=W1kmyDnBQE","excerpt":"ural computation, 10(2):251–276, 1998. Jinho Baik, Gérard Ben Arous, and Sandrine Péché. Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices. 2005. Florent Benaych-Georges and Raj Rao Nadakuditi. The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices. Advances in Mathematics, 227(1):494–521, 2011. Jeremy Bernstein and Laker Newhouse. Old optimizer, new norm: An anthology. arXiv preprint arXiv:2409.20325, 2024. √ Matan Gavish and David L Donoho. The optimal hard threshold for singular values is 4/ 3. I","arxiv_match":true},{"id":"popEknHwl3","title":"Angle-Scheduled Muon: Replacing Implicit Step Decay with an Explicit Schedule","forum_url":"https://openreview.net/forum?id=popEknHwl3","excerpt":"arXiv:2002.11803, 2020. URL https://arxiv. org/abs/2002.11803. Noah Amsel, David Persson, Christopher Musco, and Robert M. Gower. The polar express: Optimal matrix sign methods and their application to the Muon algorithm. In International Conference on Learning Representations, 2026. URL https://arxiv.org/abs/2505.16932. Jeremy Bernstein and Laker Newhouse. Old optimizer, new norm: An anthology. arXiv preprint arXiv:2409.20325, 2024. URL https://arxiv.org/abs/2409.20325v2. Peng Cheng, Jiucheng Zang, Qingnan Li, Liheng Ma, Yufei Cui, Yingxue Zhang, Boxing Chen, Ming Jian","arxiv_match":true}],"id":"2409.20325","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":813},{"title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","arxiv_id":"2312.17090","year":2023,"url":"https://arxiv.org/abs/2312.17090","citing_submissions":124,"explicit_arxiv_matches":45,"examples":[{"id":"95Ipc4DdRP","title":"LensBridge: Frequency-Guided Compound Degradation Adaptation for Lens Aberration Correction and Veiling Glare Removal","forum_url":"https://openreview.net/forum?id=95Ipc4DdRP","excerpt":"CVPR, 2025. 8, 16 Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guangtao Zhai, and Weisi Lin. Q-Align: Teaching LMMs for visual scoring via discrete text-defined levels. In ICML, 2024a. 7 Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. In NIPS, 2024b. 16 Yicheng Wu, Qiurui He, Tianfan ","arxiv_match":false},{"id":"c9o20nbLx7","title":"EpiPE: Epipolar Positional Encoding for Camera-Controlled Video Generation","forum_url":"https://openreview.net/forum?id=c9o20nbLx7","excerpt":"r video generation. In ACM SIGGRAPH Conf. Papers, pp. 1–11, 2024. Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: teaching lmms for visual scoring via discrete text-defined levels. In ICML, pp. 54015–54029, 2024. Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation. IEEE Transactions on Pa","arxiv_match":false},{"id":"trytZidnme","title":"HiFiPix: Bringing High-Fidelity Real-World Super-Resolution Back to Pixel Space","forum_url":"https://openreview.net/forum?id=trytZidnme","excerpt":"rld image super-resolution. In ECCV, pp. 101–117. Springer, 2020. Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: Teaching lmms for visual scoring via discrete text-defined levels. In ICML, 2024a. Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang, Xiangtao Kong, Jixin Zhao, Shihao Wang, and Lei Zhang. Vosr: A vision-only generative model for image super-resolution. In CVPR, pp. 1631","arxiv_match":false}],"id":"2312.17090","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":814},{"title":"ReTool: Reinforcement Learning for Strategic Tool Use in LLMs","arxiv_id":"2504.11536","year":2025,"url":"https://arxiv.org/abs/2504.11536","citing_submissions":124,"explicit_arxiv_matches":62,"examples":[{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":".acl-long.1023. URL https://aclanthology.org/ 2026.acl-long.1023/. DeepSeek. DeepSeek V4 Model Card. Official model card, 2026. URL https://fe-static. deepseek.com/chat/transparency/deepseek-V4-model-card-EN.pdf. Jiazhan Feng, Shijue Huang, Xingwei Qu, Ge Zhang, Yujia Qin, Baoquan Zhong, Chengquan Jiang, Jinxin Chi, and Wanjun Zhong. ReTool: Reinforcement Learning for Strategic Tool Use in LLMs. arXiv preprint arXiv:2504.11536, 2025. URL https://arxiv.org/abs/2504.11536. Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Erkang Zhu, Friederike N","arxiv_match":true},{"id":"CFVCzCNC1l","title":"Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents","forum_url":"https://openreview.net/forum?id=CFVCzCNC1l","excerpt":"Linguistics. ISBN 979-8-89176-395-1. doi: 10.18653/v1/2026.findings-acl.872. Jiazhan Feng, Shijue Huang, Xingwei Qu, Ge Zhang, Yujia Qin, Baoquan Zhong, Chengquan Jiang, Jinxin Chi, and Wanjun Zhong. ReTool: Reinforcement learning for strategic tool use in LLMs. In The Fourteenth International Conference on Learning Representations, 2026. Zhicheng Guo, Sijie Cheng, Hao Wang, Shihao Liang, Yujia Qin, Peng Li, Zhiyuan Liu, Maosong Sun, and Yang Liu. StableToo","arxiv_match":false},{"id":"x0TKaMUu1W","title":"Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States","forum_url":"https://openreview.net/forum?id=x0TKaMUu1W","excerpt":" large language models in clinical diagnostics. Nature Communications, 2026. Jiazhan Feng, Shijue Huang, Xingwei Qu, Ge Zhang, Yujia Qin, Baoquan Zhong, Chengquan Jiang, Jinxin Chi, and Wanjun Zhong. Retool: Reinforcement learning for strategic tool use in llms. In International Conference on Learning Representations, volume 2026, pp. 37909–37926, 2026. Qiao Gu, Ali Kuwajerwala, Sacha Morin, Krishna Murthy Jatavallabhula, Bipasha Sen, Aditya Agarwal, Corban","arxiv_match":false}],"id":"2504.11536","metadata_source":"Cached arXiv API metadata","rank":815},{"title":"Safe RLHF: Safe reinforcement learning from human feedback","arxiv_id":"2310.12773","year":2023,"url":"https://arxiv.org/abs/2310.12773","citing_submissions":124,"explicit_arxiv_matches":38,"examples":[{"id":"y89ckFndSX","title":"HazardAuditor: From Executable Threats to Safer Computer-Use Agents","forum_url":"https://openreview.net/forum?id=y89ckFndSX","excerpt":" engine. GitHub repository, 2026. URL https://github.com/NandhaKishorM/laya. Accessed: 202609-26. Juntao Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. Safe rlhf: Safe reinforcement learning from human feedback. 2024:50750– 50777, 2024. Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. Agentdojo: A dynamic environment to evaluate prompt injection attac","arxiv_match":false},{"id":"yECLVPwR3R","title":"Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation","forum_url":"https://openreview.net/forum?id=yECLVPwR3R","excerpt":"s/paper/2024/ hash/1eb543faf7c69e8a7eb8b85f70be818f-Abstract-Datasets_and_ Benchmarks_Track.html. Juntao Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. Safe RLHF: Safe Reinforcement Learning from Human Feedback. In International Conference on Learning Representations, 2024b. URL https://proceedings.iclr.cc/paper_files/paper/2024/hash/ dd1577afd396928ed64216f3f1fd5556-Abstract-Conference.html. Pucheng Dang, ","arxiv_match":false},{"id":"bCiKlMuOkz","title":"SafeOPD: Safety-Preserving On-Policy Distillation from Domain-Specialized Teachers","forum_url":"https://openreview.net/forum?id=bCiKlMuOkz","excerpt":"ive from safety neurons. Advances in Neural Information Processing Systems, 38:12665–12700, 2026. Juntao Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. Safe rlhf: Safe reinforcement learning from human feedback. In International Conference on Learning Representations, volume 2024, pp. 50750–50777, 2024. Xeron Du, Yifan Yao, Kaijing Ma, Bingli Wang, Tianyu Zheng, Minghao Liu, Yiming Liang, Xiaolong Jin, Zhen","arxiv_match":false}],"id":"2310.12773","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":816},{"title":"StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding","arxiv_id":"2411.03628","year":2024,"url":"https://arxiv.org/abs/2411.03628","citing_submissions":124,"explicit_arxiv_matches":41,"examples":[{"id":"tSF2DDsC2y","title":"ALIVE: Making Streaming Video Models Interactive","forum_url":"https://openreview.net/forum?id=tSF2DDsC2y","excerpt":"m real-world online video understanding?, 2025. URL https://arxiv.org/abs/2501.05510. Shuo Liang, Yiwu Zhong, Zi-Yuan Hu, Yeyao Tao, and Liwei Wang. Fine-grained spatiotemporal grounding on egocentric videos, 2025. URL https://arxiv.org/abs/2508.00518. Junming Lin, Zheng Fang, Chi Chen, Zihao Wan, Fuwen Luo, Peng Li, Yang Liu, and Maosong Sun. Streamingbench: Assessing the gap for mllms to achieve streaming video understanding, 2024. URL https://arxiv.org/abs/2411.03628. Xudong Lu, Yang Bo, Jinpeng Chen, Shuhan Li, Xintong Guo, Huankang Guan, Fang Liu, Dunyuan Xu, Pei","arxiv_match":true},{"id":"Ps7rUvLZGb","title":"MetricKV: Decoded-Space Geometry for Visual KV Compression in Streaming Video Understanding","forum_url":"https://openreview.net/forum?id=Ps7rUvLZGb","excerpt":"r quantization for kv cache compression. arXiv preprint arXiv:2506.18879, 2025. Junming Lin, Zheng Fang, Chi Chen, Haoxuan Cheng, Zihao Wan, Fuwen Luo, Ziyue Wang, Peng Li, Yang Liu, and Maosong Sun. Streamingbench: Assessing the gap for mllms to achieve streaming video understanding. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 12147–12151. IEEE, 2026. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vl","arxiv_match":false},{"id":"KY3xZQ7lP8","title":"ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding","forum_url":"https://openreview.net/forum?id=KY3xZQ7lP8","excerpt":"ming video reasoning. ArXiv, abs/2604.17052, 2026. URL https://api.semanticscholar.org/CorpusID:287635604. Junming Lin, Zheng Fang, Chi Chen, Zihao Wan, Fuwen Luo, Peng Li, Yang Liu, and Maosong Sun. Streamingbench: Assessing the gap for mllms to achieve streaming video understanding. ArXiv, abs/2411.03628, 2024. URL https://api.semanticscholar.org/CorpusID:273850338. Haocheng Lu, Nan Zhang, Wei Tao, Xiaoyang Qu, Guokuan Li, Jiguang Wan, and Jianzong Wang. Vista: Scene-aware opti","arxiv_match":false}],"id":"2411.03628","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":817},{"title":"Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation","arxiv_id":"2605.18740","year":2026,"url":"https://arxiv.org/abs/2605.18740","citing_submissions":124,"explicit_arxiv_matches":116,"examples":[{"id":"9XqqxYouLj","title":"Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning","forum_url":"https://openreview.net/forum?id=9XqqxYouLj","excerpt":"reinforcement learning system at scale. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, NeurIPS 2025, San Diego, CA, USA, December 2-7, 2025 / Mexico City, Mexico, November 30 December 5, 2025, 2025. 1 Qianhao Yuan, Jie Lou, Xing Yu, Hongyu Lin, Le Sun, Xianpei Han, and Yaojie Lu. Vision-opd: Learning to see fine details for multimodal llms via on-policy self-distillation. arXiv preprint arXiv:2605.18740, 2026. 1 Xiangyu Zeng, Zhiqiu Zhang, Yuhan Zhu, Xinhao Li, Zikang Wang, Changlian Ma, Qingyu Zhang,","arxiv_match":true},{"id":"sBvEgtiF5l","title":"Counterfactual Visual On-Policy Distillation","forum_url":"https://openreview.net/forum?id=sBvEgtiF5l","excerpt":"2026. Hee Suk Yoon, Eunseop Yoon, Jaehyun Jang, SooHwan Eom, Ji Woo Hong, Mark HasegawaJohnson, Qi Dai, Chong Luo, and Chang D Yoo. Decomposed on-policy distillation for visionlanguage reasoning: Steering gradients for visual grounding. arXiv preprint arXiv:2606.00564, 2026. Qianhao Yuan, Jie Lou, Xing Yu, Hongyu Lin, Le Sun, Xianpei Han, and Yaojie Lu. Vision-opd: Learning to see fine details for multimodal llms via on-policy self-distillation. arXiv preprint arXiv:2605.18740, 2026. Kangning Zhang, Yixing Li, Shuai Shao, Qingyao Li, Zhengxi Lu, Zhiyuan Yao, Jianghao Lin, We","arxiv_match":true},{"id":"WZ8PYNGCsW","title":"Think in 360°: Benchmarking and Bootstrapping Panoramic Video Reasoning","forum_url":"https://openreview.net/forum?id=WZ8PYNGCsW","excerpt":"ference.pdf. Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. MiniCPM-V: A GPT-4V Level MLLM on Your Phone. arXiv preprint arXiv:2408.01800, 2024. URL https://arxiv.org/abs/2408.01800. Qianhao Yuan, Jie Lou, Xing Yu, Hongyu Lin, Le Sun, Xianpei Han, and Yaojie Lu. Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation, 2026. URL https://arxiv.org/abs/2605.18740. Heeseung Yun, Youngjae Yu, Wonsuk Yang, Kangil Lee, and Gunhee Kim. Pano-AVQA: Grounded Audio-Visu","arxiv_match":true}],"id":"2605.18740","metadata_source":"Cached arXiv API metadata","rank":818},{"title":"AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents","arxiv_id":"2503.18666","year":2025,"url":"https://arxiv.org/abs/2503.18666","citing_submissions":123,"explicit_arxiv_matches":45,"examples":[{"id":"TO4R4c6u7V","title":"From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents","forum_url":"https://openreview.net/forum?id=TO4R4c6u7V","excerpt":"eidecke, and Alex Beutel. The instruction hierarchy: Training llms to prioritize privileged instructions, 2024. URL https: //arxiv.org/abs/2404.13208. Haoyu Wang, Christopher M. Poskitt, and Jun Sun. Agentspec: Customizable runtime enforcement for safe and reliable llm agents. In Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering, ICSE ’26, pp. 2938–2950. ACM, April 2026. doi: 10.1145/3744916.3764546. URL http://dx.doi.org/10.1145/37449","arxiv_match":false},{"id":"oJ7JcFgeXE","title":"Recovering Structural and Semantic Constraints for LLM Agent Anomaly Detection","forum_url":"https://openreview.net/forum?id=oJ7JcFgeXE","excerpt":" input prompt. In Proceedings of the 31st International Conference on Computational Linguistics, pp. 9124–9146, 2025. Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. The instruction hierarchy: Training llms to prioritize privileged instructions. arXiv preprint arXiv:2404.13208, 2024. Haoyu Wang, Christopher M Poskitt, and Jun Sun. Agentspec: Customizable runtime enforcement for safe and reliable llm agents. arXiv preprint arXiv:2503.18666, 2025a. Peiran Wang, Yang Liu, Yunfei Lu, Yifeng Cai, Hongbo Chen, Qingyou Yang, Jie Zhang, Jue Hon","arxiv_match":true},{"id":"paT4NcZxBy","title":"When and Why Do Agents Become Unsafe? A Trajectory-Aware Benchmark for Multi-Turn Agent Safety Evaluation","forum_url":"https://openreview.net/forum?id=paT4NcZxBy","excerpt":"ulti-turn, multilingual LLM agents. In Forty-third International Conference on Machine Learning, 2026. URL https://icml.cc/ virtual/2026/poster/61155. Haoyu Wang, Christopher M. Poskitt, and Jun Sun. AgentSpec: Customizable runtime enforcement for safe and reliable LLM agents. In Proceedings of the 48th International Conference on Software Engineering. Association for Computing Machinery, 2026a. doi: 10.1145/3744916. 3764546. URL https://doi.org/10.1145/3744916.3764546. J","arxiv_match":false}],"id":"2503.18666","metadata_source":"Cached arXiv API metadata","rank":819},{"title":"Autoregressive model beats diffusion: Llama for scalable image generation","arxiv_id":"2406.06525","year":2024,"url":"https://arxiv.org/abs/2406.06525","citing_submissions":123,"explicit_arxiv_matches":113,"examples":[{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"on Processing Systems, volume 29, 2016. Fengyuan Shi, Zhuoyan Luo, Yixiao Ge, Yujiu Yang, Ying Shan, and Limin Wang. Scalable image tokenization with index backpropagation quantization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16037–16046, 2025. Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeli","arxiv_match":true},{"id":"o6IsUtKlgQ","title":"Learning Spatial-Pyramid Visual Representations for Autoregressive Image Generation","forum_url":"https://openreview.net/forum?id=o6IsUtKlgQ","excerpt":"formation processing systems, 29, 2016. Fengyuan Shi, Zhuoyan Luo, Yixiao Ge, Yujiu Yang, Ying Shan, and Limin Wang. Scalable image tokenization with index backpropagation quantization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16037–16046, 2025. Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinki","arxiv_match":true},{"id":"XS1t7JP5FD","title":"Hidden in Plain Sight: Recovering Latent Generative Diversity in Visual Autoregressive Models","forum_url":"https://openreview.net/forum?id=XS1t7JP5FD","excerpt":"ichard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in neural information processing systems, 35:25278–25294, 2022. Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024. Haotian Tang, Yecheng Wu, Shang Yang, Enze Xie, Junsong Chen, Junyu Chen, Zhuoyang Zhang, Ha","arxiv_match":true}],"id":"2406.06525","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":820},{"title":"LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models","arxiv_id":"2505.19223","year":2025,"url":"https://arxiv.org/abs/2505.19223","citing_submissions":123,"explicit_arxiv_matches":64,"examples":[{"id":"9GDStxR3kP","title":"Inter-Trajectory Dependence Limits Answer Coverage in Local dLLM Repair","forum_url":"https://openreview.net/forum?id=9GDStxR3kP","excerpt":"int arXiv:2505.23614, 2025. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911, 2023. Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Hu, Jun Zhou, Jianfei Chen, Yankai Lin, Ji-Rong Wen, et al. Llada 1.5: Variance-reduced preference optimization for large language diffusion models. arXiv preprint arXiv:2505.19223, 2025. ","arxiv_match":true},{"id":"Xt9oJoQrHM","title":"Light-DiffRetriever: Budget-Flexible Retrieval with Diffusion Language Models via Matryoshka Representative Tokens","forum_url":"https://openreview.net/forum?id=Xt9oJoQrHM","excerpt":": 10.48550/arXiv.2506.05176. URL https://dblp.org/rec/journals/corr/abs-2506-05176. Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. LLaDA 1.5: Variance-reduced preference optimization for large language diffusion models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 11425–11460, 2026. doi: 10.18653/v1/2026.acl-long.524. URL https://aclantholog","arxiv_match":false},{"id":"w9AtxUbfFB","title":"Loop-Distill: Refining Information Flow in Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=w9AtxUbfFB","excerpt":"-moe: A sparse moe diffusion language model. arXiv preprint arXiv:2509.24389, 2025a. Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, JiRong Wen, and Chongxuan Li. Llada 1.5: Variance-reduced preference optimization for large language diffusion models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 11425–11460, 2026. Rui-Jie Zhu, Zixuan Wang, Kai Hua, Tianyu Zhang, Ziniu Li, ","arxiv_match":false}],"id":"2505.19223","metadata_source":"Cached arXiv API metadata","rank":821},{"title":"LongVU: Spatiotemporal adaptive compression for long video-language understanding","arxiv_id":"2410.17434","year":2024,"url":"https://arxiv.org/abs/2410.17434","citing_submissions":123,"explicit_arxiv_matches":62,"examples":[{"id":"OAX2QPoTJg","title":"HyLin: Modality-Aware Hybrid Attention for Efficient Long-Context Vision–Language Models","forum_url":"https://openreview.net/forum?id=OAX2QPoTJg","excerpt":" Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Bala Varadarajan, Florian Bordes, Zhuang Liu, Hu Xu, Hyunwoo J. Kim, Bilge Soran, Raghuraman Krishnamoorthi, Mohamed Elhoseiny, and Vikas Chandra. LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding. In Proceedings of the 42nd International Conference on Machine Learning, pp. 54582–54599, 2025. Yan Shu, Zheng Liu, Peitian Zhang, Minghao Qin, Junjie Zhou, Zhengyang Liang, Tiejun Huang, and Bo Zha","arxiv_match":false},{"id":"x3rKKKW2Wg","title":"VideoEvolve: Co-Evolving Memory and Retrieval for Long Video Understanding","forum_url":"https://openreview.net/forum?id=x3rKKKW2Wg","excerpt":"n, and Lu Hou. Timechat: A time-sensitive multimodal large language model for long video understanding. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14313–14323. IEEE, 2024. Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, et al. Longvu: Spatiotemporal adaptive compression for long video-language understanding. arXiv preprint arXiv:2410.17434, 2024. Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Hai","arxiv_match":true},{"id":"r9EgYOUmRo","title":"Saevia: Spatiotemporal Token Compression with Attention Compensation for Efficient Video Understanding","forum_url":"https://openreview.net/forum?id=r9EgYOUmRo","excerpt":" Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, Zhuang Liu, Hu Xu, Hyunwoo J. Kim, Bilge Soran, Raghuraman Krishnamoorthi, Mohamed Elhoseiny, and Vikas Chandra. LongVU: Spatiotemporal adaptive compression for long video-language understanding. In ICML, 2025b. URL https://proceedings.mlr.press/v267/shen25j.html. Dingjie Song, Wenjun Wang, Shunian Chen, Xidong Wang, Michael X. Guan, and Benyou Wang. Less is more: A simple yet effective toke","arxiv_match":false}],"id":"2410.17434","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":822},{"title":"MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training","arxiv_id":"2606.30406","year":2026,"url":"https://arxiv.org/abs/2606.30406","citing_submissions":123,"explicit_arxiv_matches":109,"examples":[{"id":"wBNqzHsvaM","title":"No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation","forum_url":"https://openreview.net/forum?id=wBNqzHsvaM","excerpt":"URL https://www.together.ai/blog/deepcoder. Wenhan Ma, Jianyu Wei, Liang Zhao, Hailin Zhang, Bangjun Xiao, Lei Li, Qibin Yang, Bofei Gao, Yudong Wang, Rang Li, Jinhao Dong, Zhifang Sui, and Fuli Luo. MOPD: Multi-teacher onpolicy distillation for capability integration in LLM post-training, 2026. URL https://arxi v.org/abs/2606.30406v1. arXiv preprint, version 1. math-ai. AIME25 dataset. Hugging Face dataset, 2025. URL https://huggingface. co/datasets/math-ai/aime25/tree/563bb8404243c5","arxiv_match":false},{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"yer? In European conference on computer vision, pp. 216–233. Springer, 2024. Wenhan Ma, Jianyu Wei, Liang Zhao, Hailin Zhang, Bangjun Xiao, Lei Li, Qibin Yang, Bofei Gao, Yudong Wang, Rang Li, et al. MOPD: Multi-teacher on-policy distillation for capability integration in LLM post-training. In ICML 2026 Workshop on RL from World Feedback, 2026. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, S","arxiv_match":false},{"id":"WqaXwcWgJT","title":"StrataDistill: Structure-Aware Distillation for Long-Document Retrieval","forum_url":"https://openreview.net/forum?id=WqaXwcWgJT","excerpt":"un Luo, Zheng Liu, Shitao Xiao, and Kang Liu. BGE landmark embedding: A chunking-free embedding method for retrieval augmented long-context large language models. arXiv preprint arXiv:2402.11573, 2024. Wenhan Ma, Jianyu Wei, Liang Zhao, Hailin Zhang, Bangjun Xiao, Lei Li, Qibin Yang, Bofei Gao, Yudong Wang, Rang Li, Jinhao Dong, Zhifang Sui, and Fuli Luo. Mopd: Multi-teacher on-policy distillation for capability integration in llm post-training. arXiv preprint arXiv:2606.30406, 2026. Zach Nussbaum, John Xavier Morris, Andriy Mulyar, and Brandon Duderstadt. Nomic embed: Train","arxiv_match":true}],"id":"2606.30406","metadata_source":"Cached arXiv API metadata","rank":823},{"title":"MedGemma Technical Report","arxiv_id":"2507.05201","year":2025,"url":"https://arxiv.org/abs/2507.05201","citing_submissions":123,"explicit_arxiv_matches":120,"examples":[{"id":"2te1UDMg3T","title":"MedRouter: Demystifying Knowledge Differences Across Medical LLMs for Routing-Based Reasoning","forum_url":"https://openreview.net/forum?id=2te1UDMg3T","excerpt":", Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. ArXiv, abs/1707.06347, 2017. URL https://api. semanticscholar.org/CorpusID:28695052. Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, Atilla Kiraly, Madeleine Traverse, Timo Kohlberger, Shawn Xu, Fayaz Jamil, Cían Hughes, Charles Lau, et al. MedGemma technical report. arXiv preprint arXiv:2507.05201, 2025. doi: 10.48550/arXiv.2507.05201. URL https: //arxiv.org/abs/2507.05201. Kexin Shang, Chia-Hsuan Chang, and Christopher C Yang. Collaboration among multiple large language","arxiv_match":true},{"id":"EUwi8JmVHj","title":"Evaluating Vision-Language Models on Images of Individuals with Limb Deficiencies","forum_url":"https://openreview.net/forum?id=EUwi8JmVHj","excerpt":"s Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-tosentence models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2015. Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, Atilla Kiraly, Madeleine Traverse, Timo Kohlberger, Shawn Xu, Fayaz Jamil, Cı́an Hughes, Charles Lau, et al. Medgemma technical report. arXiv preprint arXiv:2507.05201, 2025. Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning ","arxiv_match":true},{"id":"8zRnR4jkT9","title":"HealthCoreBench: Measuring Evaluation Artifacts for Reliable Medical LLM Evaluation","forum_url":"https://openreview.net/forum?id=8zRnR4jkT9","excerpt":" Harris, Eduardo Reis, Jeffrey Jopling, and Michael Moor. AgentClinic: A multimodal agent benchmark to evaluate ai in simulated clinical environments. arXiv preprint arXiv:2405.07960, 2024. doi: 10.48550/arXiv.2405.07960. URL https:// arxiv.org/abs/2405.07960. Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, Atilla Kiraly, Madeleine Traverse, Timo Kohlberger, Shawn Xu, Fayaz Jamil, Cı́an Hughes, Charles Lau, et al. Medgemma technical report. arXiv preprint arXiv:2507.05201, 2025. Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan ","arxiv_match":true}],"id":"2507.05201","metadata_source":"Cached arXiv API metadata","rank":824},{"title":"Moshi: A speech-text foundation model for real-time dialogue","arxiv_id":"2410.00037","year":2024,"url":"https://arxiv.org/abs/2410.00037","citing_submissions":123,"explicit_arxiv_matches":103,"examples":[{"id":"Fqv6UwosZ0","title":"From Instruction to Event: Sound-Triggered Mobile Manipulation","forum_url":"https://openreview.net/forum?id=Fqv6UwosZ0","excerpt":"-o 4.5: Towards real-time full-duplex omni-modal interaction, 2026. Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave, and Neil Zeghidour. Moshi: a speech-text foundation model for real-time dialogue. arXiv, 2024. Adrià López Escoriza, Nicklas Hansen, Stone Tao, Tongzhou Mu, and Hao Su. Multi-stage manipulation with demonstration-augmented reward, policy, and world model learning. In ICML, 2025","arxiv_match":false},{"id":"KbAV5KY9YW","title":"Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue","forum_url":"https://openreview.net/forum?id=KbAV5KY9YW","excerpt":"0/ARXIV.2512.14291. URL https:// doi.org/10.48550/arXiv.2512.14291. Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave, and Neil Zeghidour. Moshi: a speech-text foundation model for real-time dialogue. CoRR, abs/2410.00037, 2024. doi: 10.48550/ARXIV.2410.00037. URL https: //doi.org/10.48550/arXiv.2410.00037. Xiang Deng, Feng Gao, Yong Zhang, Youxin Pang, Xu Xiaoming, Zhuoliang Kang, Xiaoming Wei, ","arxiv_match":false},{"id":"3cCUAYrxFe","title":"Duplex-MPE: Benchmarking Multi-Party Interaction in Full-Duplex Dialogue","forum_url":"https://openreview.net/forum?id=3cCUAYrxFe","excerpt":"8, 2026. doi: 10.1162/TACL.A.628. URL https://doi.org/10.1162/tacl.a.628. Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave, and Neil Zeghidour. Moshi: a speech-text foundation model for real-time dialogue. CoRR, abs/2410.00037, 2024. doi: 10.48550/ARXIV.2410.00037. URL https: //doi.org/10.48550/arXiv.2410.00037. Starkey Duncan. Some signals and rules for taking speaking turns in conversations. Journal","arxiv_match":false}],"id":"2410.00037","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":825},{"title":"Qwen2-Audio technical report","arxiv_id":"2407.10759","year":2024,"url":"https://arxiv.org/abs/2407.10759","citing_submissions":123,"explicit_arxiv_matches":110,"examples":[{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"l: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In CVPR, 2024c. Junhao Cheng, Yuying Ge, Teng Wang, Yixiao Ge, Jing Liao, and Ying Shan. Video-holmes: Can mllm think like holmes for complex video reasoning?, 2025. URL https://arxiv.org/ abs/2505.21374. Yunfei Chu, Jin Xu, Qian Yang, Haojie Wei, Xipin Wei, Zhifang Guo, Yichong Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, et al. Qwen2-audio technical report. arXiv preprint arXiv:2407.10759, 2024. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dh","arxiv_match":true},{"id":"do6Wfg04EK","title":"ATTEST: Evidence-Grounded Reinforcement Learning for Long-Audio Temporal Understanding","forum_url":"https://openreview.net/forum?id=do6Wfg04EK","excerpt":"hant Arora, Tao Jin, Shinji Watanabe, and Zhou Zhao. AHa-bench: Benchmarking audio hallucinations in large audio-language models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2025. URL https://openreview.net/forum?id=vCej5sO61x. Yunfei Chu, Jin Xu, Qian Yang, Haojie Wei, Xipin Wei, Zhifang Guo, Yichong Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, et al. Qwen2-audio technical report. arXiv preprint arXiv:2407.10759, 2024. Ding Ding, Zeqian Ju, Yichong Leng, Songxiang Liu, Tong Liu, Zeyu Shang, Kai Shen, Wei Song,","arxiv_match":true},{"id":"xVqGCq3X2z","title":"SRAM: Learning Spectral Relation-Aware Representations for Environmental Sound Deepfake Detection","forum_url":"https://openreview.net/forum?id=xVqGCq3X2z","excerpt":"rint arXiv:2304.12521, 2023. Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shiliang Zhang, Zhijie Yan, Chang Zhou, and Jingren Zhou. Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models. arXiv preprint arXiv:2311.07919, 2023. Yunfei Chu, Jin Xu, Qian Yang, Haojie Wei, Xipin Wei, Zhifang Guo, Yichong Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen2-audio technical report. arXiv preprint arXiv:2407.10759, 2024. Thien-Phuc Doan, Long Nguyen-Vu, Souhwan Jung, and Kihun Hong. BTS-E: audio deepfake detecti","arxiv_match":true}],"id":"2407.10759","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":826},{"title":"RouterBench: A benchmark for multi-LLM routing system","arxiv_id":"2403.12031","year":2024,"url":"https://arxiv.org/abs/2403.12031","citing_submissions":123,"explicit_arxiv_matches":100,"examples":[{"id":"3udVm9Jxne","title":"You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference","forum_url":"https://openreview.net/forum?id=3udVm9Jxne","excerpt":"rence on algorithmic learning theory, pp. 174–188. Springer, 2011. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020. Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, and Shriyash Kaustubh Upadhyay. Routerbench: A benchmark for multi-llm routing system. arXiv preprint arXiv:2403.12031, 2024. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonza","arxiv_match":true},{"id":"hXHrZMMYZS","title":"Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestration","forum_url":"https://openreview.net/forum?id=hXHrZMMYZS","excerpt":"sentations, 2024. URL https://openreview.net/forum?id=VtmBAGCN7o. Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, and Shriyash Kaustubh Upadhyay. Routerbench: A benchmark for multi-llm routing system. CoRR, abs/2403.12031, 2024. doi: 10.48550/ARXIV.2403.12031. URL https: //doi.org/10.48550/arXiv.2403.12031. Rishi Dev Jha, Harold Triedman, Justin Wagle, and Vitaly Shmatikov. Breaking and fixing de","arxiv_match":false},{"id":"9lxTSd9x0u","title":"CompareRoute: Learning Relative Expert Capability for Prefill-Based LLM Routing","forum_url":"https://openreview.net/forum?id=9lxTSd9x0u","excerpt":"Hybrid llm: Cost-efficient and quality-aware query routing, 2024. URL https://arxiv.org/abs/2404.14618. Tao Feng, Yanzhen Shen, and Jiaxuan You. Graphrouter: A graph-based router for llm selections, 2025. URL https://arxiv.org/abs/2410.03834. Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, and Shriyash Kaustubh Upadhyay. Routerbench: A benchmark for multi-llm routing system, 2024. URL https://arxiv.org/abs/2403.12031. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Le","arxiv_match":true}],"id":"2403.12031","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":827},{"title":"Zoology: Measuring and Improving Recall in Efficient Language Models","arxiv_id":"2312.04927","year":2023,"url":"https://arxiv.org/abs/2312.04927","citing_submissions":123,"explicit_arxiv_matches":44,"examples":[{"id":"b47jrUdTcU","title":"StacKV: Spatio-Temporally Aware Compression of KV Caches in Hybrid Language Models","forum_url":"https://openreview.net/forum?id=b47jrUdTcU","excerpt":" Simran Arora, Sabri Eyuboglu, Aman Timalsina, Isys Johnson, Michael Poli, James Zou, Atri Rudra, and Christopher Ré. Zoology: Measuring and improving recall in efficient language models, 2023. URL https://arxiv.org/abs/2312.04927. Zefan Cai, Yichi Zhang, Bofei Gao, Yuliang Liu, Yucheng Li, Tianyu Liu, Keming Lu, Wayne Xiong, Yu","arxiv_match":true},{"id":"NwY6n95NIv","title":"CoWindow Attention: Full Causal Coverage Is a Collective Property","forum_url":"https://openreview.net/forum?id=NwY6n95NIv","excerpt":"er models from multi-head checkpoints, 2023. URL https://arxiv.org/abs/2305.13245. Simran Arora, Sabri Eyuboglu, Aman Timalsina, Isys Johnson, Michael Poli, James Zou, Atri Rudra, and Christopher Ré. Zoology: Measuring and improving recall in efficient language models. In The International Conference on Learning Representations, 2024. Federico Barbero, ’Alvaro Arroyo, Xiangming Gu, Christos Perivolaropoulos, Michael M. Bronstein, Petar Velivckovi ’c, and Razvan Pa","arxiv_match":false},{"id":"VB0oDV42rx","title":"MassAlloc Attention: Let Attention Allocate Its Own Compute","forum_url":"https://openreview.net/forum?id=VB0oDV42rx","excerpt":" Simran Arora, Sabri Eyuboglu, Aman Timalsina, Isys Johnson, Michael Poli, James Zou, Atri Rudra, and Christopher Ré. Zoology: Measuring and improving recall in efficient language models. In The International Conference on Learning Representations, 2024. Federico Barbero, ’Alvaro Arroyo, Xiangming Gu, Christos Perivolaropoulos, Michael M. Bronstein, Petar Velivckovi ’c, and Razvan Pa","arxiv_match":false}],"id":"2312.04927","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":828},{"title":"AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders","arxiv_id":"2501.17148","year":2025,"url":"https://arxiv.org/abs/2501.17148","citing_submissions":122,"explicit_arxiv_matches":45,"examples":[{"id":"89cECgVi4S","title":"RealControlBench: A Comprehensive Benchmark for Fine-Grained LLM and LLM Agent steer in the Wild","forum_url":"https://openreview.net/forum?id=89cECgVi4S","excerpt":"ems (NeurIPS), 2022. URL https://openreview. net/forum?id=_VjQlMeSB_J. Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D Manning, and Christopher Potts. Axbench: Steering llms? even simple baselines outperform sparse autoencoders. In International Conference on Machine Learning (ICML), pp. 67035–67080. PMLR, 2025. Zhen Xiang, Linzhi Zheng, Yanjie Li, Junyuan Hong, Qinbin Li, Han Xie, Jiawei Zhang, Zidi Xiong, Chulin Xie, Carl","arxiv_match":false},{"id":"GXJlfZBXPl","title":"D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders","forum_url":"https://openreview.net/forum?id=GXJlfZBXPl","excerpt":"alan Fabian, and Mahdi Soltanolkotabi. Emergence and evolution of interpretable concepts in diffusion models. Advances in neural information processing systems, 38:166943 – 166986, 2025. URL https://api.semanticscholar.org/CorpusID:277993804. Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D Manning, and Christopher Potts. Axbench: Steering llms? even simple baselines outperform sparse autoencoders. arXiv preprint arXiv:2501.17148, 2025. Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang L","arxiv_match":true},{"id":"nY7Q7sYhaL","title":"One-Step Steering is Not Sequential Control","forum_url":"https://openreview.net/forum?id=nY7Q7sYhaL","excerpt":"92, 2024. URL https://api.semanticscholar.org/CorpusID: 268889731. Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Daniel Jurafsky, Christopher D. Manning, and Christopher Potts. Axbench: Steering llms? even simple baselines outperform sparse autoencoders. ArXiv, abs/2501.17148, 2025. URL https://api. semanticscholar.org/CorpusID:275931808. Daniel Wurgaft, Can Rager, Matthew Kowal, Vasudev Shyam, Sheridan Feucht, Usha Bhalla, Tal Haklay, Eric J. Bigel","arxiv_match":false}],"id":"2501.17148","metadata_source":"Cached arXiv API metadata","rank":829},{"title":"Flow Q-Learning","arxiv_id":"2502.02538","year":2025,"url":"https://arxiv.org/abs/2502.02538","citing_submissions":122,"explicit_arxiv_matches":29,"examples":[{"id":"u3efKiCvkc","title":"Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent","forum_url":"https://openreview.net/forum?id=u3efKiCvkc","excerpt":"i, et al. Much ado about noising: Dispelling the myths of generative robotic control. In International Conference on Learning Representations (ICLR), 2026. Seohong Park, Qiyang Li, and Sergey Levine. Flow Q-learning. In International conference on machine learning (ICML), 2025. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on","arxiv_match":false},{"id":"b4JARCTPGU","title":"Improving Truncated First-Order Gradients for Flow Policies with Surrogate Objective","forum_url":"https://openreview.net/forum?id=b4JARCTPGU","excerpt":"wen Feng, and Angjoo Kanazawa. Flow Matching Policy Gradients. In International Conference on Learning Representations, volume 2026, pp. 36352–36372, 2026. Seohong Park, Qiyang Li, and Sergey Levine. Flow Q-Learning. In Proceedings of the 42nd International Conference on Machine Learning, pp. 48104–48127. PMLR, 2025. Michael Psenka, Alejandro Escontrela, Pieter Abbeel, and Yi Ma. Learning a Diffusion Model Polic","arxiv_match":false},{"id":"VH5qA6oJms","title":"Decoupling Policy Extraction for Offline Reinforcement Learning","forum_url":"https://openreview.net/forum?id=VH5qA6oJms","excerpt":": Benchmarking offline goal-conditioned RL. In International Conference on Learning Representations, 2025a. URL https://openreview.net/forum?id=M992mjgKzI. Seohong Park, Qiyang Li, and Sergey Levine. Flow Q-learning. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 48104–48127. PMLR, 2025b. URL https://proceedings.mlr.press/ v267","arxiv_match":false}],"id":"2502.02538","metadata_source":"Cached arXiv API metadata","rank":830},{"title":"GIFT-Eval: A benchmark for general time series forecasting model evaluation","arxiv_id":"2410.10393","year":2024,"url":"https://arxiv.org/abs/2410.10393","citing_submissions":122,"explicit_arxiv_matches":99,"examples":[{"id":"5667wgMum7","title":"Rethinking Federated Time Series Pretraining Through Shared Latent Dynamics","forum_url":"https://openreview.net/forum?id=5667wgMum7","excerpt":" Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, and Doyen Sahoo. Gift-eval: A benchmark for general time series forecasting model evaluation. arXiv preprint arXiv:2410.10393, 2024. Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen,","arxiv_match":true},{"id":"OdrD07JxzD","title":"Aurora-X: Built for Extreme Time Series Forecasting","forum_url":"https://openreview.net/forum?id=OdrD07JxzD","excerpt":" Pranjal Aggarwal and Sean Welleck. L1: Controlling how long a reasoning model thinks with reinforcement learning. arXiv preprint arXiv:2503.04697, 2025. Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, and Doyen Sahoo. Gift-eval: A benchmark for general time series forecasting model evaluation. arXiv preprint arXiv:2410.10393, 2024. Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin S","arxiv_match":true},{"id":"1mthTRpwBg","title":"QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities","forum_url":"https://openreview.net/forum?id=1mthTRpwBg","excerpt":"omas Pfister, and Jinsung Yoon. Reasoning-aware training for time series forecasting, 2026. Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, and Doyen Sahoo. GIFT-eval: A benchmark for general time series forecasting model evaluation. In NeurIPS Workshop on Time Series in the Age of Large Models, 2024. Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Syndar Ranga","arxiv_match":false}],"id":"2410.10393","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":831},{"title":"MiniCPM: Unveiling the potential of small language models with scalable training strategies","arxiv_id":"2404.06395","year":2024,"url":"https://arxiv.org/abs/2404.06395","citing_submissions":122,"explicit_arxiv_matches":82,"examples":[{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"/papers/v13/gretton12a.html. Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, Xinrong Zhang, Zheng Leng Thai, Kaihuo Zhang, Chongyi Wang, Yuan Yao, Chenyang Zhao, Jie Zhou, Jie Cai, Zhongwu Zhai, Ning Ding, Chao Jia, Guoyang Zeng, Dahai Li, Zhiyuan Liu, and Maosong Sun. Minicpm: Unveiling the potential of small language models with scalable training strategies, 2024. URL https://arxiv.org/abs/ 2404.06395. Yuheng Ji et al. Robobrain: A unified brain model for robotic manipulation from abstract to concre","arxiv_match":true},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"L https://openreview.net/forum? id=kIoBbc76Sy. Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, Xinrong Zhang, Zheng Leng Thai, Kaihuo Zhang, Chongyi Wang, Yuan Yao, Chenyang Zhao, Jie Zhou, Jie Cai, Zhongwu Zhai, Ning Ding, Chao Jia, Guoyang Zeng, Dahai Li, Zhiyuan Liu, and Maosong Sun. MiniCPM: Unveiling the potential of small language models with scalable training strategies. arXiv preprint arXiv:2404.06395, 2024. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Transformers are","arxiv_match":true},{"id":"popEknHwl3","title":"Angle-Scheduled Muon: Replacing Implicit Step Decay with an Explicit Schedule","forum_url":"https://openreview.net/forum?id=popEknHwl3","excerpt":"1. URL https://arxiv.org/abs/2010.11929. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. URL https://arxiv.org/abs/1512.03385. Shengding Hu, Yuge Tu, Xu Han, et al. MiniCPM: Unveiling the potential of small language models with scalable training strategies. arXiv preprint arXiv:2404.06395, 2024. URL https://arxiv.org/ abs/2404.06395. Florian Hübler, Kai Lion, Antonio Orvieto, and Niao He. Muown implicitly performs angular stepsize","arxiv_match":true}],"id":"2404.06395","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":832},{"title":"Molmo and PixMo: Open weights and open data for state-of-the-art vision-language models","arxiv_id":"2409.17146","year":2024,"url":"https://arxiv.org/abs/2409.17146","citing_submissions":122,"explicit_arxiv_matches":38,"examples":[{"id":"SsjhVTW9zo","title":"PhysCount: Do MLLMs Count Visual Appearances or Physical Entities?","forum_url":"https://openreview.net/forum?id=SsjhVTW9zo","excerpt":"ld, Crystal Nam, Sophie Lebrecht, Caitlin Wittlif, Carissa Schoenick, Oscar Michel, Ranjay Krishna, Luca Weihs, Noah A. Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, and Aniruddha Kembhavi. Molmo and pixmo: Open weights and open data for state-ofthe-art vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 91–104, June 2025. Jinhong Deng, Limeng Qiao, and Guanglu Wan. Holocount: A holistic visual counting ","arxiv_match":false},{"id":"gX30Aapm6u","title":"VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning","forum_url":"https://openreview.net/forum?id=gX30Aapm6u","excerpt":"ron Bischoff, Pete Walsh, Chris Newell, Piper Wolters, Tanmay Gupta, Kuo-Hao Zeng, Jon Borchardt, Dirk Groeneveld, Crystal Nam, Sophie Lebrecht, Caitlin Wittlif, Carissa Schoenick, Oscar Michel, Ranjay Krishna, Luca Weihs, Noah A. Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, and Aniruddha Kembhavi. Molmo and PixMo: Open weights and open data for state-of-the-art vision-language models. ArXiv preprint, abs/2409.17146, 2024. URL https://arxiv.org/abs/2409.17146. Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen, and Liang-Chieh Chen. COCONut: Modernizing COCO ","arxiv_match":true},{"id":"bp0HEA9om7","title":"Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters","forum_url":"https://openreview.net/forum?id=bp0HEA9om7","excerpt":"Systems, volume 36, pp. 49250–49267, 2023. Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, et al. Molmo and PixMo: Open weights and open data for state-of-the-art vision-language models. In Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. Shuaiqi Duan, Yadong Xue, Weihan Wang, Zhe Su, Huan Liu, Sheng Yang, Guobing Gan, Guo Wang, Zihan Wang, Shengdong Yan,","arxiv_match":false}],"id":"2409.17146","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":833},{"title":"R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning","arxiv_id":"2503.05592","year":2025,"url":"https://arxiv.org/abs/2503.05592","citing_submissions":122,"explicit_arxiv_matches":112,"examples":[{"id":"ROD8EjYEgO","title":"One Answer to Grade Them All? Reference-Based Evaluation and Its Optimization Effects in Search Agents","forum_url":"https://openreview.net/forum?id=ROD8EjYEgO","excerpt":", Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. URL https://arxiv.org/abs/2402. 03300. Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning, 2025. URL https://arxiv.org/abs/2503.05592. Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. ASQA: Factoid questions meet long-form","arxiv_match":true},{"id":"BOyTp7MiOV","title":"CAFE: Self-Improving Search Agents Need Co-Evolving Feedback","forum_url":"https://openreview.net/forum?id=BOyTp7MiOV","excerpt":"0-Abstract-Conference.html. Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart, Adi Ganesh, Ahmed El-Kishky, Aidan McLaughlin, Aiden Low, AJ Ostrow, Akhila Ananthram, et al. Openai gpt-5 system card. arXiv preprint arXiv:2601.03267, 2025. Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning, 2025. URL https://arxiv.org/abs/2503.05592. Richard S Sutton, Andrew G Barto, and Andrew Barto. Reinforcement learning: An introduction, volum","arxiv_match":true},{"id":"3FIUVX3ffG","title":"DatasetResearch: Benchmarking LLM Agents For Demand-Driven Dataset Discovery","forum_url":"https://openreview.net/forum?id=3FIUVX3ffG","excerpt":"82:109089, 2024. Ramneet Singh, Sathvik Joel, Abhav Mehrotra, Nalin Wadhwa, Ramakrishna B Bairi, Aditya Kanade, and Nagarajan Natarajan. Code researcher: Deep research agent for large systems code and commit history. arXiv preprint arXiv:2506.11060, 2025. Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning. arXiv preprint arXiv:2503.05592, 2025. Dilara Soylu, Christopher Potts, and Omar Khattab. Fine-tuning and prompt optimization: Two ","arxiv_match":true}],"id":"2503.05592","metadata_source":"Cached arXiv API metadata","rank":834},{"title":"ShapeNet: An information-rich 3D model repository","arxiv_id":"1512.03012","year":2015,"url":"https://arxiv.org/abs/1512.03012","citing_submissions":122,"explicit_arxiv_matches":110,"examples":[{"id":"Y2v536n2IW","title":"3DSMT++: Towards High-Performance Spike-Driven Point Cloud Learning","forum_url":"https://openreview.net/forum?id=Y2v536n2IW","excerpt":"olfo Vargas Hakim, David Osowiechi, Farzad Beizaee, Ismail Ben Ayed, and Christian Desrosiers. Spectral informed mamba for robust point cloud processing. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 11799–11809, 2025. Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. Guangyan Chen, Meiling Wang, Yi Yang, Kai Yu, Li Yuan, and Yufeng Yue. Pointgpt: Autoregress","arxiv_match":true},{"id":"DmH3HpzlV6","title":"From Parts to Sim: Bottom-Up Autoregressive Generation of  Sim-Ready 3D Assets","forum_url":"https://openreview.net/forum?id=DmH3HpzlV6","excerpt":"nd Ziwei Liu. PhysX-Omni: Unified simulation-ready physical 3D generation for rigid, deformable, and articulated objects. arXiv preprint arXiv:2605.21572, 2026b. URL https://arxiv. org/abs/2605.21572. Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. ShapeNet: An information-rich 3D model repository, 2015. URL https://arxiv.org/ abs/1512.03012. Dave Zhenyu Chen, Yawar Siddiqui, Hsin-Ying Lee, Sergey Tulyakov, and Matthias Nießner. Text2Tex: ","arxiv_match":true},{"id":"aQCPBUW7E7","title":"The Wittgensteinian Representation Hypothesis: Is Language the Effective Reference Geometry of Multimodal Alignment?","forum_url":"https://openreview.net/forum?id=aQCPBUW7E7","excerpt":"eed. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026. Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, et al. Internlm2 technical report. arXiv preprint arXiv:2403.17297, 2024. Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. Guangyan Chen, Meiling Wang, Yi Yang, Kai Yu, Li Yuan, and Yufeng Yue. Pointgpt: Autoregress","arxiv_match":true}],"id":"1512.03012","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":835},{"title":"Simplicity prevails: Rethinking negative preference optimization for LLM unlearning","arxiv_id":"2410.07163","year":2024,"url":"https://arxiv.org/abs/2410.07163","citing_submissions":122,"explicit_arxiv_matches":30,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":"y, Abhinav Jauhri, Abhinav Pandey, et al. The Llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, and Sijia Liu. Simplicity prevails: Rethinking negative preference optimization for LLM unlearning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https: //openreview.net/forum?id=JbvSQm5h1l. Lennart Finke, Chandan Sreedhara, Thomas Dooms, Mat Allen, Juan","arxiv_match":false},{"id":"bdZAOqTSho","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","forum_url":"https://openreview.net/forum?id=bdZAOqTSho","excerpt":"ning resilient to relearning attacks: A sharpness-aware minimization perspective and beyond. In ICML, 2025a. Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, and Sijia Liu. Simplicity prevails: Rethinking negative preference optimization for LLM unlearning. In NeurIPS, 2025b. Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, and Tom Goldstein. Adversarial examples make strong poisons. In NeurIPS, 2021. Shaopeng Fu, Fengxiang He","arxiv_match":false},{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":"zlyab. Constrained entropic unlearning: A primal-dual framework for large language models. In NeurIPS, 2025. Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, and Sijia Liu. Simplicity prevails: Rethinking negative preference optimization for LLM unlearning. In NeurIPS, 2025. Gemma Team. Gemma 4 Technical Report. arXiv preprint arXiv:2607.02770, 2026. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Ai","arxiv_match":false}],"id":"2410.07163","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":836},{"title":"What learning algorithm is in-context learning? investigations with linear models","arxiv_id":"2211.15661","year":2022,"url":"https://arxiv.org/abs/2211.15661","citing_submissions":122,"explicit_arxiv_matches":33,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":" Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. What Learning Algorithm Is in-Context Learning? Investigations with Linear Models. In International Conference on Learning Representations (ICLR), 2023. A. J. Ångström. New Method of Determining the Thermal Conductibility of Bodies. The London Edinburgh and Dublin Philosophical Ma","arxiv_match":false},{"id":"25DdNVKXg6","title":"Shared Computation, Different Readouts: Depth Compatibility in Looped Transformers","forum_url":"https://openreview.net/forum?id=25DdNVKXg6","excerpt":" Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. What learning algorithm is in-context learning? Investigations with linear models. In International Conference on Learning Representations, 2023. Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv:1610.01644, 2","arxiv_match":false},{"id":"fVlHzXO4UN","title":"When Does a Novel Form Become a Word? Causal Evidence for Reusable Lexical Function in Frozen Language Models","forum_url":"https://openreview.net/forum?id=fVlHzXO4UN","excerpt":" Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. What learning algorithm is in-context learning? investigations with linear models. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id= 0g0X4H8yN4I. Robin Algayres, Charles-Éric Saint-James, Mahi Luthra, Jiayi Shen, Dongyan Lin, Youss","arxiv_match":false}],"id":"2211.15661","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":837},{"title":"Generating long sequences with sparse transformers","arxiv_id":"1904.10509","year":2019,"url":"https://arxiv.org/abs/1904.10509","citing_submissions":121,"explicit_arxiv_matches":114,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":", Wayne Xiong, Yue Dong, Junjie Hu, and Wen Xiao. PyramidKV: Dynamic KV cache compression based on pyramidal information funneling. In Second Conference on Language Modeling, 2025. Vivek Chari, Guanghui Qin, and Benjamin Van Durme. KV-Distill: Nearly lossless learnable context compression for LLMs. arXiv preprint arXiv:2503.10337, 2025. Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509, 2019. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristin","arxiv_match":true},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"ig, Anirudh Ajith, and Danqi Chen. Adapting language models to compress contexts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 3829–3846, 2023. Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025. Rewon Child. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509, 2019. Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and mem","arxiv_match":true},{"id":"NwY6n95NIv","title":"CoWindow Attention: Full Causal Coverage Is a Collective Property","forum_url":"https://openreview.net/forum?id=NwY6n95NIv","excerpt":"004.05150, 2020. Loubna Ben Allal, Anton Lozhkov, Guilherme Penedo, Thomas Wolf, and Leandro von Werra. Smollm-corpus, July 2024. Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. PIQA: Reasoning about physical commonsense in natural language. In Proceedings of the AAAI conference on Artificial Intelligence, volume 34, 2020. Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509, 2019. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, a","arxiv_match":true}],"id":"1904.10509","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":838},{"title":"L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning","arxiv_id":"2503.04697","year":2025,"url":"https://arxiv.org/abs/2503.04697","citing_submissions":121,"explicit_arxiv_matches":79,"examples":[{"id":"m61N49mzzc","title":"Improving Test-Time Scaling with Adaptive Looped Transformers","forum_url":"https://openreview.net/forum?id=m61N49mzzc","excerpt":" a-m-team. Am-qwen3-distilled. https://huggingface.co/datasets/a-m-team/ AM-Qwen3-Distilled, 2025. Awni Altabaa, Siyu Chen, John Lafferty, and Zhuoran Yang. Unlocking Out-of-Distribution Generalization in Transformers via Recursive Latent Space Reasoning. arXiv preprint arXiv:2510.14095, 2025. URL https://arxiv.org/abs/2510.14095. Pranjal Aggarwal and Sean Welleck. L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning. arXiv preprint arXiv:2503.04697, 2025. URL https://arxiv. org/abs/2503.04697. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bo","arxiv_match":true},{"id":"OdrD07JxzD","title":"Aurora-X: Built for Extreme Time Series Forecasting","forum_url":"https://openreview.net/forum?id=OdrD07JxzD","excerpt":" Pranjal Aggarwal and Sean Welleck. L1: Controlling how long a reasoning model thinks with reinforcement learning. arXiv preprint arXiv:2503.04697, 2025. Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, a","arxiv_match":true},{"id":"qeKZk5n57a","title":"Reward-Gradient Aligned Low-Rank Adaptation for Post-Training","forum_url":"https://openreview.net/forum?id=qeKZk5n57a","excerpt":" Pranjal Aggarwal and Sean Welleck. L1: Controlling how long a reasoning model thinks with reinforcement learning. In Conference on Language Modeling, 2025. Alon Albalak, Duy Phung, Nathan Lile, Rafael Rafailov, Kanishk Gandhi, Louis Castricato, Anikait Singh, Chase Blagden, Violet Xiang, Dakota Mahan, and Nick ","arxiv_match":false}],"id":"2503.04697","metadata_source":"Cached arXiv API metadata","rank":839},{"title":"MapAnything: Universal Feed-Forward Metric 3D Reconstruction","arxiv_id":"2509.13414","year":2025,"url":"https://arxiv.org/abs/2509.13414","citing_submissions":121,"explicit_arxiv_matches":28,"examples":[{"id":"dmWiArTSoB","title":"Validation over Contexts: Robust Long-Context Inference for 3D Reconstruction Models","forum_url":"https://openreview.net/forum?id=dmWiArTSoB","excerpt":"her, Arno Knapitsch, Duncan Zauss, Ethan Weber, Nelson Antunes, Jonathon Luiten, Manuel Lopez-Antequera, Samuel Rota Bulò, Christian Richardt, Deva Ramanan, Sebastian Scherer, and Peter Kontschieder. MapAnything: Universal feed-forward metric 3D reconstruction. In 3DV, 2026. Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM TOG, 42(4):139–1, 2023. Jonas Kulhanek, Song","arxiv_match":false},{"id":"gXl1y5L562","title":"CLASP: Complementary Chunk and Long Passes for Feed-Forward 3D Reconstruction","forum_url":"https://openreview.net/forum?id=gXl1y5L562","excerpt":"her, Arno Knapitsch, Duncan Zauss, Ethan Weber, Nelson Antunes, Jonathon Luiten, Manuel Lopez-Antequera, Samuel Rota Bulò, Christian Richardt, Deva Ramanan, Sebastian Scherer, and Peter Kontschieder. MapAnything: Universal feed-forward metric 3D reconstruction. In 3DV, 2026. Rainer Kümmerle, Giorgio Grisetti, Hauke Strasdat, Kurt Konolige, and Wolfram Burgard. g2o: A general framework for graph optimization. In ICRA, 2011. Yushi Lan, Yihang Luo, Fangzhou H","arxiv_match":false},{"id":"0AaiHNGPSg","title":"TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction","forum_url":"https://openreview.net/forum?id=0AaiHNGPSg","excerpt":". 5322–5332, 2024. Angjoo Kanazawa, Shubham Tulsiani, Alexei A Efros, and Jitendra Malik. Learning category-specific mesh reconstruction from image collections. In European Conference on Computer Vision, pp. 371–386, 2018. Nikhil Keetha, Norman Müller, Johannes Schönberger, Lorenzo Porzi, Yuchen Zhang, Tobias Fischer, Arno Knapitsch, Duncan Zauss, Ethan Weber, Nelson Antunes, et al. Mapanything: Universal feed-forward metric 3d reconstruction. arXiv preprint arXiv:2509.13414, 2025. Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian spla","arxiv_match":true}],"id":"2509.13414","metadata_source":"Cached arXiv API metadata","rank":840},{"title":"ProgPrompt: Generating situated robot task plans using large language models","arxiv_id":"2209.11302","year":2022,"url":"https://arxiv.org/abs/2209.11302","citing_submissions":121,"explicit_arxiv_matches":56,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":" Jul 2025b. URL https://proceedings.mlr.press/v267/shi25d.html. Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using large language models. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 11523–11530, 2023. doi: 10.1109/ICRA48891.2023.10161317. Richard S. Sutton, Doina Precup, and Satinder Singh. Between mdp","arxiv_match":false},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"ational Conference on Machine Learning, 2025. arXiv:2502.19417. Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using large language models. In ICRA, 2023. Yinzhou Tang, Jingbo Xu, Yu Shang, Zihao Song, Chen Gao, Wei Wu, and Yong Li. Dreaming when necessary: Advancing world action models with adaptive multi-modal reasoning, 2026. URL htt","arxiv_match":false},{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"6. IEEE, 2020a. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768, 2020b. Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using large language models. arXiv preprint arXiv:2209.11302, 2022. Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su. Llm-p","arxiv_match":true}],"id":"2209.11302","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":841},{"title":"TIP: Token Importance in On-Policy Distillation","arxiv_id":"2604.14084","year":2026,"url":"https://arxiv.org/abs/2604.14084","citing_submissions":121,"explicit_arxiv_matches":117,"examples":[{"id":"l6TjLrpsyc","title":"AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=l6TjLrpsyc","excerpt":"reprint arXiv:2412.04141, 2024. Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, and Kai Yu. Alignment for efficient tool calling of large language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 17787–17803, 2025. Yuanda Xu, Hejian Sang, Zhengze Zhou, Ran He, Zhipeng Wang, and Alborz Geramifard. Tip: Token importance in on-policy distillation, 2026. URL https://arxiv.org/abs/2604.14084. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Cheng","arxiv_match":true},{"id":"iHdUmEIkbu","title":"Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability","forum_url":"https://openreview.net/forum?id=iHdUmEIkbu","excerpt":"nal Linguistics, 2026. doi: 10.18653/V1/2026.FINDINGS-EACL.181. URL https://doi.org/10.18653/v1/ 2026.findings-eacl.181. Hao Wang, Kun Yuan, Wenlin Zhong, Minglei Zhang, Han Xiao, Ming Sun, and Honggang Qi. Cross-tokenizer on-policy distillation via byte-prefix marginalization. arXiv preprint arXiv:2607.22334, 2026. Yuanda Xu, Hejian Sang, Zhengze Zhou, Ran He, Zhipeng Wang, and Alborz Geramifard. Tip: Token importance in on-policy distillation. arXiv preprint arXiv:2604.14084, 2026. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao,","arxiv_match":true},{"id":"nStFNVfn07","title":"Matching While Alive: A Survival View of On-Policy Trajectory Distillation","forum_url":"https://openreview.net/forum?id=nStFNVfn07","excerpt":"ng T. Le, Dhruv Madeka, Lei Li, William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, and Tomas Pfister. Speculative knowledge distillation: Bridging the teacher-student gap through interleaved sampling. In International Conference on Learning Representations, 2025. URL https://arxiv.org/abs/2410.11325. Yuanda Xu, Hejian Sang, Zhengze Zhou, Ran He, Zhipeng Wang, and Alborz Geramifard. TIP: Token importance in on-policy distillation, 2026. URL https://arxiv.org/abs/2604. 14084. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Cheng","arxiv_match":true}],"id":"2604.14084","metadata_source":"Cached arXiv API metadata","rank":842},{"title":"Unifying distillation and privileged information","arxiv_id":"1511.03643","year":2015,"url":"https://arxiv.org/abs/1511.03643","citing_submissions":121,"explicit_arxiv_matches":74,"examples":[{"id":"ZFCw6a3QKH","title":"Not All Privilege Is Distillable: Calibrating Teacher Corrections for Few-Step Video Generation","forum_url":"https://openreview.net/forum?id=ZFCw6a3QKH","excerpt":"On-policy self-distillation for post-training few-step autoregressive video generators. arXiv preprint arXiv:2607.08766, 2026. David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, and Vladimir Vapnik. Unifying distillation and privileged information. In International Conference on Learning Representations, 2016. Yang Luo, Shengju Qian, Xiaohang Tang, Zirui Zhu, Yong Liu, Xin Wang, and Yang You. On-policy adversarial flow distillation for autoreg","arxiv_match":false},{"id":"H7jtK9Q0qc","title":"What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=H7jtK9Q0qc","excerpt":"ings of Machine Learning Research, pp. 37889–37905. PMLR, 13–19 Jul 2025. URL https://proceedings.mlr.press/v267/lin25i. html. David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, and Vladimir Vapnik. Unifying distillation and privileged information. In International Conference on Learning Representations, pp. 1–10, 2016. Kevin Lu and Thinking Machines Lab. On-policy distillation. Thinking Machines Lab: Connectionism, 2025. doi: 10.64434/tml.202","arxiv_match":false},{"id":"shAJwVMuKt","title":"SHIFT: Progressive Feedback for Time Series Forecasting","forum_url":"https://openreview.net/forum?id=shAJwVMuKt","excerpt":"n Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, volume 258, pp. 3430–3438, 2025. David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, and Vladimir Vapnik. Unifying distillation and privileged information. In International Conference on Learning Representations, 2016. Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with transfo","arxiv_match":false}],"id":"1511.03643","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":843},{"title":"A General Framework for Inference-time Scaling and Steering of Diffusion Models","arxiv_id":"2501.06848","year":2025,"url":"https://arxiv.org/abs/2501.06848","citing_submissions":120,"explicit_arxiv_matches":51,"examples":[{"id":"Wgqx6XoFe1","title":"ReCAT: Recurrent Composable Activation Transition for Test-Time Scaling of Diffusion Models","forum_url":"https://openreview.net/forum?id=Wgqx6XoFe1","excerpt":"nn. Laion-aesthetics. LAION Blog, August 2022. URL https://laion. ai/blog/laion-aesthetics/. Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, and Rajesh Ranganath. A general framework for inference-time scaling and steering of diffusion models. In Forty-second International Conference on Machine Learning, 2025. URL https: //openreview.net/forum?id=Jp988ELppQ. Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling llm test-time co","arxiv_match":false},{"id":"loGYX2xRu1","title":"Correction Training for Diffusion Models","forum_url":"https://openreview.net/forum?id=loGYX2xRu1","excerpt":"nce on Learning Representations, 2024. Assaf Shocher, Amil Dravid, Yossi Gandelsman, Inbar Mosseri, Michael Rubinstein, and Alexei Efros. Idempotent generative network. In International Conference on Learning Representations, volume 2024, pp. 35440–35460, 2024. Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, and Rajesh Ranganath. A general framework for inference-time scaling and steering of diffusion models. arXiv preprint arXiv:2501.06848, 2025. Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervise","arxiv_match":true},{"id":"WTR2ae8gmI","title":"RFG: Self-Improving Diffusion Large Language Models with Reward-Free Guidance","forum_url":"https://openreview.net/forum?id=WTR2ae8gmI","excerpt":"uage models. arXiv preprint arXiv:2402.03300, 2024. Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis Titsias. Simplified and generalized masked diffusion for discrete data. Advances in neural information processing systems, 37: 103131–103167, 2024. Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, and Rajesh Ranganath. A general framework for inference-time scaling and steering of diffusion models. arXiv preprint arXiv:2501.06848, 2025. Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervise","arxiv_match":true}],"id":"2501.06848","metadata_source":"Cached arXiv API metadata","rank":844},{"title":"Attribution patching outperforms automated circuit discovery","arxiv_id":"2310.10348","year":2023,"url":"https://arxiv.org/abs/2310.10348","citing_submissions":120,"explicit_arxiv_matches":33,"examples":[{"id":"WmdhapRKFO","title":"Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits","forum_url":"https://openreview.net/forum?id=WmdhapRKFO","excerpt":"hine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 3319–3328. PMLR, 2017. URL https://proceedings. mlr.press/v70/sundararajan17a.html. Aaquib Syed, Can Rager, and Arthur Conmy. Attribution patching outperforms automated circuit discovery. In Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 407–416. Association for Computational Linguistics, 2024. doi: 10.18653/v1/2024.blackboxnlp-1.","arxiv_match":false},{"id":"CONFJT1Bkx","title":"Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning","forum_url":"https://openreview.net/forum?id=CONFJT1Bkx","excerpt":"g big-bench tasks and whether chain-of-thought can solve them. In Findings of the Association for Computational Linguistics: ACL 2023, pp. 13003–13051, 2023. Aaquib Syed, Can Rager, and Arthur Conmy. Attribution patching outperforms automated circuit discovery. In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen (eds.), Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP","arxiv_match":false},{"id":"xUHg2Gbaoo","title":"Before The Question Ends: Provisional Answer Formation In Language Models","forum_url":"https://openreview.net/forum?id=xUHg2Gbaoo","excerpt":" system. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pp. 194–199, 2023. Aaquib Syed, Can Rager, and Arthur Conmy. Attribution patching outperforms automated circuit discovery. In Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 407–416, 2024. Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yeji","arxiv_match":false}],"id":"2310.10348","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":845},{"title":"Efficient lifelong learning with a-gem","arxiv_id":"1812.00420","year":2018,"url":"https://arxiv.org/abs/1812.00420","citing_submissions":120,"explicit_arxiv_matches":36,"examples":[{"id":"AfJJPX455e","title":"Optimal Training-Time Scaling in Gradual Adaptation","forum_url":"https://openreview.net/forum?id=AfJJPX455e","excerpt":"tificial Intelligence and Statistics, volume 151 of Proceedings of Machine Learning Research, pp. 2372–2396. PMLR, 2022. Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. Efficient lifelong learning with A-GEM. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=Hkf2_sC5FX. Hong-You Chen and Wei-Lun Chao. Gradual domain adaptation without indexed intermediate ","arxiv_match":false},{"id":"9G6X8auUdk","title":"Recursive Continual Post-Training of Foundation Models","forum_url":"https://openreview.net/forum?id=9G6X8auUdk","excerpt":"Haiyang Xu, and Junyang Lin. Qwen2.5-VL technical report. arXiv preprint arXiv:2502.13923, 2025. Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara. Dark experience for general continual learning: a strong, simple baseline. Advances in neural information processing systems, 33:15920–15930, 2020. Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. Efficient lifelong learning with A-GEM. arXiv preprint arXiv:1812.00420, 2018. Cheng Chen, Junchen Zhu, Xu Luo, Heng Tao Shen, Jingkuan Song, and Lianli Gao. CoIN: A bench","arxiv_match":true},{"id":"fogSHYC0sZ","title":"Local Support Learning","forum_url":"https://openreview.net/forum?id=fogSHYC0sZ","excerpt":"aniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners, 2020. URL https://arxiv.org/abs/2005.14165. Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. Efficient lifelong learning with a-gem, 2019a. URL https://arxiv.org/abs/1812.00420. Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K. Dokania, P","arxiv_match":true}],"id":"1812.00420","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":846},{"title":"MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent","arxiv_id":"2507.02259","year":2025,"url":"https://arxiv.org/abs/2507.02259","citing_submissions":120,"explicit_arxiv_matches":61,"examples":[{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":". URL https://openreview.net/forum? id=WE_vluYUL-X. Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, YaQin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, and Hao Zhou. MemAgent: Reshaping long-context LLM with multi-conv RL-based memory agent. In The Fourteenth International Conference on Learning Representations, 2026a. URL https://proceedings.iclr.cc/paper_files/paper/2026/file/ 4264ee4376776907c0b87ed70b959585-Paper-Conference.pdf. Qiy","arxiv_match":false},{"id":"1V3066nDJF","title":"Contract Memory Compiler: Resolve, Then Traverse","forum_url":"https://openreview.net/forum?id=1V3066nDJF","excerpt":"25161, 2026. URL https://arxiv.org/abs/2606.25161. Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, and Hao Zhou. MemAgent: Reshaping long-context LLM with multi-conv RL-based memory agent. In International Conference on Learning Representations, 2026a. URL https://proceedings.iclr.cc/paper_files/paper/2026/hash/ 4264ee4376776907c0b87ed70b959585-Abstract-Conference.html. Yi Yu, Liuyi Y","arxiv_match":false},{"id":"1kdyECT6ST","title":"Credit Follows Use: Used-Grouped Policy Optimization  for Long-Context Memory Agents","forum_url":"https://openreview.net/forum?id=1kdyECT6ST","excerpt":"els, 2023b. URL https://arxiv. org/abs/2210.03629. Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, and Hao Zhou. Memagent: Reshaping long-context LLM with multi-conv rl-based memory agent. CoRR, abs/2507.02259, 2025. doi: 10.48550/ ARXIV.2507.02259. URL https://doi.org/10.48550/arXiv.2507.02259. Yanwei Yue, Boci Peng, Xuanbo Fan, Jiaxin Guo, Qiankun Li, and Yan Zhang. Mem-t: Densifyin","arxiv_match":false}],"id":"2507.02259","metadata_source":"Cached arXiv API metadata","rank":847},{"title":"MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction","arxiv_id":"2604.27393","year":2026,"url":"https://arxiv.org/abs/2604.27393","citing_submissions":120,"explicit_arxiv_matches":108,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"Dahua Lin, and Feng Zhao. Are we on the right way for evaluating large visionlanguage models? In Advances in Neural Information Processing Systems, volume 37, 2024. URL https://arxiv.org/abs/2403.20330. Junbo Cui, Bokai Xu, Chongyi Wang, Tianyu Yu, Weiyue Sun, Yingjing Xu, Tianran Wang, Zhihui He, Wenshuo Ma, Tianchi Cai, et al. MiniCPM-o 4.5: Towards Real-Time Full-Duplex OmniModal Interaction. arXiv preprint arXiv:2604.27393, 2026. URL https://arxiv.org/ abs/2604.27393. Ronghao Dang, Yuqian Yuan, Wenqi Zhang, Yifei Xin, Boqiang Zhang, Long Li, Liuyi Wang, Qinyang Zen","arxiv_match":true},{"id":"eeqPXcVTnJ","title":"MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy","forum_url":"https://openreview.net/forum?id=eeqPXcVTnJ","excerpt":"IPS, 2024. Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shiliang Zhang, Zhijie Yan, Chang Zhou, and Jingren Zhou. Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models. arXiv preprint arXiv:2311.07919, 2023. Junbo Cui, Bokai Xu, Chongyi Wang, Tianyu Yu, Weiyue Sun, Yingjing Xu, Tianran Wang, Zhihui He, Wenshuo Ma, Tianchi Cai, et al. Minicpm-o 4.5: Towards real-time full-duplex omni-modal interaction. arXiv preprint arXiv:2604.27393, 2026. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Z","arxiv_match":true},{"id":"Fqv6UwosZ0","title":"From Instruction to Event: Sound-Triggered Mobile Manipulation","forum_url":"https://openreview.net/forum?id=Fqv6UwosZ0","excerpt":"ion platform for visual-acoustic learning. NeurIPS, 35:8896–8911, 2022. Junbo Cui, Bokai Xu, Chongyi Wang, Tianyu Yu, Weiyue Sun, Yingjing Xu, Tianran Wang, Zhihui He, Wenshuo Ma, Tianchi Cai, et al. Minicpm-o 4.5: Towards real-time full-duplex omni-modal interaction, 2026. Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave, and Neil Zeghidour. Moshi: a speech-text foundation model for real-time dialogue","arxiv_match":false}],"id":"2604.27393","metadata_source":"Cached arXiv API metadata","rank":848},{"title":"TabPFN-3: Technical Report","arxiv_id":"2605.13986","year":2026,"url":"https://arxiv.org/abs/2605.13986","citing_submissions":120,"explicit_arxiv_matches":109,"examples":[{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":"e Wehrhahn, Diana Kriuchkova, Kursat Kaya, Lydia Sidhoum, Marie Salmon, Jerry Chen, Madelon Hulsebos, Yann LeCun, Samuel Müller, Bernhard schoelkopf, Sauraj Gambhir, Noah Hollmann, and Frank Hutter. TabPFN-3: Technical Report, 2026. Yuanyuan Guo, Xiaoda Niu, and Harry Zhang. An extensive empirical study on semi-supervised learning. In 2010 IEEE International Conference on Data Mining, pp. 186–195. IEEE, 2010. Noah Hollman","arxiv_match":false},{"id":"sGVZRmZQzK","title":"Amplifying Privacy Attacks on Tabular Foundation Models via a Stolen Surrogate","forum_url":"https://openreview.net/forum?id=sGVZRmZQzK","excerpt":"tte Wehrhahn, Diana Kriuchkova, Kursat Kaya, Lydia Sidhoum, Marie Salmon, Jerry Chen, Madelon Hulsebos, Yann LeCun, Samuel Müller, Bernhard Schölkopf, Sauraj Gambhir, Noah Hollmann, and Frank Hutter. Tabpfn-3: Technical report. CoRR, abs/2605.13986, 2026. doi: 10.48550/ARXIV.2605.13986. URL https://doi.org/10.48550/arXiv.2605.13986. Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. Tabpfn: A transform","arxiv_match":false},{"id":"L0Kgn7Uyx4","title":"Differentially Private Adaptations for Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=L0Kgn7Uyx4","excerpt":" Adrian Hayler, Vladyslav Moroshan, Lennart Purucker, Philipp Singer, Alan Arazi, Julien Siems, Jan Hendrik Metzen, Georg Grab, Nick Erickson, Siyuan Guo, Eliott Kalfon, Simon Bing, David Salinas, Clara Cornu, Lilly Charlotte Wehrhahn, Diana Kriuchkova, Kursat Kaya, Lydia Sidhoum, Marie Salmon, Jerry Chen, Madelon Hulsebos, Yann LeCun, Samuel Müller, Bernhard Schölkopf, Sauraj Gambhir, Noah Hollmann, and Frank Hutter. TabPFN-3: Technical Report. arXiv:2605.13986, 2026b. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving Deep into Rectifiers: Surpas","arxiv_match":true}],"id":"2605.13986","metadata_source":"Cached arXiv API metadata","rank":849},{"title":"DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments","arxiv_id":"2504.03160","year":2025,"url":"https://arxiv.org/abs/2504.03160","citing_submissions":119,"explicit_arxiv_matches":35,"examples":[{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":" Zou, and Kunle Olukotun. Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models. In International Conference on Learning Representations, 2026. https://arxiv.org/abs/2510.04618. URL Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu. DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments. arXiv preprint arXiv:2504.03160, 2025. URL https://arxiv.org/abs/2504. 03160. Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low, a","arxiv_match":true},{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":"stics, 2025. doi: 10.18653/V1/2025.EMNLP-MAIN.184. URL https://doi.org/10.18653/v1/2025.emnlp-main.184. Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu. Deepresearcher: Scaling deep research via reinforcement learning in real-world environments. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, EMNLP 2025, Suzhou, ","arxiv_match":false},{"id":"BOyTp7MiOV","title":"CAFE: Self-Improving Search Agents Need Co-Evolving Feedback","forum_url":"https://openreview.net/forum?id=BOyTp7MiOV","excerpt":"s, 2025a. doi: 10.18653/ V1/2025.EMNLP-MAIN.1106. URL https://doi.org/10.18653/v1/2025.emnlp-main.1106. Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu. Deepresearcher: Scaling deep research via reinforcement learning in real-world environments. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, EMNLP 2025, Suzhou, ","arxiv_match":false}],"id":"2504.03160","metadata_source":"Cached arXiv API metadata","rank":850},{"title":"Density estimation using real NVP","arxiv_id":"1605.08803","year":2016,"url":"https://arxiv.org/abs/1605.08803","citing_submissions":119,"explicit_arxiv_matches":37,"examples":[{"id":"JJjf2Sj6zX","title":"Coupled Subsets for Self-Supervised Point Cloud Denoising from a Single Scan","forum_url":"https://openreview.net/forum?id=JJjf2Sj6zX","excerpt":" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13530–13539, 2023b. Dasith de Silva Edirimuni, Xuequan Lu, Gang Li, Lei Wei, Antonio Robles-Kelly, and Hongdong Li. Straightpcf: Straight point cloud filtering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20721–20730, 2024. Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016. Bradley Efron. Tweedie’s formula and selection bias. Journal of the American Statistical Ass","arxiv_match":true},{"id":"WsLUWEDwZr","title":"Amortized Moment Matching for Visual Generation","forum_url":"https://openreview.net/forum?id=WsLUWEDwZr","excerpt":"ying Chen, Zhenyang Cai, Pengcheng Chen, Shunian Chen, Ke Ji, Xidong Wang, Yunjin Yang, and Benyou Wang. Sharegpt-4o-image: Aligning multimodal models with gpt-4o-level image generation. arXiv preprint arXiv:2506.18095, 2025b. Mingyang Deng, He Li, Tianhong Li, Yilun Du, and Kaiming He. Generative modeling via drifting. arXiv preprint arXiv:2602.04770, 2026. Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016. Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani. Training generative neural ","arxiv_match":true},{"id":"poa9kyljtV","title":"Improving Multimodal MCMC with Learned Proposal Mixtures","forum_url":"https://openreview.net/forum?id=poa9kyljtV","excerpt":"ity-constrained autoregressive cum conditional normalizing flow for lattice field theory simulations, 2023. URL https://arxiv.org/abs/2304.01798. Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using Real NVP. In International Conference on Learning Representations, 2017. URL https://openreview.net/forum? id=HkpbnH9lx. Laurent Dinh, Jascha Sohl-Dickstein, Hugo Larochelle, and Razvan Pascanu. A RAD approac","arxiv_match":false}],"id":"1605.08803","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":851},{"title":"G-designer: Architecting multi-agent communication topologies via graph neural networks","arxiv_id":"2410.11782","year":2024,"url":"https://arxiv.org/abs/2410.11782","citing_submissions":119,"explicit_arxiv_matches":45,"examples":[{"id":"0XTyMGNG5R","title":"ReLay: Are We Ready for Latent Routing for Multi-Agent Systems?","forum_url":"https://openreview.net/forum?id=0XTyMGNG5R","excerpt":"earning Representations, 2025c. URL https://openreview.net/forum?id=LkzuPorQ5L. Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. G-Designer: Architecting Multi-Agent Communication Topologies via Graph Neural Networks. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 76678– 76692. PMLR, 2025d. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu,","arxiv_match":false},{"id":"SLX4vJZTON","title":"ScoutCrew: Coordinating Multi-View Exploration and Evidence Synthesis for Spatial Reasoning","forum_url":"https://openreview.net/forum?id=SLX4vJZTON","excerpt":". arXiv preprint arXiv:2602.08236, 2026. URL https://arxiv.org/abs/2602. 08236. Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. G-Designer: Architecting multi-agent communication topologies via graph neural networks. In Proceedings of the 42nd International Conference on Machine Learning, volume 267, pp. 76678–76692. PMLR, 2025. URL https: //proceedings.mlr.press/v267/zhang25cu.html. Jensen Zhou, Hang Gao, Vikra","arxiv_match":false},{"id":"jfq0Rcq97f","title":"Learning Role-Context Graph Policies for Adaptive Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=jfq0Rcq97f","excerpt":"al Conference on Learning Representations, volume 2025, pp. 75389–75428, 2025c. Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. G-designer: Architecting multi-agent communication topologies via graph neural networks. In The International Conference on Machine Learning, 2025d. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, et al.","arxiv_match":false}],"id":"2410.11782","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":852},{"title":"MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents","arxiv_id":"2602.02474","year":2026,"url":"https://arxiv.org/abs/2602.02474","citing_submissions":119,"explicit_arxiv_matches":108,"examples":[{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"v preprint arXiv:2511.21087, 2025. Chi Zhang, Zhao Yang, Jiaxuan Liu, Yanda Li, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents as smartphone users. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp. 1–20, 2025. Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, and Wenya Wang. Memskill: Learning and evolving memory skills for self-evolving agents. arXiv preprint arXiv:2602.02474, 2026. Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, and Yu Su. Gpt-4v (ision) is a generalist web","arxiv_match":true},{"id":"GGUmZybpbd","title":"AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?","forum_url":"https://openreview.net/forum?id=GGUmZybpbd","excerpt":"n, and James Zou. Optimizing generative ai by backpropagating language model feedback. Nature, 639(8055):609–616, 2025. Dengjia Zhang, Xiaoou Liu, Lu Cheng, Yaqing Wang, Kenton Murray, and Hua Wei. Selaur: Self evolving llm agent via uncertainty-aware rewards. In Proc. PAKDD, 2026a. Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, and Wenya Wang. Memskill: Learning and evolving memory skills for self-evolving agents. arXiv preprint arXiv:2602.02474, 2026b. Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, ","arxiv_match":true},{"id":"WaybMSS9Gr","title":"Every Token Counts: Online Memory Worth for Resource-Constrained Lifelong Agents","forum_url":"https://openreview.net/forum?id=WaybMSS9Gr","excerpt":"on for self-evolving agents. arXiv preprint arXiv:2602.01966, 2026. Liangqi Yuan, Wenzhi Fang, Shiqiang Wang, H Vincent Poor, and Christopher G Brinton. Large language models over networks: Collaborative intelligence under resource constraints. arXiv preprint arXiv:2605.08626, 2026. Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, and Wenya Wang. Memskill: Learning and evolving memory skills for self-evolving agents. arXiv preprint arXiv:2602.02474, 2026a. Haozhen Zhang, Haodong Yue, Tao Feng, Quanyu Long, Jianzhu Bao, Bowen Jin, Weizhi Zhang, Xi","arxiv_match":true}],"id":"2602.02474","metadata_source":"Cached arXiv API metadata","rank":853},{"title":"Scaling data-constrained language models","arxiv_id":"2305.16264","year":2023,"url":"https://arxiv.org/abs/2305.16264","citing_submissions":119,"explicit_arxiv_matches":32,"examples":[{"id":"UBWOaIbzKh","title":"GRACE: Graded Representation Augmentation for Cost-Effective SLM–LLM Collaboration","forum_url":"https://openreview.net/forum?id=UBWOaIbzKh","excerpt":"l. Ministral 3. arXiv preprint arXiv:2601.08584, 2026. Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A Raffel. Scaling data-constrained language models. Advances in Neural Information Processing Systems, 36:50358–50376, 2023. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E Gonzalez, Mohammed Kadous, and Ion Stoica. Route","arxiv_match":false},{"id":"Fhj95cY1wD","title":"Decoupling Token Roles in Autoregressive Pretraining","forum_url":"https://openreview.net/forum?id=Fhj95cY1wD","excerpt":"on hypothesis. arXiv preprint arXiv:2607.01218, 2026. Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, and Colin Raffel. Scaling data-constrained language models. In Advances in Neural Information Processing Systems, 2023. OpenAI. GPT-4 technical report. arXiv preprint arXiv:2303.08774, 2023. Jinlong Pang, Na Di, Zhaowei Zhu, Jiaheng Wei, Hao Cheng, Chen Qian","arxiv_match":false},{"id":"8v0pPgA8ph","title":"Training Foundation Models with a Single Algorithm","forum_url":"https://openreview.net/forum?id=8v0pPgA8ph","excerpt":"nference on machine learning, pp. 1928–1937. PmLR, 2016. Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin Raffel. Scaling dataconstrained language models. In Advances in Neural Information Processing Systems, volume 36, pp. 50358–50376. Curran Associates, Inc., 2023. doi: 10.52202/ 075280-2191. URL https://proceedings.neurips.cc/paper_files/paper/2023","arxiv_match":false}],"id":"2305.16264","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":854},{"title":"VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model","arxiv_id":"2602.10098","year":2026,"url":"https://arxiv.org/abs/2602.10098","citing_submissions":119,"explicit_arxiv_matches":65,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"b768152c9c19d-Abstract-Conference.html. StarVLA Community. StarVLA: A lego-like codebase for vision-language-action model developing. arXiv preprint arXiv:2604.05014, 2026. URL https://arxiv.org/abs/2604.05014. Jingwen Sun, Wenyao Zhang, Zekun Qi, Shaojie Ren, Zezhi Liu, Hanxin Zhu, Guangzhong Sun, Xin Jin, and Zhibo Chen. VLA-JEPA: Enhancing vision-language-action model with latent world model. arXiv preprint arXiv:2602.10098, 2026. URL https://arxiv.org/abs/2602.10098. Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, and Jiangmiao Pang. Predictive in","arxiv_match":true},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":"geun Yi, Michaël Ramamonjisoa, et al. Dinov3. arXiv preprint arXiv:2508.10104, 2025. Jingwen Sun, Wenyao Zhang, Zekun Qi, Shaojie Ren, Zezhi Liu, Hanxin Zhu, Guangzhong Sun, Xin Jin, and Zhibo Chen. Vla-jepa: Enhancing vision-language-action model with latent world model. In European Conference on Computer Vision, pp. 478–497. Springer, 2026. Shuhan Tan, Kairan Dou, Yue Zhao, and Philipp Krähenbühl. Interactive post-training for visionlanguage-action models. arXiv ","arxiv_match":false},{"id":"xE1Rd5Luma","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","forum_url":"https://openreview.net/forum?id=xE1Rd5Luma","excerpt":" multitask learning. In International Conference on Learning Representations, volume 2025, pp. 17247–17275, 2025. Marc Rigter, Tarun Gupta, Agrin Hilmkil, and Chao Ma. Avid: Adapting video diffusion models to world models. arXiv preprint arXiv:2410.12822, 2024. Jingwen Sun, Wenyao Zhang, Zekun Qi, Shaojie Ren, Zezhi Liu, Hanxin Zhu, Guangzhong Sun, Xin Jin, and Zhibo Chen. Vla-jepa: Enhancing vision-language-action model with latent world model. arXiv preprint arXiv:2602.10098, 2026. Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, and Jiangmiao Pang. Predict","arxiv_match":true}],"id":"2602.10098","metadata_source":"Cached arXiv API metadata","rank":855},{"title":"Diffusion policies as an expressive policy class for offline reinforcement learning","arxiv_id":"2208.06193","year":2022,"url":"https://arxiv.org/abs/2208.06193","citing_submissions":118,"explicit_arxiv_matches":49,"examples":[{"id":"JQG8LaeAwe","title":"Reinforcement Learning by Change of Measure: A BSDE Theory for Generative Policies","forum_url":"https://openreview.net/forum?id=JQG8LaeAwe","excerpt":"ng of continuous-time diffusion models as entropy-regularized control. arXiv preprint arXiv:2402.15194, 2024. URL https://arxiv.org/abs/2402.15194. Zhendong Wang, Jonathan J. Hunt, and Mingyuan Zhou. Diffusion policies as an expressive policy class for offline reinforcement learning. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=AHvFDPi-FA. Zirui Wang and Lu Wang. Generalized fine-tuning of diffusion models via stochastic cont","arxiv_match":false},{"id":"6FEw1esZ1j","title":"Kuramoto Decision Diffusion Models","forum_url":"https://openreview.net/forum?id=6FEw1esZ1j","excerpt":"teering your diffusion policy with latent space reinforcement learning. arXiv preprint arXiv:2506.15799, 2025. Cited on pages 8, 9, 21, and 39. Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffusion null-space model. arXiv preprint arXiv:2212.00490, 2022a. Cited on page 20. Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou. Diffusion policies as an expressive policy class for offline reinforcement learning. arXiv preprint arXiv:2208.06193, 2022b. Cited on page 21. Max Welling and Yee W Teh. Bayesian learning via stochastic gradient lang","arxiv_match":true},{"id":"b4JARCTPGU","title":"Improving Truncated First-Order Gradients for Flow Policies with Surrogate Objective","forum_url":"https://openreview.net/forum?id=b4JARCTPGU","excerpt":"ystems, volume 37, 2024. URL https://proceedings.neurips.cc/paper_files/paper/2024/ hash/6174c67b136621f3f2e4a6b1d3286f6b-Abstract-Conference.html. Zhendong Wang, Jonathan J. Hunt, and Mingyuan Zhou. Diffusion policies as an expressive policy class for offline reinforcement learning. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=AHvFDPi-FA. Nina Wiedemann, Valentin Wüest, Antonio Loquercio, Matthias Müller, Dario Floreano, a","arxiv_match":false}],"id":"2208.06193","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":856},{"title":"Gemini Robotics: Bringing AI into the Physical World","arxiv_id":"2503.20020","year":2025,"url":"https://arxiv.org/abs/2503.20020","citing_submissions":118,"explicit_arxiv_matches":109,"examples":[{"id":"vowOuAXSIO","title":"EmboScope: A Multi-Source Comprehensive Benchmark for Embodied Perception, Understanding, and Planning","forum_url":"https://openreview.net/forum?id=vowOuAXSIO","excerpt":"n, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Caifeng Shan, Ran He, and Xing Sun. Video-MME: The first-ever comprehensive evaluation benchmark of multi-modal LLMs in video analysis. arXiv preprint arXiv:2405.21075, 2024. URL https://arxiv.org/abs/2405.21075. Gemini Robotics Team. Gemini robotics: Bringing AI into the physical world. arXiv preprint arXiv:2503.20020, 2025. URL https://arxiv.org/abs/2503.20020. Gemma Team. Gemma 3 Technical Report. arXiv preprint arXiv:2503.19786, 2025. URL https: //arxiv.or","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"Seungjun Nah, Loic Magne, Jiannan Xiang, Yuqi Xie, Ruijie Zheng, Dantong Niu, You Liang Tan, K.R. Zentner, George Kurian, Suneel Indupuru, Pooya Jannaty, Jinwei Gu, Jun Zhang, Jitendra Malik, Pieter Abbeel, Ming-Yu Liu, Yuke Zhu, Joel Jang, and Linxi ”Jim” Fan. Dreamdojo: A generalist robot world model from large-scale human videos. arXiv preprint arXiv:2602.06949, 2026. Gemini Robotics Team. Gemini robotics: Bringing AI into the physical world. arXiv preprint arXiv:2503.20020, 2025a. Gemini Robotics Team. Gemini robotics 1.5: Pushing the frontier of generalist robots with a","arxiv_match":true},{"id":"dVXFFMVuTo","title":"ManiUnit: A Manipulation Skill Dataset and Benchmark for Long-Horizon Tasks","forum_url":"https://openreview.net/forum?id=dVXFFMVuTo","excerpt":"n. In Forty-third International Conference on Machine Learning, 2026. Caelan Reed Garrett, Ajay Mandlekar, Bowen Wen, and Dieter Fox. SkillMimicGen: Automated demonstration generation for efficient skill learning and deployment. In Proceedings of The 8th Conference on Robot Learning, volume 270 of Proceedings of Machine Learning Research, pp. 2750–2790. PMLR, 2025. Gemini Robotics Team et al. Gemini Robotics: Bringing AI into the physical world. arXiv preprint arXiv:2503.20020, 2025. Jiayuan Gu, Devendra Singh Chaplot, Hao Su, and Jitendra Malik. Multi-skill mobile manipulat","arxiv_match":true}],"id":"2503.20020","metadata_source":"Cached arXiv API metadata","rank":857},{"title":"Illuminating search spaces by mapping elites","arxiv_id":"1504.04909","year":2015,"url":"https://arxiv.org/abs/1504.04909","citing_submissions":118,"explicit_arxiv_matches":115,"examples":[{"id":"CkuA2Se9vX","title":"MODEPrompt: Taxonomy-Guided Pareto Evolution for Prompt Optimization","forum_url":"https://openreview.net/forum?id=CkuA2Se9vX","excerpt":": Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36:46534–46594, 2023. Elliot Meyerson, Mark J Nelson, Herbie Bradley, Adam Gaier, Arash Moradi, Amy K Hoover, and Joel Lehman. Language model crossover: Variation through few-shot prompting. ACM Transactions on Evolutionary Learning, 4(4):1–40, 2024. doi: 10.1145/3694791. Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites. arXiv preprint arXiv:1504.04909, 2015. Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, Kashun Shum, Jipeng Zhang, Renjie Pi,","arxiv_match":true},{"id":"sPAEy9VbRn","title":"AutoQS: Automated Quantization Strategy Design with Large Language Models","forum_url":"https://openreview.net/forum?id=sPAEy9VbRn","excerpt":" In International Conference on Machine Learning, pp. 32201–32223. PMLR, 2024. Yijiang Liu, Hengyu Fang, Liulu He, Rongyu Zhang, Yichuan Bai, Yuan Du, and Li Du. Fbquant: Feedback quantization for large language models. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, pp. 7589–7597, 2025. doi: 10.24963/ijcai.2025/ 844. Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites. arXiv preprint arXiv:1504.04909, 2015. Alexander Novikov, Ngân Vũ, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt W","arxiv_match":true},{"id":"kWVfb2a0lE","title":"SC2Forge: A Calibrated Benchmark and a Program-Writing Agent for Full-Game StarCraft II","forum_url":"https://openreview.net/forum?id=kWVfb2a0lE","excerpt":". Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Eureka: human-level reward design via coding large language models. In International Conference on Learning Representations (ICLR), 2024b. URL https://arxiv.org/abs/2310.12931. Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites. arXiv preprint arXiv:1504.04909, 2015. URL https://arxiv.org/abs/1504.04909. Alexander Novikov, Ngân Vũ, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner,","arxiv_match":true}],"id":"1504.04909","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":858},{"title":"RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots","arxiv_id":"2603.04356","year":2026,"url":"https://arxiv.org/abs/2603.04356","citing_submissions":118,"explicit_arxiv_matches":37,"examples":[{"id":"dVXFFMVuTo","title":"ManiUnit: A Manipulation Skill Dataset and Benchmark for Long-Horizon Tasks","forum_url":"https://openreview.net/forum?id=dVXFFMVuTo","excerpt":"e-conditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters, 7(3):7327–7334, 2022. Soroush Nasiriany, Sep Nasiriany, Abhiram Maddukuri, and Yuke Zhu. RoboCasa365: A largescale simulation framework for training and benchmarking generalist robots. In International Conference on Learning Representations, volume 2026, pp. 98643–98667, 2026. NVIDIA et al. GR00T N1: An open foundation model for generalist humanoid robots. arXiv preprint arXiv:250","arxiv_match":false},{"id":"9FaUYSuhHT","title":"SmokeBot: A Perception-to-Manipulation Framework and Simulation Platform for Robotic Manipulation in Smoke Scenes","forum_url":"https://openreview.net/forum?id=9FaUYSuhHT","excerpt":" RoboCasa: Large-scale simulation of everyday tasks for generalist robots. In Proceedings of Robotics: Science and Systems, 2024. Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri, and Yuke Zhu. RoboCasa365: A largescale simulation framework for training and benchmarking generalist robots. In International Conference on Learning Representations, 2026. Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine. Meta-World: A benchmark and evalu","arxiv_match":false},{"id":"TSmJv3g9IV","title":"Vision-Language Models Are Joint Denoisers in Robotic Action Generation","forum_url":"https://openreview.net/forum?id=TSmJv3g9IV","excerpt":"erstanding and generation. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7739–7751. IEEE, 2025. Soroush Nasiriany, Sep Nasiriany, Abhiram Maddukuri, and Yuke Zhu. Robocasa365: A largescale simulation framework for training and benchmarking generalist robots. In International Conference on Learning Representations, volume 2026, pp. 98643–98667, 2026. Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang X","arxiv_match":false}],"id":"2603.04356","metadata_source":"Cached arXiv API metadata","rank":859},{"title":"SAM 3D: 3Dfy Anything in Images","arxiv_id":"2511.16624","year":2025,"url":"https://arxiv.org/abs/2511.16624","citing_submissions":118,"explicit_arxiv_matches":51,"examples":[{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":", Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F Yago Vicente, Thomas Dideriksen, Himanshu Arora, et al. Abo: Dataset and benchmarks for real-world 3d object understanding. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21094–21104. IEEE, 2022. Xingyu Chen, Fu-Jen Chu, Pierre Gleize, Kevin J Liang, Alexander Sax, Hao Tang, Weiyao Wang, Michelle Guo, Thibaut Hardin, Xiang Li, et al. Sam 3d: 3dfy anything in images. arXiv preprint arXiv:2511.16624, 2025b. Tianhang Cheng, Wei-Chiu Ma, Kaiyu Guan, Antonio Torralba, and Shenlong Wang. Structure fro","arxiv_match":true},{"id":"MOLTwuUqhX","title":"Intrinsic Alignment by Generation: Monocular Hand–Object Reconstruction via Interaction Scaffolds","forum_url":"https://openreview.net/forum?id=MOLTwuUqhX","excerpt":" J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics, 36(6):245:1–245:17, 2017. Rolandos Alexandros Potamias, Jinglei Zhang, Jiankang Deng, and Stefanos Zafeiriou. WiLoR: End-to-end 3d hand localization and reconstruction in-the-wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12242–12254, 2025. SAM 3D Team. SAM 3d: 3dfy anything in images. arXiv preprint arXiv:2511.16624, 2025. Jin-Chuan Shi, Binhong Ye, Tao Liu, Junzhe He, Yangjinhui Xu, Xiaoyang Liu, Zeju Li, Hao Che","arxiv_match":true},{"id":"PTgaRLuhod","title":"Faster3D: Exploring the Roles of Latent Tokens for Faster Image-to-3D Generation","forum_url":"https://openreview.net/forum?id=PTgaRLuhod","excerpt":"SBN 978-3-319-46484-8. Weilun Feng, Mingqiang Wu, zhiliang chen, Chuanguang Yang, Haotong Qin, Yuqi Li, Xiaokun Liu, Guoxin Fan, Libo Huang, Yulun Zhang, Michele Magno, Yongjun Xu, and Zhulin An. FastSAM3d: 3dfy anything in images but faster. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=2kKk6W6oOF. Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyu","arxiv_match":false}],"id":"2511.16624","metadata_source":"Cached arXiv API metadata","rank":860},{"title":"AMBER: An LLM-free multi-dimensional benchmark for MLLMs hallucination evaluation","arxiv_id":"2311.07397","year":2023,"url":"https://arxiv.org/abs/2311.07397","citing_submissions":117,"explicit_arxiv_matches":113,"examples":[{"id":"gX30Aapm6u","title":"VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning","forum_url":"https://openreview.net/forum?id=gX30Aapm6u","excerpt":"chen Zhang, and Haomiao Sun. Tarsier: Recipes for training and evaluating large video description models. ArXiv preprint, abs/2407.00634, 2024b. URL https: //arxiv.org/abs/2407.00634. Junyang Wang, Yuhang Wang, Guohai Xu, Jing Zhang, Yukai Gu, Haitao Jia, Jiaqi Wang, Haiyang Xu, Ming Yan, Ji Zhang, and Jitao Sang. AMBER: An LLM-free multi-dimensional benchmark for MLLMs hallucination evaluation. ArXiv preprint, abs/2311.07397, 2023. URL https:// arxiv.org/abs/2311.07397. Qiang Wang, Xinyuan Gao, SongLin Dong, Jizhou Han, Jiangyang Li, Yuhang He, and Yihong Gong. Vdc-a","arxiv_match":true},{"id":"hVl6H6DnYO","title":"Consistency as an Inductive Bias: Mitigating Cross-View Reliability Gap in MLLMs","forum_url":"https://openreview.net/forum?id=hVl6H6DnYO","excerpt":"s Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, and Muhao Chen. mDPO: Conditional preference optimization for multimodal large language models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2024. Junyang Wang, Yuhang Wang, Guohai Xu, Jing Zhang, Yukai Gu, Haitao Jia, Jiaqi Wang, Haiyang Xu, Ming Yan, Ji Zhang, and Jitao Sang. AMBER: An LLM-free multi-dimensional benchmark for MLLMs hallucination evaluation. arXiv preprint arXiv:2311.07397, 2023. Zhenhailong Wang, Xuehang Guo, Sofia Stoica, Haiyang Xu, Hongru Wang, Hyeonjeong Ha, Xiusi C","arxiv_match":true},{"id":"wpTLstf0kk","title":"Rollout-Visual-Commit: Mitigating Object Hallucination via Commit-Time Multimodal Scoring","forum_url":"https://openreview.net/forum?id=wpTLstf0kk","excerpt":"Rico Sennrich, and Ivan Titov. Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5797–5808, 2019. Junyang Wang, Yuhang Wang, Guohai Xu, Jing Zhang, Yukai Gu, Haitao Jia, Jiaqi Wang, Haiyang Xu, Ming Yan, Ji Zhang, et al. AMBER: An LLM-free multi-dimensional benchmark for MLLM hallucination evaluation. arXiv preprint arXiv:2311.07397, 2023a. Junyang Wang, Yiyang Zhou, Guohai Xu, Pengcheng Shi, Chenlin Zhao, Haiyang Xu, Qinghao Ye, ","arxiv_match":true}],"id":"2311.07397","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":861},{"title":"MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence","arxiv_id":"2505.23764","year":2025,"url":"https://arxiv.org/abs/2505.23764","citing_submissions":117,"explicit_arxiv_matches":43,"examples":[{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"Xiv preprint arXiv:2511.04670, 2025c. Sihan Yang, Runsen Xu, Yiman Xie, Sizhe Yang, Mo Li, Jingli Lin, Chenming Zhu, Xiaochen Chen, Haodong Duan, Xiangyu Yue, Dahua Lin, Tai Wang, and Jiangmiao Pang. MMSI-Bench: A benchmark for multi-image spatial intelligence. In International Conference on Learning Representations, volume 2026, pp. 157051–157088, 2026a. Yuncong Yang, Jiageng Liu, Zheyuan Zhang, Siyuan Zhou, Reuben Tan, Jianwei Yang, Yilun Du, and Chuang ","arxiv_match":false},{"id":"h0PK0nlkuc","title":"Seek-and-View Reasoning for Multi-View Spatial Understanding","forum_url":"https://openreview.net/forum?id=h0PK0nlkuc","excerpt":"or crossview spatial reasoning. arXiv preprint arXiv:2605.27310, 2026a. Sihan Yang, Runsen Xu, Yiman Xie, Sizhe Yang, Mo Li, Jingli Lin, Chenming Zhu, Xiaochen Chen, Haodong Duan, Xiangyu Yue, et al. Mmsi-bench: A benchmark for multi-image spatial intelligence. In International Conference on Learning Representations, volume 2026, pp. 157051–157088, 2026b. Yuncong Yang, Jiageng Liu, Zheyuan Zhang, Siyuan Zhou, Reuben Tan, Jianwei Yang, Yilun Du, and Chuang ","arxiv_match":false},{"id":"TA0thWBRuQ","title":"ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence","forum_url":"https://openreview.net/forum?id=TA0thWBRuQ","excerpt":"e on Learning Representations, 2026a. Sihan Yang, Runsen Xu, Yiman Xie, Sizhe Yang, Mo Li, Jingli Lin, Chenming Zhu, Xiaochen Chen, Haodong Duan, Xiangyu Yue, Dahua Lin, Tai Wang, and Jiangmiao Pang. MMSI-Bench: A benchmark for multi-image spatial intelligence. In The Fourteenth International Conference on Learning Representations, 2026b. Yanlai Yang, Zhuokai Zhao, Satya Narayan Shukla, Aashu Singh, Shlok Kumar Mishra, Lizhu Zhang, and Mengye Ren. StreamMe","arxiv_match":false}],"id":"2505.23764","metadata_source":"Cached arXiv API metadata","rank":862},{"title":"Perception Encoder: The best visual embeddings are not at the output of the network","arxiv_id":"2504.13181","year":2025,"url":"https://arxiv.org/abs/2504.13181","citing_submissions":117,"explicit_arxiv_matches":35,"examples":[{"id":"AzPpBFMquF","title":"Segment Anything In-Context via Visual Demonstrations","forum_url":"https://openreview.net/forum?id=AzPpBFMquF","excerpt":"presentations, volume 2024, pp. 5304–5325, 2024. Daniel Bolya, Po-Yao Huang, Peize Sun, Jang Hyun Cho, Andrea Madotto, Chen Wei, Tengyu Ma, Jiale Zhi, Jathushan Rajasegaran, Hanoona Bangalath, et al. Perception encoder: The best visual embeddings are not at the output of the network. Advances in Neural Information Processing Systems, 38:60884–60937, 2026. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, G","arxiv_match":false},{"id":"F0hAiHbpm3","title":"FigAct: Turning Scientific Figures into Active Canvases for Explanation","forum_url":"https://openreview.net/forum?id=F0hAiHbpm3","excerpt":"sentations, volume 2024, pp. 55917–55943, 2024a. Daniel Bolya, Po-Yao Huang, Peize Sun, Jang Hyun Cho, Andrea Madotto, Chen Wei, Tengyu Ma, Jiale Zhi, Jathushan Rajasegaran, Hanoona Bangalath, et al. Perception encoder: The best visual embeddings are not at the output of the network. Advances in Neural Information Processing Systems, 38:60884–60937, 2026. Magy Seif El-Nasr. An interactive narrative architecture based on filmmaking theory. 2004. Martin Ester, Hans-Peter Kriegel, ","arxiv_match":false},{"id":"MpNPoDTf3B","title":"Semantic Fields Are Interfaces: Consumer-Typed Functional Migration for Representation Autoencoders","forum_url":"https://openreview.net/forum?id=MpNPoDTf3B","excerpt":"dotto, Chen Wei, Tengyu Ma, Jiale Zhi, Jathushan Rajasegaran, Hanoona Bangalath, Junke Wang, Marco Monteiro, Hu Xu, Shiyu Dong, Nikhila Ravi, Shang-Wen Li, Piotr Dollár, and Christoph Feichtenhofer. Perception encoder: The best visual embeddings are not at the output of the network. In Advances in Neural Information Processing Systems, volume 38, 2025. Ngoc Bui, Menglin Yang, Runjin Chen, Leonardo Neves, Mingxuan Ju, Zhitao Ying, Neil Shah, and Tong Zhao. Learning along the arr","arxiv_match":false}],"id":"2504.13181","metadata_source":"Cached arXiv API metadata","rank":863},{"title":"Progressive neural networks","arxiv_id":"1606.04671","year":2016,"url":"https://arxiv.org/abs/1606.04671","citing_submissions":117,"explicit_arxiv_matches":111,"examples":[{"id":"fkF1iei3Gs","title":"ACNA: Decentralized Active-Cell Routing for Sparse and Dynamically Expandable Neural Networks","forum_url":"https://openreview.net/forum?id=fkF1iei3Gs","excerpt":"l Computation and Automatic Sharding. arXiv:2006.16668, 2020. W. Fedus, B. Zoph, and N. Shazeer. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity. JMLR 23(120):1–39, 2022. Y. Zhou et al. Mixture-of-Experts with Expert Choice Routing. arXiv:2202.09368, 2022. Y. Liu, J. Han, S. Yan, V. Tresp, and Y. Ma. Routing-Free Mixture-ofExperts. arXiv:2604.00801, 2026. A. A. Rusu et al. Progressive Neural Networks. arXiv:1606.04671, 2016. C. Fernando et al. PathNet: Evolution Channels Gradient Descent in Super Neural Networks. 20","arxiv_match":true},{"id":"rQbFetzbJu","title":"Which Host Versions Should a Removable Branch Learn From?","forum_url":"https://openreview.net/forum?id=rQbFetzbJu","excerpt":"dvances in Neural Information Processing Systems, 2017. Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell. Progressive neural networks, 2016. Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, and Donald Metzler. Confident adaptive language modeling. In Advances in Neural Information Processing ","arxiv_match":false},{"id":"fzmS4ZKTvr","title":"Reducing Pretrained Representation Degradation in Parameter-Efficient Continual Learning","forum_url":"https://openreview.net/forum?id=fzmS4ZKTvr","excerpt":"s of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020. Roger Ratcliff. Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychological review, 97(2):285, 1990. Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell. Progressive neural networks. arXiv preprint arXiv:1606.04671, 2016. Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl:","arxiv_match":true}],"id":"1606.04671","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":864},{"title":"PyTorch FSDP: Experiences on scaling fully sharded data parallel","arxiv_id":"2304.11277","year":2023,"url":"https://arxiv.org/abs/2304.11277","citing_submissions":117,"explicit_arxiv_matches":66,"examples":[{"id":"094XMxiFom","title":"PLUMAGE: A Probabilistic Low-rank Unbiased Min-Variance Gradient Estimator for Memory Efficient Large Model Training","forum_url":"https://openreview.net/forum?id=094XMxiFom","excerpt":"tml. Y. Zhao, A. Gu, R. Varma, L. Luo, C.-C. Huang, M. Xu, L. Wright, H. Shojanazeri, M. Ott, S. Shleifer, A. Desmaison, C. Balioglu, P. Damania, B. Nguyen, G. Chauhan, Y. Hao, A. Mathews, and S. Li. Pytorch fsdp: Experiences on scaling fully sharded data parallel. Proceedings of the VLDB Endowment, 16(12):3848–3860, Aug. 2023. ISSN 2150-8097. doi: 10.14778/3611540.3611569. URL http://dx.doi.org/10.14778/3611540.3611569. H. Zhu, Z. Zhang, W. Cong, X. Liu, S. P","arxiv_match":false},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":" computation-communication overlapping for mixture-of-experts. arXiv preprint arXiv:2502.19811, 2025. Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated delta networks: Improving Mamba2 with delta rule, 2024. URL https://arxiv.org/abs/2412.06464. Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, et al. PyTorch FSDP: experiences on scaling fully sharded data parallel. arXiv preprint arXiv:2304.11277, 2023. Ruidong Zhu, Ziheng Jiang, Chao Jin, Peng Wu, Cesar A Stuardo, Dongyang Wang, Xinlei Zhang, ","arxiv_match":true},{"id":"V0dfRkM2lE","title":"One Step Generation via Condition Shifting","forum_url":"https://openreview.net/forum?id=V0dfRkM2lE","excerpt":"ichard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6613–6623, 2024b. Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, et al. Pytorch fsdp: experiences on scaling fully sharded data parallel. arXiv preprint arXiv:2304.11277, 2023. Kaiwen Zheng, Yongxin Chen, Huayu Chen, Guande He, Ming-Yu Liu, Jun Zhu, and Qinsheng Zhang.","arxiv_match":true}],"id":"2304.11277","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":865},{"title":"UCF101: A dataset of 101 human actions classes from videos in the wild","arxiv_id":"1212.0402","year":2012,"url":"https://arxiv.org/abs/1212.0402","citing_submissions":117,"explicit_arxiv_matches":101,"examples":[{"id":"aVu1Zo8kCI","title":"Beyond Holistic Representations: Dual Distribution Modeling for Test-Time Adaptation of Vision–Language Models","forum_url":"https://openreview.net/forum?id=aVu1Zo8kCI","excerpt":"ternational journal of computer vision, 115:211–252, 2015. Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao. Test-time prompt tuning for zero-shot generalization in vision-language models. In Advances in Neural Information Processing Systems, volume 35, pp. 14274–14289, 2022. Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. Yi-Lin Sung, Jaemin Cho, and Mohit Bansal. Vl-adapter: Parameter-efficient transfer learning","arxiv_match":true},{"id":"M1V3pHi7o3","title":"Beyond Text Anchors: Dual-Anchor Flow Matching for Few-Shot and Incremental Adaptation","forum_url":"https://openreview.net/forum?id=M1V3pHi7o3","excerpt":" In CVPR, 2026. Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 2017. Zeyin Song, Yifan Zhao, Yujun Shi, Peixi Peng, Li Yuan, and Yonghong Tian. Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class-incremental learning. In CVPR, 2023. Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. Yu-Ming Tang, Yi-Xing Peng, Jingke Meng, and Wei-Shi Zheng. Rethinking few-shot classincreme","arxiv_match":true},{"id":"RoJiIFcc1v","title":"InnerCIL: Internal Neuron Representations for Analytic Class-Incremental Learning","forum_url":"https://openreview.net/forum?id=RoJiIFcc1v","excerpt":"ree continual learning. In 2023 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 11909–11919. IEEE, 2023. Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf. A hilbert space embedding for distributions. In International conference on algorithmic learning theory, pp. 13–31. Springer, 2007. Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The Caltech","arxiv_match":true}],"id":"1212.0402","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":866},{"title":"Paligemma: A versatile 3b vlm for transfer","arxiv_id":"2407.07726","year":2024,"url":"https://arxiv.org/abs/2407.07726","citing_submissions":116,"explicit_arxiv_matches":109,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":", Zhe Li, Dakai Wang, Lei Xing, Xiaoguang Zhao, Pengwei Wang, Zhongyuan Wang, Cheng Chi, et al. Latent reasoning vla: Latent thinking and prediction for vision-language-action models. arXiv preprint arXiv:2602.01166, 2026. Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, et al. Paligemma: A versatile 3b vlm for transfer. arXiv preprint arXiv:2407.07726, 2024. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fus","arxiv_match":true},{"id":"MeVsuBHOpo","title":"EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training","forum_url":"https://openreview.net/forum?id=MeVsuBHOpo","excerpt":"Bugliarello, Thomas Unterthiner, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bošnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier Henaff, Xi Xiong, Radu Soricut, Jeremiah Harmsen, and Xiaohua Zhai. Paligemma: A versatile 3b vlm for transfer. arXiv preprint arXiv:2407.07726, 2024. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fus","arxiv_match":true},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":" Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, and Dumitru Erhan. Fitvid: Overfitting in pixel-level video prediction. arXiv preprint arXiv:2106.13195, 2021. Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, et al. Paligemma: A versatile 3b vlm for transfer. arXiv preprint arXiv:2407.07726, 2024. Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Y","arxiv_match":true}],"id":"2407.07726","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":867},{"title":"SimpleMem: Efficient Lifelong Memory for LLM Agents","arxiv_id":"2601.02553","year":2026,"url":"https://arxiv.org/abs/2601.02553","citing_submissions":116,"explicit_arxiv_matches":76,"examples":[{"id":"bKtriz3SuM","title":"Capability-Driven Self-Evolution of Agent Memory","forum_url":"https://openreview.net/forum?id=bKtriz3SuM","excerpt":"agents. In Findings of the Association for Computational Linguistics: ACL 2026, pp. 21700–21720, 2026. Jiaqi Liu, Yaofeng Su, Peng Xia, Siwei Han, Zeyu Zheng, Cihang Xie, Mingyu Ding, and Huaxiu Yao. SimpleMem: Efficient lifelong memory for LLM agents. In Forty-third International Conference on Machine Learning, 2026a. URL https://openreview.net/forum?id= oBgLvd5YC6. Jiaqi Liu, Xinyu Ye, Peng Xia, Zeyu Zheng, Cihang Xie, Mingyu Ding, and Huaxiu Ya","arxiv_match":false},{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":"n-language-action model for gui visual agent. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 19498–19508, 2025. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36:34892–34916, 2023. Jiaqi Liu, Yaofeng Su, Peng Xia, Siwei Han, Zeyu Zheng, Cihang Xie, Mingyu Ding, and Huaxiu Yao. Simplemem: Efficient lifelong memory for llm agents. arXiv preprint arXiv:2601.02553, 2026a. Jiateng Liu, Zhenhailong Wang, Rushi Wang, Bingxuan Li, Jeonghwan Kim, Aditi Tiwari, Pengfe","arxiv_match":true},{"id":"1V3066nDJF","title":"Contract Memory Compiler: Resolve, Then Traverse","forum_url":"https://openreview.net/forum?id=1V3066nDJF","excerpt":" https://aclanthology.org/ C02-1150/. Yidan Lin, Kaixiang Wang, Jiong Lou, and Jie Li. Stop when memory suffices: Evidenceconditioned progressive execution for LLM agents. arXiv preprint arXiv:2608.01285, 2026. URL https://arxiv.org/abs/2608.01285. Jiaqi Liu, Yaofeng Su, Peng Xia, Siwei Han, Zeyu Zheng, Cihang Xie, Mingyu Ding, and Huaxiu Yao. SimpleMem: Efficient lifelong memory for LLM agents. arXiv preprint arXiv:2601.02553, 2026. URL https://arxiv.org/abs/2601.02553. Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Pe","arxiv_match":true}],"id":"2601.02553","metadata_source":"Cached arXiv API metadata","rank":868},{"title":"Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents","arxiv_id":"2604.10674","year":2026,"url":"https://arxiv.org/abs/2604.10674","citing_submissions":116,"explicit_arxiv_matches":111,"examples":[{"id":"8rZ7oQVOCr","title":"Self-Distillation with Verbal Regularization","forum_url":"https://openreview.net/forum?id=8rZ7oQVOCr","excerpt":"Yan, Chen Shen, Hao Chen, Wentao Ye, Xiaomeng Hu, Rui Miao, Haobo Wang, Junbo Zhao, Gang Chen, and Jieping Ye. Purified opsd: On-policy selfdistillation without losing how to think, 2026b. URL https://arxiv.org/abs/2607. 02234. Hao Wang, Guozhi Wang, Han Xiao, Yufeng Zhou, Yue Pan, Jichao Wang, Ke Xu, Yafei Wen, Xiaohu Ruan, Xiaoxin Chen, and Honggang Qi. Skill-sd: Skill-conditioned self-distillation for multi-turn llm agents, 2026a. URL https://arxiv.org/abs/2604.10674. Yinjie Wang, Xuyang Chen, Xiaolong Jin, Mengdi Wang, and Ling Yang. Openclaw-rl: Train any agent s","arxiv_match":true},{"id":"50kJuTajGS","title":"ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation","forum_url":"https://openreview.net/forum?id=50kJuTajGS","excerpt":" Trischler, and Matthew Hausknecht. ALFWorld: Aligning Text and Embodied Environments for Interactive Learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2021. URL https://arxiv.org/abs/2010.03768. Hao Wang, Guozhi Wang, Han Xiao, Yufeng Zhou, Yue Pan, Jichao Wang, Ke Xu, Yafei Wen, Xiaohu Ruan, Xiaoxin Chen, et al. Skill-sd: Skill-conditioned self-distillation for multi-turn llm agents, 2026. URL https://arxiv.org/abs/2604.10674. Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Y","arxiv_match":true},{"id":"dHl5b1GjT0","title":"ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience","forum_url":"https://openreview.net/forum?id=dHl5b1GjT0","excerpt":"ations (ICLR), 2021. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An Open-Ended Embodied Agent with Large Language Models. Transactions on Machine Learning Research, 2024. Hao Wang, Guozhi Wang, Han Xiao, Yufeng Zhou, Yue Pan, Jichao Wang, Ke Xu, Yafei Wen, Xiaohu Ruan, Xiaoxin Chen, and Honggang Qi. Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents. arXiv preprint arXiv:2604.10674, 2026b. Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai. Continual Test-Time Domain Adaptation. ","arxiv_match":true}],"id":"2604.10674","metadata_source":"Cached arXiv API metadata","rank":869},{"title":"VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding","arxiv_id":"2501.13106","year":2025,"url":"https://arxiv.org/abs/2501.13106","citing_submissions":116,"explicit_arxiv_matches":111,"examples":[{"id":"TDch8W9nIS","title":"HypToC: Hyperbolic Vision Token Compression for Video Large Language Models","forum_url":"https://openreview.net/forum?id=TDch8W9nIS","excerpt":"gyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. VisionZip: Longer is better but not necessary in vision language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19792–19802, 2025b. Boqiang Zhang, Kehan Li, Zesen Cheng, Zhiqiang Hu, Yuqian Yuan, Guanzheng Chen, Sicong Leng, Yuming Jiang, Hang Zhang, Xin Li, et al. VideoLLaMA 3: Frontier multimodal foundation models for image and video understanding. arXiv preprint arXiv:2501.13106, 2025a. Hang Zhang, Xin Li, and Lidong Bing. Video-LLaMA: An instruction-tuned audio-visual languag","arxiv_match":true},{"id":"Q1JvJKZyrX","title":"Unlocking Dense Metric Depth Estimation in VLMs","forum_url":"https://openreview.net/forum?id=Q1JvJKZyrX","excerpt":"r Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3712–3722, 2018. Boqiang Zhang, Kehan Li, Zesen Cheng, Zhiqiang Hu, Yuqian Yuan, Guanzheng Chen, Sicong Leng, Yuming Jiang, Hang Zhang, Xin Li, et al. Videollama 3: Frontier multimodal foundation models for image and video understanding. arXiv preprint arXiv:2501.13106, 2025. Jiahui Zhang, Yurui Chen, Yueming Xu, Ze Huang, Jilin Mei, Chunhui Chen, Yanpeng Zhou, Yu-Ji","arxiv_match":true},{"id":"JTISUDVXnr","title":"DOVE: D-Optimal Visual Evidence for Extreme Video Token Compression","forum_url":"https://openreview.net/forum?id=JTISUDVXnr","excerpt":"ngyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. VisionZip: Longer is better but not necessary in vision language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19792–19802, 2025. Boqiang Zhang, Kehan Li, Zesen Cheng, Zhiqiang Hu, Yuqian Yuan, Guanzheng Chen, Sicong Leng, Yuming Jiang, Hang Zhang, Xin Li, et al. VideoLLaMA 3: Frontier multimodal foundation models for image and video understanding. arXiv preprint arXiv:2501.13106, 2025a. Hang Zhang, Xin Li, and Lidong Bing. Video-LLaMA: An instruction-tuned audio-visual languag","arxiv_match":true}],"id":"2501.13106","metadata_source":"Cached arXiv API metadata","rank":870},{"title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","arxiv_id":"1803.01271","year":2018,"url":"https://arxiv.org/abs/1803.01271","citing_submissions":115,"explicit_arxiv_matches":102,"examples":[{"id":"843NwTNlpm","title":"Where Can Normality Go: Learning Admissible Futures for Time Series Anomaly Detection","forum_url":"https://openreview.net/forum?id=843NwTNlpm","excerpt":" Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A Zuluaga. Usad: Unsupervised anomaly detection on multivariate time series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3395–3404, 2020. Shaojie Bai, J Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018. Ane Blázquez-Garcı́a, Angel Conde, Usue Mori, and Jose A Lozano. A review on outlier/anomal","arxiv_match":true},{"id":"QFoofhRoan","title":"Snow: A Stable Signed Channel Interaction Module for High-Dimensional Time Series Forecasting","forum_url":"https://openreview.net/forum?id=QFoofhRoan","excerpt":" Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018. doi: 10.48550/arXiv.1803.01271. URL https://arxiv.org/abs/1803.01271. Valérie Castin, Pierre Ablin, and Gabriel Peyré. How smooth is attention? In Proceedings of the ","arxiv_match":true},{"id":"bqu17MhkDo","title":"Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification","forum_url":"https://openreview.net/forum?id=bqu17MhkDo","excerpt":"on Bostrom, Paul Southam, and Eamonn Keogh. The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075, 2018. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014. Shaojie Bai, J Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018. Anthony J Bell and Terrence J Sejnowski. An information-maximization approach to blind separ","arxiv_match":true}],"id":"1803.01271","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":871},{"title":"An empirical study of example forgetting during deep neural network learning","arxiv_id":"1812.05159","year":2018,"url":"https://arxiv.org/abs/1812.05159","citing_submissions":115,"explicit_arxiv_matches":40,"examples":[{"id":"aZ4BU8bGIL","title":"Interaction-Aware Influence Functions for Group Attribution","forum_url":"https://openreview.net/forum?id=aZ4BU8bGIL","excerpt":"ization. In The Thirteenth International Conference on Learning Representations, 2025. Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon. An empirical study of example forgetting during deep neural network learning. In International Conference on Learning Representations, 2019. Athanasios Tsanas, Max Little, Patrick McSharry, and Lorraine Ramig. Accurate telemonitoring of parkinson’s disease progression by non-","arxiv_match":false},{"id":"YZsY9jjIur","title":"From Windows to Regions: Regional Gradients for Controlling Forecasting Trajectories","forum_url":"https://openreview.net/forum?id=YZsY9jjIur","excerpt":"earch, pp. 5739–5748. PMLR, 2019. URL https://proceedings.mlr.press/v97/shen19e.html. Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. An empirical study of example forgetting during deep neural network learning. In International Conference on Learning Representations, 2019. URL https://openreview. net/forum?id=BJlxm30cKm. Artur Trindade. ElectricityLoadDiagrams20112014. UCI Machine Learning Repository, 2015","arxiv_match":false},{"id":"bpZ7gnPe7g","title":"Mind the Tail: Dataset Selection Through Sparse Concepts","forum_url":"https://openreview.net/forum?id=bpZ7gnPe7g","excerpt":"izhen Zhao, and Xiaojuan Qi. Data pruning by information maximization. In ICLR, 2025. Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. An empirical study of example forgetting during deep neural network learning. In ICLR, 2019. Antonio Torralba and Alexei A Efros. Unbiased look at dataset bias. In CVPR, pp. 1521–1528. IEEE, 2011. Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim ","arxiv_match":false}],"id":"1812.05159","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":872},{"title":"Cognitive architectures for language agents","arxiv_id":"2309.02427","year":2023,"url":"https://arxiv.org/abs/2309.02427","citing_submissions":115,"explicit_arxiv_matches":47,"examples":[{"id":"yFzdWNvfkV","title":"Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents","forum_url":"https://openreview.net/forum?id=yFzdWNvfkV","excerpt":"nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7370–7392, 2024. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in neural information processing systems, 36:8634–8652, 2023. Theodore R Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L Griffiths. Cognitive architectures for language agents. arXiv preprint arXiv:2309.02427, 2023. Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long Le, Yiwen Song, Yanfei Chen, Ham","arxiv_match":true},{"id":"BJlDHn1L8T","title":"AECG: Asymmetric Experience Consolidation and Governance In Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=BJlDHn1L8T","excerpt":"gent collaboration. In International Conference on Learning Representations, volume 2025, pp. 41488–41505, 2025. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in neural information processing systems, 36:8634–8652, 2023. Theodore R Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L Griffiths. Cognitive architectures for language agents. arXiv preprint arXiv:2309.02427, 2023. Zhixun Tan, Qiang Chen, Tairan Huang, Xiu Su, and Yi Chen. Conmem: Structured memory-guided ","arxiv_match":true},{"id":"xmEaMO4noX","title":"Skill Training with Corruption and Reconstruction Loop","forum_url":"https://openreview.net/forum?id=xmEaMO4noX","excerpt":"nd Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning, 2023. URL https://arxiv.org/abs/2303.11366. Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics, 2015. URL https://arxiv.org/ abs/1503.03585. Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L. Griffiths. Cognitive architectures for language agents, 2024. URL https://arxiv.org/abs/2309.02427. Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, S. H. Cai, Yuan Cao, Ziwei Chai, Y. Charles, H. S.","arxiv_match":true}],"id":"2309.02427","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":873},{"title":"Large language model based multi-agents: A survey of progress and challenges","arxiv_id":"2402.01680","year":2024,"url":"https://arxiv.org/abs/2402.01680","citing_submissions":115,"explicit_arxiv_matches":58,"examples":[{"id":"1z1nriEksh","title":"Breaking the Martingale Curse: Multi-Agent Debate via Epistemic Asymmetry","forum_url":"https://openreview.net/forum?id=1z1nriEksh","excerpt":" Geanakoplos. Common knowledge. Journal of Economic Perspectives, 6(4):53–82, 1992. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang. Large language model based multi-agents: A survey of progress and challenges. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, pp. 8048–8057, 2024. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Y","arxiv_match":false},{"id":"x9qK9ehqNi","title":"Coordinated Mixed-Precision KV Cache Quantization for Efficient LLM-based Multi-agent Systems","forum_url":"https://openreview.net/forum?id=x9qK9ehqNi","excerpt":"tional Conference on Learning Representations, volume 2024, pp. 22975–22988, 2024. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, and Xiangliang Zhang. Large language model based multi-agents: A survey of progress and challenges. In Kate Larson (ed.), Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, pp. 8048–8057. International Joint Conferences on Artificial Intelligence O","arxiv_match":false},{"id":"0XTyMGNG5R","title":"ReLay: Are We Ready for Latent Routing for Multi-Agent Systems?","forum_url":"https://openreview.net/forum?id=0XTyMGNG5R","excerpt":"unication in llm-based multi-agent systems. arXiv preprint arXiv:2606.02359, 2026. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, and Xiangliang Zhang. Large language model based multi-agents: a survey of progress and challenges. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI ’24, 2024. ISBN 978-1-956792-04-1. doi: 10.24963/ijcai.2024/890. URL https://doi.org/10.24963/ijca","arxiv_match":false}],"id":"2402.01680","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":874},{"title":"MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention","arxiv_id":"2506.13585","year":2025,"url":"https://arxiv.org/abs/2506.13585","citing_submissions":115,"explicit_arxiv_matches":108,"examples":[{"id":"y89ckFndSX","title":"HazardAuditor: From Executable Threats to Safer Computer-Use Agents","forum_url":"https://openreview.net/forum?id=y89ckFndSX","excerpt":"dvances in Neural Information Processing Systems, 2025. Jianan Ma, Xiaohu Du, Ruixiao Lin, Yaoxiang Bian, Jialuo Chen, Jingyi Wang, Xiaofang Yang, Shiwen Cui, Changhua Meng, Xinhao Deng, et al. Benchmarking autonomous agents against temporal, spatial, and semantic evasions. arXiv preprint arXiv:2605.22321, 2026. MiniMax. Minimax-m1: Scaling test-time compute efficiently with lightning attention. arXiv preprint arXiv:2506.13585, 2025. URL https://arxiv.org/abs/2506.13585. Nous Research. Hermes agent, 2026. OpenAI. Codex. https://openai.com/index/introducing-codex/, 202","arxiv_match":true},{"id":"QbUjIKUlaV","title":"Co-Evolving Policy and Rubric via Capability Awareness for Open-Ended Generation","forum_url":"https://openreview.net/forum?id=QbUjIKUlaV","excerpt":"s improved human health. arXiv preprint arXiv:2505.08775, 2025. Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning, pp. 41–48, 2009. Aili Chen, Aonian Li, Bangwei Gong, Binyang Jiang, Bo Fei, Bo Yang, Boji Shan, Changqing Yu, Chao Wang, Cheng Zhu, et al. Minimax-m1: Scaling test-time compute efficiently with lightning attention. arXiv preprint arXiv:2506.13585, 2025. Hao Chen, Ziyu Han, Yukun Yan, Qingfu Zhu, Maosong Sun, and Wanxiang Che. From holistic eval","arxiv_match":true},{"id":"Dy3RZfaeEs","title":"Do What You Say: Towards Verifiable Reasoning for Scientific Editing","forum_url":"https://openreview.net/forum?id=Dy3RZfaeEs","excerpt":"i Shi, Yiran Zhong, Yonghong Duan, Yongxiang Fu, Yongyi Hu, Yu Gao, Yuanxiang Fan, Yufeng Yang, Yuhao Li, Yulin Hu, Yunan Huang, Yunji Li, Yunzhi Xu, Yuxin Mao, Yuxuan Shi, Yuze Wenren, Zehan Li, Zelin Li, Zhanxu Tian, Zhengmao Zhu, Zhenhua Fan, Zhenzhen Wu, Zhichao Xu, Zhihang Yu, Zhiheng Lyu, Zhuo Jiang, Zibo Gao, Zijia Wu, Zijian Song, and Zijun Sun. Minimax-m1: Scaling test-time compute efficiently with lightning attention, 2025. URL https://arxiv.org/abs/2506.13585. Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awada","arxiv_match":true}],"id":"2506.13585","metadata_source":"Cached arXiv API metadata","rank":875},{"title":"Mixture-of-Depths: Dynamically allocating compute in transformer-based language models","arxiv_id":"2404.02258","year":2024,"url":"https://arxiv.org/abs/2404.02258","citing_submissions":115,"explicit_arxiv_matches":107,"examples":[{"id":"YzI2kFui6F","title":"CoMo: Efficient Video Transformer via Codec Residual and Motion-Field Geometry","forum_url":"https://openreview.net/forum?id=YzI2kFui6F","excerpt":"tion, pp. 12309–12318, 2022. Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh. Dynamicvit: Efficient vision transformers with dynamic token sparsification. Advances in neural information processing systems, 34:13937–13949, 2021. David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, and Adam Santoro. Mixture-of-depths: Dynamically allocating compute in transformer-based language models. arXiv preprint arXiv:2404.02258, 2024. Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitha","arxiv_match":true},{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":" Róbert Csordás, and Jürgen Schmidhuber. Mixture of sparse attention: Content-based learnable sparse attention via expert-choice routing. In NeurIPS 2025 Workshop on Efficient Reasoning, 2025. URL https://openreview.net/forum?id=6JEa0TKZWA. David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, and Adam Santoro. Mixture-of-depths: Dynamically allocating compute in transformer-based language models, 2024. URL https://arxiv.org/abs/2404.02258. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff","arxiv_match":true},{"id":"moFf9vJeAN","title":"WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning","forum_url":"https://openreview.net/forum?id=moFf9vJeAN","excerpt":"al Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4777–4789, San Diego, California, United States, July 2026. Association for Computational Linguistics. ISBN 979-8-89176-390-6. doi: 10.18653/v1/2026.acl-long.218. David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, and Adam Santoro. Mixture-of-Depths: Dynamically allocating compute in transformer-based language models. http://arxiv.org/abs/2404.02258, April 2024. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An ","arxiv_match":true}],"id":"2404.02258","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":876},{"title":"Open-Sora: Democratizing efficient video production for all","arxiv_id":"2412.20404","year":2024,"url":"https://arxiv.org/abs/2412.20404","citing_submissions":115,"explicit_arxiv_matches":104,"examples":[{"id":"9kxTLJwbxB","title":"TraceWorld: Trajectory-Indexed Memory for Spatially Consistent Video World Model","forum_url":"https://openreview.net/forum?id=9kxTLJwbxB","excerpt":"n Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4245–4257, 2026. Guangcong Zheng, Teng Li, Rui Jiang, Yehao Lu, Tao Wu, and Xi Li. Cami2v: Camera-controlled image-to-video diffusion model. arXiv preprint arXiv:2410.15957, 2024a. Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all. arXiv preprint arXiv:2412.20404, 2024b. Jensen Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip To","arxiv_match":true},{"id":"RafiZ0ikvb","title":"IVT-Guard: All-in-One Reasoning Model for AI-Generated Content Detection","forum_url":"https://openreview.net/forum?id=RafiZ0ikvb","excerpt":"image-2.0 technical report. arXiv preprint arXiv:2605.10730, 2026. Chende Zheng, Ruiqi Suo, Chenhao Lin, Zhengyu Zhao, Le Yang, Shuai Liu, Minghui Yang, Cong Wang, and Chao Shen. D3: Training-free ai-generated video detection using second-order features. In ICCV, 2025. Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all. arXiv preprint arXiv:2412.20404, 2024. ZhipuAI. Cogview4-6b. https://huggingface.co/zai-org/CogView4-6B, 2025. Bolei Zhou, Hang Zha","arxiv_match":true},{"id":"D4CEn4EUB8","title":"World Observer: Joint Actor-Observer Generation for Persistent World Modeling","forum_url":"https://openreview.net/forum?id=D4CEn4EUB8","excerpt":"ld models, 2026. URL https://arxiv.org/abs/2603. 17117. Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric, 2018. URL https://arxiv.org/abs/ 1801.03924. Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all, 2024. URL https://arxiv.org/abs/2412.20404. Yixuan Zhu, Jiaqi Feng, Wenzhao Zheng, Yuan Gao, Xin Tao, Pengfei Wan, Jie Zhou, and Jiwen Lu. Ast","arxiv_match":true}],"id":"2412.20404","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":877},{"title":"RLHF: Scaling reinforcement learning from human feedback with AI feedback","arxiv_id":"2309.00267","year":2023,"url":"https://arxiv.org/abs/2309.00267","citing_submissions":115,"explicit_arxiv_matches":43,"examples":[{"id":"Wk9CEDXx8m","title":"Optimal Design for Active Preference Learning with Biased LLM Judges","forum_url":"https://openreview.net/forum?id=Wk9CEDXx8m","excerpt":"arch. PMLR, 2026. Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Ren Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. RLAIF vs. RLHF: Scaling reinforcement learning from human feedback with AI feedback. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 26874–26901. PMLR, 2024. Kimin Lee, Laura M. Smith, and Pieter Ab","arxiv_match":false},{"id":"UrDgYAs0MP","title":"When Good Verifiers Go Bad: Silent Negative Transfer in Verifier-Guided VLM Training","forum_url":"https://openreview.net/forum?id=UrDgYAs0MP","excerpt":"inguistics, 2025. Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Ren Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. RLAIF vs. RLHF: Scaling reinforcement learning from human feedback with AI feedback. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 26874–26901. PMLR, 2024. Xinxin Liu, Ming Li, Zonglin Lyu, Yuzhan","arxiv_match":false},{"id":"7NBlFCtd2e","title":"Radioactive Feedback: Tracing Models Trained by Proprietary LLM Judges","forum_url":"https://openreview.net/forum?id=7NBlFCtd2e","excerpt":"g Research, 2024. Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Ren Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. RLAIF vs. RLHF: Scaling reinforcement learning from human feedback with AI feedback. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 26874–26901, 2024. Kaixin Li. Verified TACO problems. Hugging Fac","arxiv_match":false}],"id":"2309.00267","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":878},{"title":"Reinforced self-training (ReST) for language modeling","arxiv_id":"2308.08998","year":2023,"url":"https://arxiv.org/abs/2308.08998","citing_submissions":115,"explicit_arxiv_matches":102,"examples":[{"id":"PhxAHquiaN","title":"CCL: Counterfactual Contract Learning for Local Execution Recovery in Embodied Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=PhxAHquiaN","excerpt":"n Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, et al. Reinforced self-training (rest) for language modeling. arXiv preprint arXiv:2308.08998, 2023. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang,","arxiv_match":true},{"id":"6N8dYXmsNk","title":"Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation","forum_url":"https://openreview.net/forum?id=6N8dYXmsNk","excerpt":"the v in vqa matter: Elevating the role of image understanding in visual question answering. In CVPR, 2017. Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. MiniLLM: Knowledge distillation of large language models. In ICLR, 2024. Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, et al. Reinforced self-training (rest) for language modeling. arXiv preprint arXiv:2308.08998, 2023. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Z","arxiv_match":true},{"id":"dHl5b1GjT0","title":"ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience","forum_url":"https://openreview.net/forum?id=dHl5b1GjT0","excerpt":"ciates, Inc., 2025. doi: 10.52202/ 085713-1544. URL https://proceedings.neurips.cc/paper_files/paper/ 2025/file/420c9f777c0b4f78d515e53cf74d58b2-Paper-Conference.pdf. Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, and Nando de Freitas. Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998, 2023. Moritz Hardt and Yu Sun. Test-Time Training on Nearest Neighbors for Large Language Models. ","arxiv_match":true}],"id":"2308.08998","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":879},{"title":"Self-Distilled Agentic Reinforcement Learning","arxiv_id":"2605.15155","year":2026,"url":"https://arxiv.org/abs/2605.15155","citing_submissions":115,"explicit_arxiv_matches":109,"examples":[{"id":"1UUiXNNCyo","title":"Sparse Where, Dense Within: Contrastive Privileged Action Distillation for Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=1UUiXNNCyo","excerpt":" reinforcement learning with implicit step rewards, 2025. Zhengxi Lu, Zhiyuan Yao, Zhuowen Han, Zi-Han Wang, Jinyang Wu, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao, Yueting Zhuang, and Yongliang Shen. Self-distilled agentic reinforcement learning, 2026. Leyi Pan, Shuchang Tao, Yunpeng Zhai, Lingzhe Zhang, Zhaoyang Liu, Bolin Ding, Aiwei Liu, and Lijie Wen. RLCSD: Reinforcement learning with contrastive on-policy self-distillation, 2026. Rafae","arxiv_match":false},{"id":"5H9yPPYAla","title":"Tangent On-Policy Distillation: Local-Direction Supervision Beyond Outputs","forum_url":"https://openreview.net/forum?id=5H9yPPYAla","excerpt":"Shijie Xia, Xiaojie Cai, Tianze Xu, et al. Agencybench: Benchmarking the frontiers of autonomous agents in 1mtoken real-world contexts. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7422–7440, 2026c. Zhengxi Lu, Zhiyuan Yao, Zhuowen Han, Zi-Han Wang, Jinyang Wu, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao, Yueting Zhuang, et al. Self-distilled agentic reinforcement learning. arXiv preprint arXiv:2605.15155, 2026. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal poli","arxiv_match":true},{"id":"5iMbperkQz","title":"Context-consistent Teacher Signals for On-policy Distillation","forum_url":"https://openreview.net/forum?id=5iMbperkQz","excerpt":"Shijie Xia, Xiaojie Cai, Tianze Xu, et al. Agencybench: Benchmarking the frontiers of autonomous agents in 1mtoken real-world contexts. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7422–7440, 2026c. Zhengxi Lu, Zhiyuan Yao, Zhuowen Han, Zi-Han Wang, Jinyang Wu, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao, Yueting Zhuang, et al. Self-distilled agentic reinforcement learning. arXiv preprint arXiv:2605.15155, 2026. Ivan Moshkov, Darragh Hanley, Ivan Sorokin, Shubham Toshniwal, Christof Henkel, Benedikt Sch","arxiv_match":true}],"id":"2605.15155","metadata_source":"Cached arXiv API metadata","rank":880},{"title":"Steering Your Diffusion Policy with Latent Space Reinforcement Learning","arxiv_id":"2506.15799","year":2025,"url":"https://arxiv.org/abs/2506.15799","citing_submissions":115,"explicit_arxiv_matches":52,"examples":[{"id":"rCLogsiTWC","title":"Reasoning as a Distribution: Generative Plan Latents for Vision–Language–Action Models","forum_url":"https://openreview.net/forum?id=rCLogsiTWC","excerpt":"entations, 2024. URL https://openreview.net/forum?id=tGQirjzddO. Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, and Sergey Levine. Steering your diffusion policy with latent space reinforcement learning. Conference on Robot Learning, 2025. Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, and Lingpeng Kong. Diffusion of thought: Chain-of","arxiv_match":false},{"id":"U6seufWPDg","title":"AMINO: Generative Auto-bidding with Multimodal Trajectory Modeling and Initial Noise Optimization","forum_url":"https://openreview.net/forum?id=U6seufWPDg","excerpt":"rnational Conference on Machine Learning, pp. 48892–48918, 2024. Andrew Wagenmaker, Yunchu Zhang, Mitsuhiko Nakamoto, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, and Sergey Levine. Steering your diffusion policy with latent space reinforcement learning. In Proceedings of the 9th Conference on Robot Learning, volume 305 of Proceedings of Machine Learning Research, pp. 258–282. PMLR, 2025. Bram Wallace, Akash Gokul, Stefano Ermon, and Nikhil Naik. En","arxiv_match":false},{"id":"aIOL1tsnGQ","title":"DVCA: Denoising-Variance Credit Assignment for Flow-Based Robot Policy Optimization","forum_url":"https://openreview.net/forum?id=aIOL1tsnGQ","excerpt":"sion and Pattern Recognition (CVPR), pp. 22412–22422, June 2026. Andrew Wagenmaker, Yunchu Zhang, Mitsuhiko Nakamoto, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, and Sergey Levine. Steering your diffusion policy with latent space reinforcement learning. In Joseph Lim, Shuran Song, and Hae-Won Park (eds.), Proceedings of The 9th Conference on Robot Learning, volume 305 of Proceedings of Machine Learning Research, pp. 258–282. PMLR, 27–30 Sep 2025. U","arxiv_match":false}],"id":"2506.15799","metadata_source":"Cached arXiv API metadata","rank":881},{"title":"SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines","arxiv_id":"2502.14739","year":2025,"url":"https://arxiv.org/abs/2502.14739","citing_submissions":115,"explicit_arxiv_matches":56,"examples":[{"id":"5iMbperkQz","title":"Context-consistent Teacher Signals for On-policy Distillation","forum_url":"https://openreview.net/forum?id=5iMbperkQz","excerpt":" Xeron Du, Yifan Yao, Kaijing Ma, Bingli Wang, Tianyu Zheng, Minghao Liu, Yiming Liang, Xiaolong Jin, Zhenlin Wei, Chujie Zheng, et al. Supergpqa: Scaling llm evaluation across 285 graduate disciplines. Advances in Neural Information Processing Systems, 38, 2026. Dayuan Fu, Yunze Wu, Xiaojie Cai, Lyumanshan Ye, Shijie Xia, Zhen Huang, Weiye Si, Tianze Xu, Jie Sun, Keyu Li, et al. Argo: Asynchronous","arxiv_match":false},{"id":"NkTmsKRnDh","title":"StageOPD: Stage-aware Loss Reweighting for On-policy Distillation","forum_url":"https://openreview.net/forum?id=NkTmsKRnDh","excerpt":" Xeron Du, Yifan Yao, Kaijing Ma, Bingli Wang, Tianyu Zheng, Minghao Liu, Yiming Liang, Xiaolong Jin, Zhenlin Wei, Chujie Zheng, et al. Supergpqa: Scaling llm evaluation across 285 graduate disciplines. Advances in Neural Information Processing Systems, 38, 2026. Dayuan Fu, Yunze Wu, Xiaojie Cai, Lyumanshan Ye, Shijie Xia, Zhen Huang, Weiye Si, Tianze Xu, Jie Sun, Keyu Li, et al. Argo: Asynchronous","arxiv_match":false},{"id":"bCiKlMuOkz","title":"SafeOPD: Safety-Preserving On-Policy Distillation from Domain-Specialized Teachers","forum_url":"https://openreview.net/forum?id=bCiKlMuOkz","excerpt":"on Learning Representations, volume 2024, pp. 50750–50777, 2024. Xeron Du, Yifan Yao, Kaijing Ma, Bingli Wang, Tianyu Zheng, Minghao Liu, Yiming Liang, Xiaolong Jin, Zhenlin Wei, Chujie Zheng, et al. Supergpqa: Scaling llm evaluation across 285 graduate disciplines. Advances in Neural Information Processing Systems, 38, 2026. Yu Fu, Longxuan Yu, Haz Sameen Shahgir, Zhipeng Wei, Hui Liu, N Benjamin Erichson, and Yue Dong. Reducing the safety tax in llm safety al","arxiv_match":false}],"id":"2502.14739","metadata_source":"Cached arXiv API metadata","rank":882},{"title":"$π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities","arxiv_id":"2604.15483","year":2026,"url":"https://arxiv.org/abs/2604.15483","citing_submissions":114,"explicit_arxiv_matches":101,"examples":[{"id":"dXXc4qNuNM","title":"Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=dXXc4qNuNM","excerpt":"ian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. pi05: a vision-language-action model with open-world generalization. arXiv preprint arXiv:2504.16054, 2025b. Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, et al. pi07: a steerable generalist robotic foundation model with emergent capabilities. arXiv preprint arXiv:2604.15483, 2026. Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison. Rlbench: The robot lear","arxiv_match":true},{"id":"cbhdAcpH22","title":"Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes","forum_url":"https://openreview.net/forum?id=cbhdAcpH22","excerpt":"aset for real-world visual reasoning and compositional question answering. In CVPR, 2019. Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. The platonic representation hypothesis. In ICML, 2024. Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, et al. Pi0.7: a steerable generalist robotic foundation model with emergent capabilities. arXiv preprint arXiv:2604.15483, 2026. Mingkai Jia, Mingxiao Li, Zhijian Shu, Anlin Zheng, Liaoyuan Fan, Jiaxin Guo, Tianxing Shi, ","arxiv_match":true},{"id":"FZ7Aq4uPre","title":"UniWAM: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision","forum_url":"https://openreview.net/forum?id=FZ7Aq4uPre","excerpt":"l features without supervision. arXiv preprint arXiv:2304.07193, 2023. Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, and Elvis Nava. mimic-video: Video-action models for generalizable robot control beyond VLAs. arXiv preprint arXiv:2512.15692, 2025. Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, et al. π0.7 : A steerable generalist robotic foundation model with emergent capabilities. arXiv preprint arXiv:2604.15483, 2026. Santhosh Kumar Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alexander Cl","arxiv_match":true}],"id":"2604.15483","metadata_source":"Cached arXiv API metadata","rank":883},{"title":"ACON: Optimizing Context Compression for Long-horizon LLM Agents","arxiv_id":"2510.00615","year":2025,"url":"https://arxiv.org/abs/2510.00615","citing_submissions":114,"explicit_arxiv_matches":68,"examples":[{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"ong Wang, Hamed Zamani, and Jiawei Han. Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning. In Conference on Language Modeling, 2025. URL https: //arxiv.org/abs/2503.09516. Minki Kang, Wei-Ning Chen, Dongge Han, Huseyin A. Inan, Lukas Wutschitz, Yanzhi Chen, Robert Sim, and Saravan Rajmohan. ACON: Optimizing Context Compression for Long-horizon LLM Agents. arXiv preprint arXiv:2510.00615, 2025. URL https://arxiv.org/abs/2510. 00615. Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhy","arxiv_match":true},{"id":"6Zkm41r219","title":"REx: Small Language Model Agents with Recursive Execution and Subtree Compression","forum_url":"https://openreview.net/forum?id=6Zkm41r219","excerpt":"9. Gemma Team. Gemma 4 technical report. arXiv preprint arXiv:2607.02770, 2026. Minki Kang, Wei-Ning Chen, Dongge Han, Huseyin A. Inan, Lukas Wutschitz, Yanzhi Chen, Robert Sim, and Saravan Rajmohan. ACON: Optimizing context compression for long-horizon LLM agents. In International Conference on Machine Learning, 2026. Mengkang Hu, Tianxing Chen, Qiguang Chen, Yao Mu, Wenqi Shao, and Ping Luo. HiAgent: Hierarchical working memory management for solving long-ho","arxiv_match":false},{"id":"x0TKaMUu1W","title":"Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States","forum_url":"https://openreview.net/forum?id=x0TKaMUu1W","excerpt":"doi: 10.18653/v1/2026.acl-long.464. URL https://aclanthology. org/2026.acl-long.464/. Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra. Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2):99–134, 1998. Minki Kang, Wei-Ning Chen, Dongge Han, Huseyin A Inan, Lukas Wutschitz, Yanzhi Chen, Robert Sim, and Saravan Rajmohan. Acon: Optimizing context compression for long-horizon llm agents. arXiv preprint arXiv:2510.00615, 2025. Chinmaya Kausik, Adith Swaminathan, and Nathan Kallus. The context gathering decision proces","arxiv_match":true}],"id":"2510.00615","metadata_source":"Cached arXiv API metadata","rank":884},{"title":"ALBERT: A lite BERT for self-supervised learning of language representations","arxiv_id":"1909.11942","year":2019,"url":"https://arxiv.org/abs/1909.11942","citing_submissions":114,"explicit_arxiv_matches":44,"examples":[{"id":"wslxAq80Kb","title":"Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models","forum_url":"https://openreview.net/forum?id=wslxAq80Kb","excerpt":"eedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2793–2806, 2020. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. ALBERT: A lite BERT for self-supervised learning of language representations. In International Conference on Learning Representations, 2020. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. BART","arxiv_match":false},{"id":"WDdwx0VKGy","title":"Generalizing Machine-Generated Text Detection to Unseen Generators via Harmful Subspace Removal","forum_url":"https://openreview.net/forum?id=WDdwx0VKGy","excerpt":"Zikang Guo, and Zhendong Mao. M-RangeDetector: Enhancing generalization in machine-generated text detection through multi-range attention masks. In Findings of the Association for Computational Linguistics: ACL 2025, pp. 8971–8983, 2025. doi: 10.18653/v1/2025.findings-acl.469. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942, 2019. Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi,","arxiv_match":true},{"id":"25DdNVKXg6","title":"Shared Computation, Different Readouts: Depth Compatibility in Looped Transformers","forum_url":"https://openreview.net/forum?id=25DdNVKXg6","excerpt":"ational Linguistics, 2019. arXiv preprint Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of Neural Network Representations Revisited. In Proceedings of the 36th International Conference on Machine Learning, volume 97, pp. 3519–3529. PMLR, 2019. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. arXiv preprint arXiv:1909.11942, 2019. Mark Oskin. Off-Axis, On Purpose: Where a Transformer Computes Concepts and Why it Does So. ","arxiv_match":true}],"id":"1909.11942","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":885},{"title":"Do generative video models understand physical principles?","arxiv_id":"2501.09038","year":2025,"url":"https://arxiv.org/abs/2501.09038","citing_submissions":114,"explicit_arxiv_matches":36,"examples":[{"id":"Zf68SX9abN","title":"S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation","forum_url":"https://openreview.net/forum?id=Zf68SX9abN","excerpt":" model. In International Conference on Machine Learning, 2026. URL https://research.nvidia.com/labs/ genair/flex-forcing/. Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, and Robert Geirhos. Do generative video models understand physical principles? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 948–958, 2026. NVIDIA. Cosmos 3: Omnimodal world models for physical ai. arXiv preprint arXiv:2606.02800, 202","arxiv_match":false},{"id":"P6EnVJlhxB","title":"What Can Latent World Models Know? Physical Information in Multimodal Predictive Representations","forum_url":"https://openreview.net/forum?id=P6EnVJlhxB","excerpt":": Clean scenes cannot rank physical inference in pretrained visual representations. arXiv preprint arXiv:2609.08250, 2026. Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, and Robert Geirhos. Do generative video models understand physical principles? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 948–958, 2026. doi: 10.1109/WACV61042.2026.00099. URL https://openaccess.thecvf. com/content/WACV2026/","arxiv_match":false},{"id":"rtlzkXdJe9","title":"PhysiCalWorld: In-Context Physics Calibration for World Models","forum_url":"https://openreview.net/forum?id=rtlzkXdJe9","excerpt":"ld simulator: Crafting physical commonsense-based benchmark for video generation. arXiv preprint arXiv:2410.05363, 2024. 3 Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, and Robert Geirhos. Do generative video models understand physical principles? In 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 948–958. IEEE, 2026. 3 Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pier","arxiv_match":false}],"id":"2501.09038","metadata_source":"Cached arXiv API metadata","rank":886},{"title":"EvoSkill: Automated Skill Discovery for Multi-Agent Systems","arxiv_id":"2603.02766","year":2026,"url":"https://arxiv.org/abs/2603.02766","citing_submissions":114,"explicit_arxiv_matches":100,"examples":[{"id":"Xq1WyguERb","title":"Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery","forum_url":"https://openreview.net/forum?id=Xq1WyguERb","excerpt":" Salaheddin Alzubi, Noah Provenzano, Jaydon Bingham, Weiyuan Chen, and Tu Vu. Evoskill: Automated skill discovery for multi-agent systems. arXiv preprint arXiv:2603.02766, 2026. Akari Asai, Jacqueline He, Rulin Shao, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle ","arxiv_match":true},{"id":"v3S9tm6h0C","title":"RRU: Improving Agent Skill Generation with Reflect-Replay-Update","forum_url":"https://openreview.net/forum?id=v3S9tm6h0C","excerpt":"grawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J Ryan, Meng Jiang, et al. GEPA: Reflective prompt evolution can outperform reinforcement learning. In International Conference on Learning Representations, volume 2026, pp. 8479–8565, 2026. Salaheddin Alzubi, Noah Provenzano, Jaydon Bingham, Weiyuan Chen, and Tu Vu. EvoSkill: Automated skill discovery for multi-agent systems. arXiv preprint arXiv:2603.02766, 2026. Anthropic. Equipping Agents for the Real World with Agent Skills. https: //www.anthropic.com","arxiv_match":true},{"id":"V7gMQjOqcP","title":"Learning from Single-Feedback Deployment Experience","forum_url":"https://openreview.net/forum?id=V7gMQjOqcP","excerpt":"The Tien Mai, and Massimiliano Pontil. Regret bounds for lifelong learning. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pp. 261–269. PMLR, 20–22 Apr 2017. Salaheddin Alzubi, Noah Provenzano, Jaydon Bingham, Weiyuan Chen, and Tu Vu. EvoSkill: Automated skill discovery for multi-agent systems. arXiv preprint arXiv:2603.02766, 2026. URL https://arxiv.org/abs/2603.02766. Anthropic. Claude Code: Overview. https://code.claude.com/docs/en/overview, n.d. Accessed Septembe","arxiv_match":true}],"id":"2603.02766","metadata_source":"Cached arXiv API metadata","rank":887},{"title":"LoRA learns less and forgets less","arxiv_id":"2405.09673","year":2024,"url":"https://arxiv.org/abs/2405.09673","citing_submissions":114,"explicit_arxiv_matches":44,"examples":[{"id":"qeKZk5n57a","title":"Reward-Gradient Aligned Low-Rank Adaptation for Post-Training","forum_url":"https://openreview.net/forum?id=qeKZk5n57a","excerpt":"man, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John Patrick Cunningham. LoRA learns less and forgets less. Transactions on Machine Learning Research, 2024. Yuchen Cai, Ding Cao, Xin Xu, Zijun Yao, Yuqing Huang, Benyi Zhang, Zhenyu Tan, Guiquan Liu, and Junfeng Fang. On predictability of reinforcement lea","arxiv_match":false},{"id":"4QvYQS54l8","title":"LoRA vs. Full Fine-Tuning: A Theoretical Perspective","forum_url":"https://openreview.net/forum?id=4QvYQS54l8","excerpt":"man, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John Patrick Cunningham. LoRA learns less and forgets less. Transactions on Machine Learning Research, 2024. ISSN 2835-8856. Stéphane Boucheron, Gábor Lugosi, and Pascal Massart. Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford Univ","arxiv_match":false},{"id":"YxhULr4TwN","title":"Learning When to Adapt","forum_url":"https://openreview.net/forum?id=YxhULr4TwN","excerpt":"int arXiv:2105.05241, 2021. Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, et al. LoRA learns less and forgets less. Transactions on Machine Learning Research, 2024. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. PIQA: Reasoning about physical commonsense in natural language. In Thirty-F","arxiv_match":false}],"id":"2405.09673","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":888},{"title":"Matrix-game 2.0: An open-source, real-time, and streaming interactive world model","arxiv_id":"2508.13009","year":2025,"url":"https://arxiv.org/abs/2508.13009","citing_submissions":114,"explicit_arxiv_matches":107,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"cy in interactive video world models. arXiv preprint arXiv:2608.27328, 2026b. Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. CameraCtrl: Enabling Camera Control for Text-to-Video Generation. arXiv preprint arXiv:2404.02101, 2024. URL https://arxiv.org/abs/2404.02101. Xianglong He, Chunli Peng, Zexiang Liu, Boyang Wang, et al. Matrix-Game 2.0: An open-source real-time and streaming interactive world model. arXiv preprint arXiv:2508.13009, 2025. Yue Hu, Siyuan Huang, Yue Liao, Shengcong Chen, Pengfei Zhou, Liliang Chen, Maoqing Yao, and","arxiv_match":true},{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"409, 2025. Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, et al. Ltx-video: Realtime video latent diffusion. arXiv preprint arXiv:2501.00103, 2024. Xianglong He, Chunli Peng, Zexiang Liu, Boyang Wang, Yifan Zhang, Qi Cui, Fei Kang, Biao Jiang, Mengyin An, Yangyang Ren, et al. Matrix-game 2.0: An open-source real-time and streaming interactive world model. arXiv preprint arXiv:2508.13009, 2025. Xun Huang, Zhengqi Li, Guande He, Mingyuan Zhou, and Eli Shechtman. Self forcing: Bridging t","arxiv_match":true},{"id":"GM9LePOwrX","title":"Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=GM9LePOwrX","excerpt":"xiang Liu, Boyang Wang, Yifan Zhang, Qi Cui, Fei Kang, Biao Jiang, Mengyin An, Yangyang Ren, Baixin Xu, Hao-Xiang Guo, Kaixiong Gong, Cyrus Wu, Wei Li, Xuchen Song, Yang Liu, Eric Li, and Yahui Zhou. Matrix-game 2.0: An open-source, real-time, and streaming interactive world model. CoRR, abs/2508.13009, 2025. Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet. Video diffusion models. In NeurIPS, 2022. Wenyi Hong, Ming Ding, Wendi","arxiv_match":false}],"id":"2508.13009","metadata_source":"Cached arXiv API metadata","rank":889},{"title":"MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details","arxiv_id":"2507.02546","year":2025,"url":"https://arxiv.org/abs/2507.02546","citing_submissions":114,"explicit_arxiv_matches":36,"examples":[{"id":"DhE1QzCIwU","title":"Learning Temporally-Aware Compact Gaussians for Feed-Forward 4D Reconstruction","forum_url":"https://openreview.net/forum?id=DhE1QzCIwU","excerpt":"6, 2025a. Qianqian Wang, Vickie Ye, Hang Gao, Weijia Zeng, Jake Austin, Zhengqi Li, and Angjoo Kanazawa. Shape of motion: 4d reconstruction from a single video. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9660–9672, 2025b. Ruicheng Wang, Sicheng Xu, Yue Dong, Yu Deng, Jianfeng Xiang, Zelong Lv, Guangzhong Sun, Xin Tong, and Jiaolong Yang. Moge-2: Accurate monocular geometry with metric scale and sharp details. arXiv preprint arXiv:2507.02546, 2025c. Weijie Wang, Yeqing Chen, Zeyu Zhang, Hengyu Liu, Haoxiao Wang, Zhiyuan Feng, Wenkang Qin, ","arxiv_match":true},{"id":"gqKba6Tu8o","title":"EagleDepth: Efficient Fine-Grained Monocular Depth Estimation via Pixel Diffusion Decoder","forum_url":"https://openreview.net/forum?id=gqKba6Tu8o","excerpt":"IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE, 2021. Ruicheng Wang, Sicheng Xu, Yue Dong, Yu Deng, Jianfeng Xiang, Zelong Lv, Guangzhong Sun, Xin Tong, and Jiaolong Yang. Moge-2: Accurate monocular geometry with metric scale and sharp details. Advances in Neural Information Processing Systems, 38:35928–35959, 2026b. Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer. Ta","arxiv_match":false},{"id":"B65JGdo51z","title":"Distilling World Understanding from Video Generators for Robot Control","forum_url":"https://openreview.net/forum?id=B65JGdo51z","excerpt":"perception of the world at any resolution. arXiv preprint arXiv:2409.12191, 2024. Ruicheng Wang, Sicheng Xu, Yue Dong, Yu Deng, Jianfeng Xiang, Zelong Lv, Guangzhong Sun, Xin Tong, and Jiaolong Yang. MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details. Advances in Neural Information Processing Systems, 38:35928– 35959, 2026d. Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng, Shenyuan Gao, Sihyun Yu, George Kurian, Suneel Indupuru, You Liang Tan, Chuning Zh","arxiv_match":false}],"id":"2507.02546","metadata_source":"Cached arXiv API metadata","rank":890},{"title":"Show your work: Scratchpads for intermediate computation with language models","arxiv_id":"2112.00114","year":2021,"url":"https://arxiv.org/abs/2112.00114","citing_submissions":114,"explicit_arxiv_matches":93,"examples":[{"id":"m4ccvE2AMl","title":"SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time","forum_url":"https://openreview.net/forum?id=m4ccvE2AMl","excerpt":"it S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. Advances in neural information processing systems, 26, 2013. Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, et al. Show your work: Scratchpads for intermediate computation with language models. In Deep Learning for Code Workshop, 2022. Fawaz Sammani, Tzoulio Chamiti, and Nikos Deligiannis. On test-time scaling for vision-language models. In Computer Vision – ECCV 2026, pp. 168–185, 2026. Z","arxiv_match":false},{"id":"B1kMCDbu0l","title":"Seeing and Solving Are Not Enough for Vision-Language Models","forum_url":"https://openreview.net/forum?id=B1kMCDbu0l","excerpt":"chmark for question answering about charts with visual and logical reasoning. In Findings of the Association for Computational Linguistics (Findings of ACL), pp. 2263–2279, 2022. Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. Show your work: Scratchpads for intermediate computation with language models. arXiv preprint arXiv:2112.00114, 2021. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. Measuring ","arxiv_match":true},{"id":"3QRcMlJ4ic","title":"Plan Before You Denoise: Semantic Availability in Image Generation","forum_url":"https://openreview.net/forum?id=3QRcMlJ4ic","excerpt":"tradeoff: Limitations of log-precision transformers. Transactions of the Association for Computational Linguistics, 11:531–545, 2023. doi: 10.1162/tacl_a_00562. Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. Show your work: Scratchpads for intermediate computation with language models, 2021. URL https://arxiv.org/abs/2112.00114. William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of th","arxiv_match":true}],"id":"2112.00114","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":891},{"title":"VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs","arxiv_id":"2406.07476","year":2024,"url":"https://arxiv.org/abs/2406.07476","citing_submissions":114,"explicit_arxiv_matches":111,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":"nan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In CVPR, 2024c. Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, and Lidong Bing. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv:2406.07476, 2024. Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Junfei Wu, Xi","arxiv_match":true},{"id":"w8aZY88Jbh","title":"ReVA: A New Scene-Centric Dataset for Remote Sensing Video Question Answering","forum_url":"https://openreview.net/forum?id=w8aZY88Jbh","excerpt":"aux, and Devis Tuia. Promptrsvqa: Prompting visual context to a language model for remote sensing visual question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1372–1381, 2022. Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thoma","arxiv_match":true},{"id":"9XqqxYouLj","title":"Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning","forum_url":"https://openreview.net/forum?id=9XqqxYouLj","excerpt":"technical report. arXiv preprint arXiv:2502.13923, 2025. 6 Junhao Cheng, Yuying Ge, Teng Wang, Yixiao Ge, Jing Liao, and Ying Shan. Video-holmes: Can MLLM think like holmes for complex video reasoning? CoRR, abs/2505.21374, 2025. 6 Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. 6 Xiangxiang Chu, Hailang Huang, Xiao Zhang, Fei Wei, and Yong Wang. GPG: A simple and stron","arxiv_match":true}],"id":"2406.07476","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":892},{"title":"Deep variational information bottleneck","arxiv_id":"1612.00410","year":2016,"url":"https://arxiv.org/abs/1612.00410","citing_submissions":113,"explicit_arxiv_matches":38,"examples":[{"id":"yECLVPwR3R","title":"Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation","forum_url":"https://openreview.net/forum?id=yECLVPwR3R","excerpt":" Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. Deep Variational Information Bottleneck. In International Conference on Learning Representations, 2017. URL https: //openreview.net/forum?id=HyxQzBceg. Anthropic. Claude Opus 4.6 System Card. System card, 2026. URL https://anthropic. com/c","arxiv_match":false},{"id":"3gnF7aPtNF","title":"AuDiAE: Learning Audio Latents by Coupling Diffusion Priors and Decoders","forum_url":"https://openreview.net/forum?id=3gnF7aPtNF","excerpt":"l, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, Matt Sharifi, Neil Zeghidour, and Christian Frank. MusicLM: Generating music from text. arXiv preprint arXiv:2301.11325, 2023. URL https://arxiv.org/abs/2301.11325. Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. Deep variational information bottleneck. In International Conference on Learning Representations, 2017. URL https: //arxiv.org/abs/1612.00410. Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. VGGSound: A large-scale audiovisual","arxiv_match":true},{"id":"wkIcLNpxYS","title":"Hidden Label Distinctions Change the Gain from Representation Rate","forum_url":"https://openreview.net/forum?id=wkIcLNpxYS","excerpt":" Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. Deep variational information bottleneck. In International Conference on Learning Representations, 2017. URL https: //openreview.net/forum?id=HyxQzBceg. Alljoined. Alljoined-1.6M dataset card, 2025. URL https://huggingface.co/datasets/ Allj","arxiv_match":false}],"id":"1612.00410","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":893},{"title":"Emergent world representations: Exploring a sequence model trained on a synthetic task","arxiv_id":"2210.13382","year":2022,"url":"https://arxiv.org/abs/2210.13382","citing_submissions":113,"explicit_arxiv_matches":36,"examples":[{"id":"UnQVghvXji","title":"Beyond OOD: Evaluating Large Foundation Models via Out-of-World Distribution","forum_url":"https://openreview.net/forum?id=UnQVghvXji","excerpt":"tion. In Proceedings of the IEEE International Conference on Computer Vision, pp. 5542–5550, 2017. Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Emergent world representations: Exploring a sequence model trained on a synthetic task. In International Conference on Learning Representations, 2023b. Jiaxuan Li, Lang Yu, and Allyson Ettinger. Counterfactual reasoning: Testing language models’ understanding of hypothetical scenarios.","arxiv_match":false},{"id":"SqiTOxOtoj","title":"Have I Seen Enough? Frozen Video-Language Models Encode Evidence Readiness","forum_url":"https://openreview.net/forum?id=SqiTOxOtoj","excerpt":" Transactions on Machine Learning Research, 2025. URL https://openreview.net/ forum?id=zKv8qULV6n. Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Emergent world representations: Exploring a sequence model trained on a synthetic task. In International Conference on Learning Representations (ICLR), 2023a. Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Inferencetime intervention: Eliciting truthf","arxiv_match":false},{"id":"wEvzWAKzLV","title":"Context Is King: How In-Context Specification Shapes the Geometry of Concepts","forum_url":"https://openreview.net/forum?id=wEvzWAKzLV","excerpt":", and K. Filippova. Language models struggle to use representations learned in-context. arXiv preprint arXiv:2602.04212, 2026. K. Li, A. K. Hopkins, D. Bau, F. Viégas, H. Pfister, and M. Wattenberg. Emergent world representations: exploring a sequence model trained on a synthetic task. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=DeG07 TcZvT. T. Lieberum, M. Rahtz, J. Kramár, N. Nanda, G. Irving, R. Shah, and V. Mikulik. Does ","arxiv_match":false}],"id":"2210.13382","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":894},{"title":"FlashAttention-3: Fast and accurate attention with asynchrony and low-precision","arxiv_id":"2407.08608","year":2024,"url":"https://arxiv.org/abs/2407.08608","citing_submissions":113,"explicit_arxiv_matches":30,"examples":[{"id":"6uvDXBEO71","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=6uvDXBEO71","excerpt":"t, attending smartly: Training-free acceleration for diffusion transformers. arXiv preprint arXiv:2505.14687, 2025. Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, and Tri Dao. Flashattention-3: Fast and accurate attention with asynchrony and low-precision. Advances in Neural Information Processing Systems, 37:68658–68685, 2024. Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, and Song Han. Quest: Query-aware sparsity for efficient lo","arxiv_match":false},{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"allenge at scale. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 8732–8740, 2020. Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, and Tri Dao. FlashAttention-3: Fast and accurate attention with asynchrony and low-precision. Advances in Neural Information Processing Systems, 37:68658–68685, 2024. Adam Stooke, Joshua Achiam, and Pieter Abbeel. Responsive safety in reinforcement learning by PID Lagrangian methods. In Inte","arxiv_match":false},{"id":"r9EgYOUmRo","title":"Saevia: Spatiotemporal Token Compression with Attention Compensation for Efficient Video Understanding","forum_url":"https://openreview.net/forum?id=r9EgYOUmRo","excerpt":"rIPS, 2021. URL https://papers.nips.cc/paper_files/paper/2021/hash/ 6a30e32e56fce5cf381895dfe6ca7b6f-Abstract.html. Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, and Tri Dao. FlashAttention-3: Fast and accurate attention with asynchrony and lowprecision. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 68658–68685. Curran Associates, Inc., 2","arxiv_match":false}],"id":"2407.08608","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":895},{"title":"Learning invariant representations for reinforcement learning without reconstruction","arxiv_id":"2006.10742","year":2020,"url":"https://arxiv.org/abs/2006.10742","citing_submissions":113,"explicit_arxiv_matches":38,"examples":[{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":" theory to parallel computation. Journal of Guidance, Control, and Dynamics, 40 (2):344–357, 2017. doi: 10.2514/1.G001921. Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine. Learning invariant representations for reinforcement learning without reconstruction. In International Conference on Learning Representations, 2021a. URL https://openreview.net/forum?id=-2FCwDKRREu. Lunjun Zhang, Ge Yang, and Bradly C. Stadie. World model as a graph: Learning latent ","arxiv_match":false},{"id":"GZdwmSCjJs","title":"Juno: Taming Predictive Latents for Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=GZdwmSCjJs","excerpt":"In International Conference on Learning Representations, volume 2025, pp. 28213–28239, 2025. Ge Yuan, Qiyuan Qiao, Jing Zhang, and Dong Xu. Adaworldpolicy: World-model-driven diffusion policy with online adaptive learning for robotic manipulation. arXiv preprint arXiv:2602.20057, 2026. Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine. Learning invariant representations for reinforcement learning without reconstruction. arXiv preprint arXiv:2006.10742, 2020. Tianyuan Zhang, Sai Bi, Yicong Hong, Kai Zhang, Fujun Luan, Songlin Yang, Kalyan Sunkavalli,","arxiv_match":true},{"id":"vBuP2bGyw9","title":"Post-Training Multimodal Embedding Models towards Physical Dynamics Awareness","forum_url":"https://openreview.net/forum?id=vBuP2bGyw9","excerpt":"2022. URL https://arxiv.org/abs/2205.01917. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid Loss for Language Image Pre-Training. arXiv preprint arXiv:2303.15343, 2023. URL https://arxiv.org/ abs/2303.15343. Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine. Learning Invariant Representations for Reinforcement Learning without Reconstruction. arXiv preprint arXiv:2006.10742, 2020. URL https://arxiv.org/abs/2006.10742. Jie Zhang, Xiaoyue Chen, Anzhe Chen, Dayiheng Liu, Deqing Li, Gengze Zhou, Hale Yin, Haoqi Yuan, H","arxiv_match":true}],"id":"2006.10742","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":896},{"title":"Less is More: Recursive Reasoning with Tiny Networks","arxiv_id":"2510.04871","year":2025,"url":"https://arxiv.org/abs/2510.04871","citing_submissions":113,"explicit_arxiv_matches":109,"examples":[{"id":"IbYlE3lDII","title":"When Wrong Answers Remain Unsettled: Failure Dynamics in Recurrent Reasoners","forum_url":"https://openreview.net/forum?id=IbYlE3lDII","excerpt":"me compute with latent reasoning: A recurrent depth approach. In Advances in Neural Information Processing Systems (NeurIPS), 2025. URL https://arxiv.org/abs/2502.05171. Alex Graves. Adaptive computation time for recurrent neural networks. arXiv preprint arXiv:1603.08983, 2016. URL https://arxiv.org/abs/1603.08983. Alexia Jolicoeur-Martineau. Less is more: Recursive reasoning with tiny networks. arXiv preprint arXiv:2510.04871, 2025. URL https://arxiv.org/abs/2510.04871. Ferdinand Kapl, Emmanouil Angelis, Kaitlin Maile, Johannes von Oswald, and Stefan Bauer. From grow","arxiv_match":true},{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"https://proceedings.neurips.cc/paper_files/paper/2024/ file/03469b1a66e351b18272be23baf3b809-Paper-Conference.pdf. Peng Jin, Bo Zhu, Li Yuan, and Shuicheng YAN. Moe++: Accelerating mixture-of-experts methods with zero-computation experts. In International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=t7P5BUKcYv. Alexia Jolicoeur-Martineau. Less is more: Recursive reasoning with tiny networks, 2025. URL https://arxiv.org/abs/2510.04871. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray,","arxiv_match":true},{"id":"dcQKvnC6cP","title":"Learning from Recurrence: Internalizing Depth-Induced Preferences in Looped Language Models","forum_url":"https://openreview.net/forum?id=dcQKvnC6cP","excerpt":". In International Conference on Learning Representations, 2018. Ahmadreza Jeddi, Marco Ciccone, and Babak Taati. LoopFormer: Elastic-depth looped transformers for latent reasoning via shortcut modulation. In International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=RzYXb5YWBs. Alexia Jolicoeur-Martineau. Less is more: Recursive reasoning with tiny networks. arXiv preprint arXiv:2510.04871, 2025. URL https://arxiv.org/abs/2510.04871. Yoon Kim and Alexander M. Rush. Sequence-level knowledge distillation. In Proceedings of the 2016 ","arxiv_match":true}],"id":"2510.04871","metadata_source":"Cached arXiv API metadata","rank":897},{"title":"Mamba-3: Improved Sequence Modeling using State Space Principles","arxiv_id":"2603.15569","year":2026,"url":"https://arxiv.org/abs/2603.15569","citing_submissions":113,"explicit_arxiv_matches":37,"examples":[{"id":"xUxa60BiCG","title":"SLiK: Learned Sturm–Liouville Spectral Geometry for Sequence Modeling","forum_url":"https://openreview.net/forum?id=xUxa60BiCG","excerpt":"s of features from tiny images. 2009. URL https: //api.semanticscholar.org/CorpusID:18268744. Aakash Sunil Lahoti, Kevin Li, Berlin Chen, Caitlin Wang, Aviv Bick, Zico Kolter, Tri Dao, and Albert Gu. Mamba-3: Improved sequence modeling using state space principles. In International Conference on Learning Representations, volume 2026, pp. 86173–86202, 2026. James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, and Santiago Ontanon. Fnet: Mixing tokens with fourier tr","arxiv_match":false},{"id":"8EzQDRLyfM","title":"Selective but Not Broad: Retrieval Capacity and Accessibility in State-Space Models","forum_url":"https://openreview.net/forum?id=8EzQDRLyfM","excerpt":"ear attention. In Proceedings of the 37th International Conference on Machine Learning, 2020. Aakash Lahoti, Kevin Y. Li, Berlin Chen, Caitlin Wang, Aviv Bick, J. Zico Kolter, Tri Dao, and Albert Gu. Mamba-3: Improved sequence modeling using state space principles, 2026. ICLR 2026. Zhong Li, Jiequn Han, Weinan E, and Qianxiao Li. On the curse of memory in recurrent neural networks: Approximation and optimization analysis. In International Conference on Learnin","arxiv_match":false},{"id":"xJcUIBSxEE","title":"Elastic Selective Spectral Hybrids for Train-Once, Export-Many Budgeted Inference","forum_url":"https://openreview.net/forum?id=xJcUIBSxEE","excerpt":"ing research. Transactions of the Association for Computational Linguistics, 7:453–466, 2019. Aakash Sunil Lahoti, Kevin Li, Berlin Chen, Caitlin Wang, Aviv Bick, Zico Kolter, Tri Dao, and Albert Gu. Mamba-3: Improved sequence modeling using state space principles. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 86173–86202, 2026. URL https://proceedings.icl","arxiv_match":false}],"id":"2603.15569","metadata_source":"Cached arXiv API metadata","rank":898},{"title":"Multimodal chain-of-thought reasoning in language models","arxiv_id":"2302.00923","year":2023,"url":"https://arxiv.org/abs/2302.00923","citing_submissions":113,"explicit_arxiv_matches":68,"examples":[{"id":"aIio1AlTq2","title":"Latent Noise Mask Eliminates Visual Redundancy of Multimodal Large Language Models","forum_url":"https://openreview.net/forum?id=aIio1AlTq2","excerpt":"t arXiv:2511.11007, 2025b. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pp. 169–186. Springer, 2024. Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models. arXiv preprint arXiv:2302.00923, 2023. Shitian Zhao, Haoquan Zhang, Shaoheng Lin, Ming Li, Qilong Wu, Kaipeng Zhang, and Chen Wei. ","arxiv_match":true},{"id":"UKkwWcghJj","title":"Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning","forum_url":"https://openreview.net/forum?id=UKkwWcghJj","excerpt":"ning to see fine details for multimodal LLMs via on-policy self-distillation. arXiv preprint arXiv:2605.18740, 2026. Xinsen Zhang, Zhenkai Ding, Tianjun Pan, Run Yang, Chun Kang, Xue Xiong, and Jingnan Gu. OPSDL: On-policy self-distillation for long-context language models. arXiv preprint arXiv:2604.17535, 2026. Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models. arXiv preprint arXiv:2302.00923, 2023. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover. ","arxiv_match":true},{"id":"B1kMCDbu0l","title":"Seeing and Solving Are Not Enough for Vision-Language Models","forum_url":"https://openreview.net/forum?id=B1kMCDbu0l","excerpt":"niCPM-V 4.5: Cooking efficient MLLMs via architecture, data, and training recipe. arXiv preprint arXiv:2509.18154, 2025. Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models. Transactions on Machine Learning Research (TMLR), 2024. A Table 7 gives the question counts behind the composition-failure rates in Section 4.2. In every evaluated setting, Direct fails on a substan","arxiv_match":false}],"id":"2302.00923","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":899},{"title":"Not all language model features are one-dimensionally linear","arxiv_id":"2405.14860","year":2024,"url":"https://arxiv.org/abs/2405.14860","citing_submissions":113,"explicit_arxiv_matches":37,"examples":[{"id":"fXt5rH6mod","title":"Position: The Linear Representation Hypothesis Needs a Group Action","forum_url":"https://openreview.net/forum?id=fXt5rH6mod","excerpt":" Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al. Toy models of superposition. arXiv preprint arXiv:2209.10652, 2022. Josh Engels, Eric Michaud, Isaac Liao, Wes Gurnee, and Max Tegmark. Not all language model features are one-dimensionally linear. In International Conference on Learning Representations, volume 2025, pp. 84591–84622, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/file/ d3221cdb27e49d9c1cd35ad254feccfe-Paper-Confe","arxiv_match":false},{"id":"0r44FPmIK4","title":"Beyond Linear Representation Hypothesis: Non-Linear Activation Steering in Text-to-Image Models","forum_url":"https://openreview.net/forum?id=0r44FPmIK4","excerpt":"ference on Machine Learning, 2025. URL https://openreview.net/forum?id=6N0GxaKdX9. Yusuf Dalva and Pinar Yanardag. Noiseclr: A contrastive learning approach for unsupervised discovery of interpretable directions in diffusion models. Computer Vision and Pattern Recognition, 2023. doi: 10.1109/CVPR52733.2024.02285. Joshua Engels, Eric J. Michaud, Isaac Liao, Wes Gurnee, and Max Tegmark. Not all language model features are one-dimensionally linear. arXiv preprint arXiv: 2405.14860, 2024. Tatiana Gaintseva, Chengcheng Ma, Ziquan Liu, Martin Benning, Gregory Slabaugh, Jiankang Den","arxiv_match":true},{"id":"QZzzVO39xA","title":"A Transport Law for Linear Directions Between Language Models","forum_url":"https://openreview.net/forum?id=QZzzVO39xA","excerpt":"ler. Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics, 90(3):414–427, 2008. Joshua Engels, Eric J Michaud, Isaac Liao, Wes Gurnee, and Max Tegmark. Not all language model features are one-dimensionally linear. In The Thirteenth International Conference on Learning Representations, 2025. Adrián Csiszárik, Péter Kőrösi-Szabó, Ákos K. Matszangosz, Gergely Papp, and Dániel Varga. Similarity and matchi","arxiv_match":false}],"id":"2405.14860","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":900},{"title":"Pitfalls of graph neural network evaluation","arxiv_id":"1811.05868","year":2018,"url":"https://arxiv.org/abs/1811.05868","citing_submissions":113,"explicit_arxiv_matches":74,"examples":[{"id":"splSFnPJdt","title":"Decision Transparency: When Can a Black-Box Decision Reveal Its Hidden Information?","forum_url":"https://openreview.net/forum?id=splSFnPJdt","excerpt":"oceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 51471–51488, 2025. Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1:206–215, 2019. Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868, 2018. Mattia Silvestri, Senne Berden, Gaetano Signorelli, Ali Irfan Mahmutogullari, Jayanta Mandi,","arxiv_match":true},{"id":"f7p5NRwcKR","title":"FedDiG: Federated Continual Graph Learning with Disentangled Generative Replay","forum_url":"https://openreview.net/forum?id=f7p5NRwcKR","excerpt":"kova. A critical look at the evaluation of GNNs under heterophily: Are we really making progress? In ICLR, 2023. Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell. Progressive neural networks. arXiv preprint arXiv:1606.04671, 2016. Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868, 2018. Jonathan M Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M Dongh","arxiv_match":true},{"id":"Id9RBNSwPz","title":"Federated Few-Shot Graph Domain Generalization with Non-Aligned Label Spaces","forum_url":"https://openreview.net/forum?id=Id9RBNSwPz","excerpt":"s under heterophily: Are we really making progress? arXiv preprint arXiv:2302.11640, 2023. Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith. On the convergence of federated optimization in heterogeneous networks. CoRR, abs/1812.06127, 2018. URL http://arxiv.org/abs/1812.06127. Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868, 2018. Jie Wen, Zhixia Zhang, Yang Lan, Zhihua Cui, Jianghui Cai, and Wensheng Zhang. A survey on f","arxiv_match":true}],"id":"1811.05868","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":901},{"title":"Sundial: A Family of Highly Capable Time Series Foundation Models","arxiv_id":"2502.00816","year":2025,"url":"https://arxiv.org/abs/2502.00816","citing_submissions":113,"explicit_arxiv_matches":37,"examples":[{"id":"8Awrl0pIuq","title":"Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=8Awrl0pIuq","excerpt":"me Series Forecasting. In International Conference on Learning Representations (ICLR), 2025a. Yong Liu, Guo Qin, Zhiyuan Shi, Zhi Chen, Caiyin Yang, Xiangdong Huang, Jianmin Wang, and Mingsheng Long. Sundial: A Family of Highly Capable Time Series Foundation Models. In International Conference on Machine Learning (ICML), 2025b. Lennart Ljung. System Identification: Theory for the User. Prentice Hall, Upper Saddle River, NJ, 2nd edition, 1999. Anurag Mishra. Dis","arxiv_match":false},{"id":"LJ3tnmkITu","title":"Recurrent Domain Incremental Adaptation of Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=LJ3tnmkITu","excerpt":"ry and Data Mining, KDD 2024, Barcelona, Spain, August 25-29, 2024, pp. 6555–6565. ACM, 2024. Yong Liu, Guo Qin, Zhiyuan Shi, Zhi Chen, Caiyin Yang, Xiangdong Huang, Jianmin Wang, and Mingsheng Long. Sundial: A family of highly capable time series foundation models. In Fortysecond International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13-19, 2025, volume 267 of Proceedings of Machine Learning Research. PMLR / OpenReview.net, 2025. ","arxiv_match":false},{"id":"iaMmGPCgNV","title":"Recursive Inference for Test-Time Compute Allocation in Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=iaMmGPCgNV","excerpt":"arning, volume 235 of Proceedings of Machine Learning Research, pp. 32369–32399. PMLR, 2024b. Yong Liu, Guo Qin, Zhiyuan Shi, Zhi Chen, Caiyin Yang, Xiangdong Huang, Jianmin Wang, and Mingsheng Long. Sundial: A family of highly capable time series foundation models. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 39295–39317. PMLR, 2025. Aman Madaan, Niket Tandon, Prakhar Gupta","arxiv_match":false}],"id":"2502.00816","metadata_source":"Cached arXiv API metadata","rank":902},{"title":"Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents","arxiv_id":"2601.01885","year":2026,"url":"https://arxiv.org/abs/2601.01885","citing_submissions":112,"explicit_arxiv_matches":34,"examples":[{"id":"NLjjgPN2po","title":"MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents","forum_url":"https://openreview.net/forum?id=NLjjgPN2po","excerpt":" Shi, Mi Yang, et al. Step-gui technical report. arXiv preprint arXiv:2512.15431, 2025. Woongyeong Yeo, Kangsan Kim, Jaehong Yoon, and Sung Ju Hwang. Worldmm: Dynamic multimodal memory agent for long video reasoning. arXiv preprint arXiv:2512.02425, 2025. Yi Yu, Liuyi Yao, Yuexiang Xie, Qingquan Tan, Jiaqi Feng, Yaliang Li, and Libing Wu. Agentic memory: Learning unified long-term and short-term memory management for large language model agents. arXiv preprint arXiv:2601.01885, 2026. Ziyun Zeng, Hang Hua, and Jiebo Luo. Mira: Multimodal iterative reasoning agent for image ed","arxiv_match":true},{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":"l policy and memory bank for llm agents. arXiv preprint arXiv:2608.01739, 2026. Jing Yu, Yibo Zhao, Jiaming Zhang, and Xiang Li. Lazymem: Retrieve broadly, construct selectively for efficient long-term agent memory. arXiv preprint arXiv:2607.22690, 2026a. Yi Yu, Liuyi Yao, Yuexiang Xie, Qingquan Tan, Jiaqi Feng, Yaliang Li, and Libing Wu. Agentic memory: Learning unified long-term and short-term memory management for large language model agents. arXiv preprint arXiv:2601.01885, 2026b. Aohan Zeng, Xin Lv, Zhenyu Hou, Zhengxiao Du, Qinkai Zheng, Bin Chen, Da Yin, Chendi Ge, Ch","arxiv_match":true},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":"s://proceedings.neurips.cc/paper_files/paper/2025/hash/ a4277440d50f1f15d2cb4c14f7e0c0d2-Abstract-Conference.html. Yi Yu, Liuyi Yao, Yuexiang Xie, Qingquan Tan, Jiaqi Feng, Yaliang Li, and Libing Wu. Agentic memory: Learning unified long-term and short-term memory management for large language model agents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 21457–21483. Association for Computational Linguistics, 2026b. URL https://acl","arxiv_match":false}],"id":"2601.01885","metadata_source":"Cached arXiv API metadata","rank":903},{"title":"Agentic Reinforced Policy Optimization","arxiv_id":"2507.19849","year":2025,"url":"https://arxiv.org/abs/2507.19849","citing_submissions":112,"explicit_arxiv_matches":52,"examples":[{"id":"xmHs4HLScl","title":"Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning","forum_url":"https://openreview.net/forum?id=xmHs4HLScl","excerpt":"ural Information Processing Systems, 38:131646–131671, 2026. Guanting Dong, Hangyu Mao, Kai Ma, Licheng Bao, Yifei Chen, Zhongyuan Wang, Zhongxia Chen, Jiazhen Du, Huiyang Wang, Fuzheng Zhang, et al. Agentic reinforced policy optimization. In International Conference on Learning Representations, volume 2026, pp. 16981–17017, 2026. Kyle Cranmer, Juan Pavez, and Gilles Louppe. Approximating likelihood ratios with calibrated discriminati","arxiv_match":false},{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"echnical report, 2025. URL https://arxiv.org/abs/2502.13923. Guanting Dong, Hangyu Mao, Kai Ma, Licheng Bao, Yifei Chen, Zhongyuan Wang, Zhongxia Chen, Jiazhen Du, Huiyang Wang, Fuzheng Zhang, et al. Agentic reinforced policy optimization. In International Conference on Learning Representations, volume 2026, pp. 16981–17017, 2026. Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An. Group-in-group policy optimization for llm agent traini","arxiv_match":false},{"id":"hupwdXzUgM","title":"When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation","forum_url":"https://openreview.net/forum?id=hupwdXzUgM","excerpt":".2510.14545. Guanting Dong, Hangyu Mao, Kai Ma, Licheng Bao, Yifei Chen, Zhongyuan Wang, Zhongxia Chen, Jiazhen Du, Huiyang Wang, Fuzheng Zhang, Guorui Zhou, Yutao Zhu, Ji-Rong Wen, and Zhicheng Dou. Agentic reinforced policy optimization. CoRR, abs/2507.19849, 2025b. doi: 10. 48550/ARXIV.2507.19849. URL https://doi.org/10.48550/arXiv.2507.19849. Gongfan Fang, Xinyin Ma, and Xinchao Wang. Thinkless: LLM learns when to think. In Daniel","arxiv_match":false}],"id":"2507.19849","metadata_source":"Cached arXiv API metadata","rank":904},{"title":"Dreamsim: Learning new dimensions of human visual similarity using synthetic data","arxiv_id":"2306.09344","year":2023,"url":"https://arxiv.org/abs/2306.09344","citing_submissions":112,"explicit_arxiv_matches":45,"examples":[{"id":"7x4AAlYBDM","title":"Stabilizing Multi-Step Diffusion Distillation with Student Perturbation Consistency","forum_url":"https://openreview.net/forum?id=7x4AAlYBDM","excerpt":" metric for machine learning. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data, 2023. Risheek Garrepalli, Shweta Mahajan, Munawar Hayat, and Fatih Porikli. Ddil: Diversity enhancing diffusion distillation with imitation learning, 2025. URL https://arxiv.org/abs/2410. 11971. Zhe","arxiv_match":false},{"id":"WsLUWEDwZr","title":"Amortized Moment Matching for Visual Generation","forum_url":"https://openreview.net/forum?id=WsLUWEDwZr","excerpt":"avid Haldimann, Sai Aitharaju, Victor G Turrisi da Costa, Louis Béthune, Zhe Gan, et al. Multimodal autoregressive pre-training of large vision encoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9641–9654, 2025. Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. arXiv preprint arXiv:2306.09344, 2023. Zhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, and Kaiming He. Mean flows for o","arxiv_match":true},{"id":"IPOA8cdiZU","title":"BRIDGE: Background Routing and Isolated Discrete Gating for Coarse-Mask Local Editing","forum_url":"https://openreview.net/forum?id=IPOA8cdiZU","excerpt":"f Proceedings of Machine Learning Research, pp. 12606–12633. PMLR, 2024. URL https://proceedings.mlr.press/v235/esser24a.html. S. Fu, N. Tamir, S. Sundaram, L. Chai, R. Zhang, T. Dekel, and P. Isola. DreamSim: Learning new dimensions of human visual similarity using synthetic data. In NeurIPS, 2023. A. B. Gokmen, Y. Ekin, B. B. Bilecen, and A. Dundar. RoPECraft: Training-free motion transfer with trajectory-guided RoPE optimization on diffusion transformers. In NeurIPS, 2025. ","arxiv_match":false}],"id":"2306.09344","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":905},{"title":"Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models","arxiv_id":"2504.12626","year":2025,"url":"https://arxiv.org/abs/2504.12626","citing_submissions":112,"explicit_arxiv_matches":37,"examples":[{"id":"9kxTLJwbxB","title":"TraceWorld: Trajectory-Indexed Memory for Spatially Consistent Video World Model","forum_url":"https://openreview.net/forum?id=9kxTLJwbxB","excerpt":"d Xihui Liu. Context as memory: Scene-consistent interactive long video generation with memory retrieval. pp. 1–11, 2025b. Lvmin Zhang, Shengqu Cai, Muyang Li, Gordon Wetzstein, and Maneesh Agrawala. Frame context packing and drift prevention in next-frame-prediction video diffusion models. Advances in Neural Information Processing Systems, 38:30546–30566, 2026. Jinjing Zhao, Fangyun Wei, Zhening Liu, Hongyang Zhang, Chang Xu, and Yan Lu. Spatia: Video generation with updatable spatial","arxiv_match":false},{"id":"h9oDIdD6ok","title":"Cine-Long: Consistent Long-Horizon Multi-Shot Audio-Visual Generation","forum_url":"https://openreview.net/forum?id=h9oDIdD6ok","excerpt":"ti-shot long video storytelling with memory. arXiv preprint arXiv:2512.19539, 2025a. URL https://arxiv.org/abs/2512.19539. Lvmin Zhang, Shengqu Cai, Muyang Li, Gordon Wetzstein, and Maneesh Agrawala. Frame context packing and drift prevention in next-frame-prediction video diffusion models. In Advances in Neural Information Processing Systems, volume 38, 2025b. URL https://proceedings.neurips.cc/paper_files/paper/2025/hash/ 2bde8fef08f7ebe42b584266cbcfc909-Abstract-Conference.html. Pro","arxiv_match":false},{"id":"bI2MFCxHdB","title":"Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion","forum_url":"https://openreview.net/forum?id=bI2MFCxHdB","excerpt":"L 2026 Workshop on Foundations of Deep Generative Models: Understanding Memorization, Generalization, and Reasoning, 2026. Lvmin Zhang, Shengqu Cai, Muyang Li, Gordon Wetzstein, and Maneesh Agrawala. Frame context packing and drift prevention in next-frame-prediction video diffusion models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025a. Tianyuan Zhang, Sai Bi, Yicong Hong, Kai Zhang, Fujun Luan, Songlin Yang, Kalyan Sunkavalli, William T Freeman,","arxiv_match":false}],"id":"2504.12626","metadata_source":"Cached arXiv API metadata","rank":906},{"title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","arxiv_id":"2104.13478","year":2021,"url":"https://arxiv.org/abs/2104.13478","citing_submissions":112,"explicit_arxiv_matches":106,"examples":[{"id":"hVl6H6DnYO","title":"Consistency as an Inductive Bias: Mitigating Cross-View Reliability Gap in MLLMs","forum_url":"https://openreview.net/forum?id=hVl6H6DnYO","excerpt":"ao Deng, Wei Ding, Chang Gao, Chunjiang Ge, et al. Qwen3-vl technical report. arXiv preprint arXiv:2511.21631, 2025a. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923, 2025b. Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478, 2021. Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P. Xu, and Liang Lin","arxiv_match":true},{"id":"BiZ19ar6es","title":"𝕊𝕃(n) Representation Learning: An Intrinsic Mixed-Curvature Space with Higher Curvature Capacities and Deeper Order-Aware Composition","forum_url":"https://openreview.net/forum?id=BiZ19ar6es","excerpt":"https: //doi.org/10.1007/978-1-4612-1268-3_2. Thomas Bendokat, Ralf Zimmermann, and P.-A. Absil. A grassmann manifold handbook: basic geometry and computational aspects. Adv. Comput. Math., 50(1), January 2024. ISSN 1019-7168. doi: 10.1007/s10444-023-10090-8. URL https://doi.org/10.1007/ s10444-023-10090-8. Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478, 2021. Ines Chami, Zhitao Ying, Christopher Ré, and Jure Leskovec. Hyperbolic graph convolutional ","arxiv_match":true},{"id":"pLJEIU2Iu3","title":"Geometry-Induced Operator Families for Deep Representation Learning","forum_url":"https://openreview.net/forum?id=pLJEIU2Iu3","excerpt":"ed Brody, Uri Alon, and Eran Yahav. How attentive are graph attention networks? arXiv preprint arXiv:2105.14491, 2021. Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18–42, 2017. Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478, 2021. Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, and Eman","arxiv_match":true}],"id":"2104.13478","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":907},{"title":"Make-a-video: Text-to-video generation without text-video data","arxiv_id":"2209.14792","year":2022,"url":"https://arxiv.org/abs/2209.14792","citing_submissions":112,"explicit_arxiv_matches":65,"examples":[{"id":"NOBzyFqdOi","title":"From Preference to Flow: Turning Pairwise Preferences into Local Flow Supervision","forum_url":"https://openreview.net/forum?id=NOBzyFqdOi","excerpt":"ained video preference. In Neural Information Processing Systems, 2025. Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. ArXiv preprint, 2022. Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment u","arxiv_match":false},{"id":"vMypxbZHbc","title":"Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation","forum_url":"https://openreview.net/forum?id=vMypxbZHbc","excerpt":"r-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3c: 3d-informed world-consistent video generation with precise camera control. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6121–6132, 2025. Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data, 2022. URL https://arxiv.org/abs/2209.14792. Chenxi Song, Yanming Yang, Tong Zhao, Ruibo Li, and Chi Zhang. Worldforge: Unlocking emergent 3d/4","arxiv_match":true},{"id":"Wui5vF8IQf","title":"E$^2$PO: Concept Erasing in Text-to-Video Models via Energy-based Erasing Preference Optimization","forum_url":"https://openreview.net/forum?id=Wui5vF8IQf","excerpt":" text-to-image diffusion models. arXiv preprint arXiv:2511.04834, 2025. Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. In International Conference on Learning Representations, 2023. Yang Song and Diederik P Kingma. How to train your energy-based models. arXiv preprint arXiv:2101.03288, 2021. Koushik Srivatsan, Fahad","arxiv_match":false}],"id":"2209.14792","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":908},{"title":"Swift:a scalable lightweight infrastructure for fine-tuning","arxiv_id":"2408.05517","year":2024,"url":"https://arxiv.org/abs/2408.05517","citing_submissions":112,"explicit_arxiv_matches":56,"examples":[{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":"c vision with dynamic tooling. arXiv preprint arXiv:2507.07998, 2025a. Yuze Zhao, Jintao Huang, Jinghan Hu, Xingjun Wang, Yunlin Mao, Daoze Zhang, Zeyinzi Jiang, Zhikai Wu, Baole Ai, Ang Wang, et al. Swift: a scalable lightweight infrastructure for fine-tuning. In AAAI, volume 39, pp. 29733–29735, 2025b. Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, et al. Group sequence policy optimizat","arxiv_match":false},{"id":"gX30Aapm6u","title":"VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning","forum_url":"https://openreview.net/forum?id=gX30Aapm6u","excerpt":"eprint arXiv:2501.12380, 2025a. URL https://arxiv.org/abs/ 2501.12380. Yuze Zhao, Jintao Huang, Jinghan Hu, Xingjun Wang, Yunlin Mao, Daoze Zhang, Zeyinzi Jiang, Zhikai Wu, Baole Ai, Ang Wang, et al. Swift: a scalable lightweight infrastructure for fine-tuning. In AAAI, volume 39, pp. 29733–29735, 2025b. Chunlin Zhong, Qiuxia Hou, Zhangjun Zhou, Shuang Hao, Haonan Lu, Yanhao Zhang, He Tang, and Xiang Bai. Owlcap: Harmonizing motion-detail for video caption","arxiv_match":false},{"id":"xVqGCq3X2z","title":"SRAM: Learning Spectral Relation-Aware Representations for Environmental Sound Deepfake Detection","forum_url":"https://openreview.net/forum?id=xVqGCq3X2z","excerpt":"ctions. arXiv preprint arXiv:2412.09596, 2024. Yuze Zhao, Jintao Huang, Jinghan Hu, Xingjun Wang, Yunlin Mao, Daoze Zhang, Zeyinzi Jiang, Zhikai Wu, Baole Ai, Ang Wang, Wenmeng Zhou, and Yingda Chen. SWIFT: A scalable lightweight infrastructure for fine-tuning. In AAAI, 2025. A ","arxiv_match":false}],"id":"2408.05517","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":909},{"title":"AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning","arxiv_id":"2505.24298","year":2025,"url":"https://arxiv.org/abs/2505.24298","citing_submissions":111,"explicit_arxiv_matches":53,"examples":[{"id":"4OInujSgtd","title":"Skill$^2$: Fully Agentic Co-Evolution of Policy and Skill Library","forum_url":"https://openreview.net/forum?id=4OInujSgtd","excerpt":", Han Zhou, and Zaiqiao Meng. A comprehensive survey of self-evolving ai agents: A new paradigm bridging foundation models and lifelong agentic systems, 2025. URL https://arxiv.org/abs/2508.07407. Wei Fu, Jiaxuan Gao, Xujie Shen, Chen Zhu, Zhiyu Mei, Chuyi He, Shusheng Xu, Guo Wei, Jun Mei, Jiashu Wang, Tongkai Yang, Binhang Yuan, and Yi Wu. Areal: A large-scale asynchronous reinforcement learning system for language reasoning, 2026. URL https://arxiv.org/ abs/2505.24298. Jinyi Han, Yuanjian Xu, Ying Liao, Xinyi Wang, Zishang Jiang, Zixiang Di, Fanyang Lu, Zhichao Hu, ","arxiv_match":true},{"id":"SMiWjRdzKn","title":"Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors","forum_url":"https://openreview.net/forum?id=SMiWjRdzKn","excerpt":"matical dataset for advancing reasoning, 2025. URL https://arxiv.org/abs/2504.11456. Wei Fu, Jiaxuan Gao, Xujie Shen, Chen Zhu, Zhiyu Mei, Chuyi He, Shusheng Xu, Guo Wei, Jun Mei, Jiashu Wang, et al. Areal: A large-scale asynchronous reinforcement learning system for language reasoning. Advances in Neural Information Processing Systems, 38:36256–36282, 2026. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al.","arxiv_match":false},{"id":"8v0pPgA8ph","title":"Training Foundation Models with a Single Algorithm","forum_url":"https://openreview.net/forum?id=8v0pPgA8ph","excerpt":"Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, and Marcin Michalski. Seed rl: Scalable and efficient deep-rl with accelerated central inference. arXiv preprint arXiv:1910.06591, 2019. Wei Fu, Jiaxuan Gao, Xujie Shen, Chen Zhu, Zhiyu Mei, Chuyi He, Shusheng Xu, Guo Wei, Jun Mei, Jiashu Wang, Tongkai Yang, Binhang Yuan, and Yi Wu. Areal: A large-scale asynchronous reinforcement learning system for language reasoning, 2026a. URL https://arxiv.org/abs/ 2505.24298. Yuqian Fu, Tinghong Chen, Jiajun Chai, Xihuai Wang, Songjun Tu, Guojun Yin, Wei Lin, Qichao Zhang,","arxiv_match":true}],"id":"2505.24298","metadata_source":"Cached arXiv API metadata","rank":910},{"title":"AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?","arxiv_id":"2509.03312","year":2025,"url":"https://arxiv.org/abs/2509.03312","citing_submissions":111,"explicit_arxiv_matches":44,"examples":[{"id":"FMJWTvDXOP","title":"Credit Where It Matters: Dependency-Aware Policy Optimization for Terminal Agents","forum_url":"https://openreview.net/forum?id=FMJWTvDXOP","excerpt":"c: Credit assignment in reinforcement learning for large language models. arXiv preprint arXiv:2604.09459, 2026. Guibin Zhang, Junhao Wang, Junjie Chen, Wangchunshu Zhou, Kun Wang, and Shuicheng Yan. Agentracer: Who is inducing failure in the llm agentic systems? In Proceedings of International Conference on Learning Representations, volume 2026, pp. 11377–11399, 2026. Siyuan Zhu, Chao Yu, Rongxin Yang, Zongkai Liu, Jinjun Hu, Qiwen Chen, and Yibo Zhang. Gag","arxiv_match":false},{"id":"7FvOdS8ZMe","title":"EventLedger: Typed Counterfactual Credit for Multi-Agent LLM Orchestration","forum_url":"https://openreview.net/forum?id=7FvOdS8ZMe","excerpt":". Chenchen Zhang. From reasoning to agentic: Credit assignment in reinforcement learning for large language models. arXiv preprint arXiv:2604.09459, 2026a. Chenchen Zhang. Reinforcement learning for LLM-based multi-agent systems through orchestration traces. arXiv preprint arXiv:2605.02801, 2026b. Guibin Zhang, Junhao Wang, Junjie Chen, Wangchunshu Zhou, Kun Wang, and Shuicheng Yan. AgenTracer: Who is inducing failure in the LLM agentic systems? arXiv preprint arXiv:2509.03312, 2025. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen ","arxiv_match":true},{"id":"zCNHISpxH5","title":"CATO: Contrastive Attribution for Targeted Optimization of Multi-Component LLM Agents","forum_url":"https://openreview.net/forum?id=zCNHISpxH5","excerpt":"mproving agents. In International Conference on Learning Representations, volume 2026, pp. 104223–104294, 2026b. Guibin Zhang, Junhao Wang, Junjie Chen, Wangchunshu Zhou, Kun Wang, and Shuicheng Yan. AgenTracer: Who is inducing failure in the LLM agentic systems? In International Conference on Learning Representations, volume 2026, pp. 11377–11399, 2026a. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Chen","arxiv_match":false}],"id":"2509.03312","metadata_source":"Cached arXiv API metadata","rank":911},{"title":"DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge","arxiv_id":"2507.04447","year":2025,"url":"https://arxiv.org/abs/2507.04447","citing_submissions":111,"explicit_arxiv_matches":30,"examples":[{"id":"xE1Rd5Luma","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","forum_url":"https://openreview.net/forum?id=xE1Rd5Luma","excerpt":"tive real-world simulators. arXiv preprint arXiv:2310.06114, 2023. Wenyao Zhang, Hongsi Liu, Zekun Qi, Yunnan Wang, Xinqiang Yu, Jiazhao Zhang, Runpei Dong, Jiawei He, He Wang, Zhizheng Zhang, et al. Dreamvla: a vision-language-action model dreamed with comprehensive world knowledge. Advances in Neural Information Processing Systems, 38: 24195–24228, 2026a. Yue Zhang, Rui Wang, Jiehong Lin, Zhongrui Wang, and Xiaojuan Qi. Retrieval-vla: Trainingfree in-context adaptation for vis","arxiv_match":false},{"id":"wEc8DpSJ41","title":"GF-VLA: Dual Geometry Forcing for Vision-Language-Action Models","forum_url":"https://openreview.net/forum?id=wEc8DpSJ41","excerpt":"u, Zhengcheng Yu, He Zhang, Jiyao Zhang, Lin Zhao, Chen Zhou, Nan Duan, Yuzheng Zhuang, and Liang Lin. JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy. arXiv preprint arXiv:2604.20100, 2026c. Wenyao Zhang, Hongsi Liu, Zekun Qi, Yunnan Wang, Xinqiang Yu, Jiazhao Zhang, Runpei Dong, Jiawei He, Fan Lu, He Wang, Zhizheng Zhang, Li Yi, Wenjun Zeng, and Xin Jin. DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge. arXiv preprint arXiv:2507.04447, 2025. Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qian","arxiv_match":true},{"id":"REE0TB9Fde","title":"PINT: A Unified Model with Shared Predictive Intent for Robot Manipulation","forum_url":"https://openreview.net/forum?id=REE0TB9Fde","excerpt":"ke Wang, Jiahao Shao, Gangwei Xu, et al. Native video-action pretraining for generalizable robot control. arXiv preprint arXiv:2607.08639, 2026b. Wenyao Zhang, Hongsi Liu, Zekun Qi, Yunnan Wang, Xinqiang Yu, Jiazhao Zhang, Runpei Dong, Jiawei He, Fan Lu, He Wang, Zhizheng Zhang, Li Yi, Wenjun Zeng, and Xin Jin. DreamVLA: A vision-language-action model dreamed with comprehensive world knowledge. arXiv preprint arXiv:2507.04447, 2025b. URL https://arxiv.org/abs/2507.04447. Zhanguang Zhang, Zhiyuan Li, Behnam Rahmati, Rui Heng Yang, Yintao Ma, Amir Rasouli, Sajjad Pakdam","arxiv_match":true}],"id":"2507.04447","metadata_source":"Cached arXiv API metadata","rank":912},{"title":"Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs","arxiv_id":"2502.17424","year":2025,"url":"https://arxiv.org/abs/2502.17424","citing_submissions":111,"explicit_arxiv_matches":46,"examples":[{"id":"vsiVsJWvnz","title":"Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning","forum_url":"https://openreview.net/forum?id=vsiVsJWvnz","excerpt":"3, 2024. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022. Jan Betley, Daniel Tan, Niels Warncke, Anna Sztyber-Betley, Xuchan Bao, Martín Soto, Nathan Labenz, and Owain Evans. Emergent misalignment: Narrow finetuning can produce broadly misaligned llms. arXiv preprint arXiv:2502.17424, 2025. Lang Cao. Learn to refuse: Making large language models more controllable and reliable throu","arxiv_match":true},{"id":"quYCH8w7k5","title":"LLM Persona Unlearning","forum_url":"https://openreview.net/forum?id=quYCH8w7k5","excerpt":" Biderman. LEACE: Perfect linear concept erasure in closed form. In NeurIPS, 2023. Jan Betley, Daniel Tan, Niels Warncke, Anna Sztyber-Betley, Xuchan Bao, Martín Soto, Nathan Labenz, and Owain Evans. Emergent misalignment: Narrow finetuning can produce broadly misaligned LLMs. In ICML, 2025. Bing Team. The new Bing & Edge—learning from our first week. Microsoft Bing Blogs, February 2023. URL https://blogs.bing.com/search/february-2023/The-new-Bing-Edge-L earning-from-our-","arxiv_match":false},{"id":"2fAXwfIawS","title":"Transferable LLM Misalignment Attack via Safety Task Vector Extraction","forum_url":"https://openreview.net/forum?id=2fAXwfIawS","excerpt":"vances in Neural Information Processing Systems, 37:136037–136083, 2024. Jan Betley, Daniel Chee Hian Tan, Niels Warncke, Anna Sztyber-Betley, Xuchan Bao, Martin Soto, Nathan Labenz, and Owain Evans. Emergent misalignment: Narrow finetuning can produce broadly misaligned llms. In International Conference on Machine Learning, pp. 4043–4068. PMLR, 2025. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you h","arxiv_match":false}],"id":"2502.17424","metadata_source":"Cached arXiv API metadata","rank":913},{"title":"Improved Mean Flows: On the Challenges of Fastforward Generative Models","arxiv_id":"2512.02012","year":2025,"url":"https://arxiv.org/abs/2512.02012","citing_submissions":111,"explicit_arxiv_matches":30,"examples":[{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":"h Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=uWj4s7rMnR. Zhengyang Geng, Yiyang Lu, Zongze Wu, Eli Shechtman, J Zico Kolter, and Kaiming He. Improved mean flows: On the challenges of fastforward generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 30467–30476, 2026. Hyojun Go, Byeongjun Park, Jiho Jang, Jin-Young Kim, Soonwoo Kwon, and Changick Kim. Spla","arxiv_match":false},{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"J Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling. arXiv preprint arXiv:2505.13447, 2025a. Zhengyang Geng, Yiyang Lu, Zongze Wu, Eli Shechtman, J Zico Kolter, and Kaiming He. Improved mean flows: On the challenges of fastforward generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 30467–30476, 2026. Zigang Geng, Yibing Wang, Yeyao Ma, Chen Li, Yongming Rao, Shuyang Gu, Zhao Zhong, Qingli","arxiv_match":false},{"id":"wcae0JKDe0","title":"MoGeFi: Learning Geometric Feedback for Few-Step Human Motion Generation","forum_url":"https://openreview.net/forum?id=wcae0JKDe0","excerpt":"rative modeling. In Advances in Neural Information Processing Systems, volume 38, 2025. doi: 10.52202/085713-2534. Zhengyang Geng, Yiyang Lu, Zongze Wu, Eli Shechtman, J. Zico Kolter, and Kaiming He. Improved mean flows: On the challenges of fastforward generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 30467–30476, 2026. Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng. Generating ","arxiv_match":false}],"id":"2512.02012","metadata_source":"Cached arXiv API metadata","rank":914},{"title":"Jumping ahead: Improving reconstruction fidelity with JumpReLU sparse autoencoders","arxiv_id":"2407.14435","year":2024,"url":"https://arxiv.org/abs/2407.14435","citing_submissions":111,"explicit_arxiv_matches":98,"examples":[{"id":"WZBavPTogb","title":"From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness","forum_url":"https://openreview.net/forum?id=WZBavPTogb","excerpt":"Zhu (eds.), Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 48393–48421. PMLR, 13–19 Jul 2025. URL https://proceedings.mlr.press/v267/paulo25a.html. Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, János Kramár, and Neel Nanda. Jumping ahead: Improving reconstruction fidelity with jumprelu sparse autoencoders, 2024. URL https://arxiv.org/abs/2407.14435. Xu Shen, Zhen Tan, Song Wang, Pingjun Hong, Rui Miao, Xin Wang, and Tianlong Chen. Detecting unfai","arxiv_match":true},{"id":"GXJlfZBXPl","title":"D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders","forum_url":"https://openreview.net/forum?id=GXJlfZBXPl","excerpt":"same data learn different features. arXiv preprint arXiv:2501.16615, 2025. William Peebles and Saining Xie. Scalable diffusion models with transformers. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4172–4182. IEEE, 2023. Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, János Kramár, and Neel Nanda. Jumping ahead: Improving reconstruction fidelity with jumprelu sparse autoencoders. arXiv preprint arXiv:2407.14435, 2024. Sukrut Rao, Sweta Mahajan, Moritz Böhle, and Bernt Schiele. Discover-then-name: Task-agnosti","arxiv_match":true},{"id":"eqmbSYhaLy","title":"Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility","forum_url":"https://openreview.net/forum?id=eqmbSYhaLy","excerpt":"ll, Colin Raffel, Leandro Von Werra, and Thomas Wolf. The FineWeb datasets: Decanting the web for the finest text data at scale. In Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2024. arXiv:2406.17557. Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, János Kramár, and Neel Nanda. Jumping ahead: Improving reconstruction fidelity with JumpReLU sparse autoencoders. arXiv preprint arXiv:2407.14435, 2024. Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-se","arxiv_match":true}],"id":"2407.14435","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":915},{"title":"Memory in the Age of AI Agents","arxiv_id":"2512.13564","year":2025,"url":"https://arxiv.org/abs/2512.13564","citing_submissions":111,"explicit_arxiv_matches":100,"examples":[{"id":"cZtUReT2nl","title":"One Assistant, Many Memories: Benchmarking Real-World Group Assistance When Recall Is Not Enough","forum_url":"https://openreview.net/forum?id=cZtUReT2nl","excerpt":"ao, Qiankun Li, Kun Wang, Wangchunshu Zhou, Yixin Liu, Dawei Cheng, Qi Zhang, Tao Gui, Shirui Pan, Yan Zhang, Philip Torr, Zhicheng Dou, Ji-Rong Wen, Xuanjing Huang, Yu-Gang Jiang, and Shuicheng Yan. Memory in the age of AI agents. CoRR, abs/2512.13564, 2025. doi: 10.48550/ARXIV.2512.13564. URL https://doi.org/10.48550/arXiv. 2512.13564. Zhanghao Hu, Qinglin Zhu, Runcong Zhao, Di Liang, Hanqi Yan, Yulan He, and Lin Gui. Beyond","arxiv_match":false},{"id":"L7iP938SBC","title":"Test-Time Memory Construction","forum_url":"https://openreview.net/forum?id=L7iP938SBC","excerpt":"Sun, Boci Peng, Zhenrong Cheng, Xuanbo Fan, Jiaxin Guo, Xinlei Yu, Zhenhong Zhou, Zewen Hu, Jiahao Huo, Junhao Wang, Yuwei Niu, Yu Wang, Zhenfei Yin, Xiaobin Hu, Yue Liao, Qiankun Li, Kun Wang, Wangchunshu Zhou, Yixin Liu, Dawei Cheng, Qi Zhang, Tao Gui, Shirui Pan, Yan Zhang, Philip Torr, Zhicheng Dou, Ji-Rong Wen, Xuanjing Huang, Yu-Gang Jiang, and Shuicheng Yan. Memory in the age of ai agents. ArXiv preprint, abs/2512.13564, 2025. URL https://arxiv.org/abs/2512.13564. Zhanghao Hu, Qinglin Zhu, Runcong Zhao, Di Liang, Hanqi Yan, Yulan He, and Lin Gui. Beyond rag for","arxiv_match":true},{"id":"QZN7WgUfvg","title":"Mem-C: Scaling and Allocating Credit Beyond Dense Rewards in Long-Horizon Agents","forum_url":"https://openreview.net/forum?id=QZN7WgUfvg","excerpt":"rizon tasks: A survey. ACM Computing Surveys, 58(14):1–37, 2026. Gunshi Gupta, Karmesh Yadav, Zsolt Kira, Yarin Gal, and Rahaf Aljundi. Memo: Training memoryefficient embodied agents with reinforcement learning. Advances in Neural Information Processing Systems, 38:104392–104414, 2026. Yuyang Hu, Shichun Liu, Yanwei Yue, Guibin Zhang, Boyang Liu, Fangyi Zhu, Jiahang Lin, Honglin Guo, Shihan Dou, Zhiheng Xi, et al. Memory in the age of ai agents. arXiv preprint arXiv:2512.13564, 2025. Tenghao Huang, Kinjal Basu, Ibrahim Abdelaziz, Pavan Kapanipathi, Jonathan May, and Muhao Ch","arxiv_match":true}],"id":"2512.13564","metadata_source":"Cached arXiv API metadata","rank":916},{"title":"Self-play fine-tuning converts weak language models to strong language models","arxiv_id":"2401.01335","year":2024,"url":"https://arxiv.org/abs/2401.01335","citing_submissions":111,"explicit_arxiv_matches":51,"examples":[{"id":"QJU7wVAnLZ","title":"Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training","forum_url":"https://openreview.net/forum?id=QJU7wVAnLZ","excerpt":"dings of the Association for Computational Linguistics: ACL 2026, pp. 32970–32984. Association for Computational Linguistics, 2026. Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu. Self-play fine-tuning converts weak language models to strong language models. In Proceedings of the 41st International Conference on Machine Learning, pp. 6621–6642. PMLR, 2024. Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep rei","arxiv_match":false},{"id":"07pvXM8POP","title":"Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills","forum_url":"https://openreview.net/forum?id=07pvXM8POP","excerpt":"i-agent evolve: Llm self-improve through co-evolution. arXiv preprint arXiv:2510.23595, 2025. Zhiyu Chen, Zihan Guo, Bo Huang, Bingwei Lu, Jianghao Lin, Yuanjian Zhou, and Weinan Zhang. Skilljuror: Measuring how agent skill organization changes runtime behavior. arXiv preprint arXiv:2606.11543, 2026b. Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu. Self-play fine-tuning converts weak language models to strong language models. arXiv preprint arXiv:2401.01335, 2024. Jiale Cheng, Xiao Liu, Cunxiang Wang, Xiaotao Gu, Yida Lu, Dan Zhang, Yuxiao Dong, Jie Tang,","arxiv_match":true},{"id":"ePhUAgGReu","title":"HalluMirror: Discovering and Mitigating Multimodal Hallucinations via Co-Evolutionary Self-Play","forum_url":"https://openreview.net/forum?id=ePhUAgGReu","excerpt":"s. In Findings of ACL, pp. 9197–9222, 2025. Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, and Huajun Chen. Unified hallucination detection for multimodal large language models. In Annu. Meeting Assoc. Comput. Linguistics, pp. 3235–3252, 2024a. Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu. Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. arXiv preprint arXiv:2401.01335, 2024b. Pengyu Cheng, Yong Dai, Tianhao Hu, Han Xu, Zhisong Zhang, Lei Han, Nan Du, and Xiaolong Li","arxiv_match":true}],"id":"2401.01335","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":917},{"title":"Adding error bars to evals: A statistical approach to language model evaluations","arxiv_id":"2411.00640","year":2024,"url":"https://arxiv.org/abs/2411.00640","citing_submissions":110,"explicit_arxiv_matches":103,"examples":[{"id":"ts0kgHyBfr","title":"Phantom Gains: Auditing Self-Improvement Against a Measured Null","forum_url":"https://openreview.net/forum?id=ts0kgHyBfr","excerpt":"benchmark for rl to expand beyond the base model. In C. Vondrick, B. Hariharan, C. Raffel, L. Pinto, D. Yang, and A. Faust (eds.), International Conference on Learning Representations, volume 2026, pp. 68471–68489, 2026. URL https://proceedings.iclr.cc/paper_files/paper/2026/file/ 6fdf57c71bc1f1ee29014b8dc52e723f-Paper-Conference.pdf. Evan Miller. Adding error bars to evals: A statistical approach to language model evaluations, 2024. URL https://arxiv.org/abs/2411.00640. Mohammad Mahdi Moradi, Hossam Amer, Sudhir Mudur, Weiwei Zhang, Yang Liu, and Walid Ahmed. Continu","arxiv_match":true},{"id":"NH98Pcbbwd","title":"Are Agent Reliability Rankings Real? A Statistical Treatment of the $\\mathrm{pass}^k$ Metric","forum_url":"https://openreview.net/forum?id=NH98Pcbbwd","excerpt":"print arXiv:2407.01502, 2024. Jiajun Jiang, Sharon Zheng, Natan Vidra, and Spurthi Setty. Beyond pass@k: Measuring reliability and security of agentic code generation. arXiv preprint arXiv:2608.14711, 2026. Andreas Maurer and Massimiliano Pontil. Empirical bernstein bounds and sample-variance penalization. In Conference on Learning Theory (COLT), 2009. Evan Miller. Adding error bars to evals: A statistical approach to language model evaluations. arXiv preprint arXiv:2411.00640, 2024. Bitya Neuhof and Yuval Benjamini. Quantifying ranking uncertainty in LLM benchmarks. arXiv p","arxiv_match":true},{"id":"LhhLpXBFgo","title":"Opinionated Optimizers: Why Agents Struggle to Improve Agents","forum_url":"https://openreview.net/forum?id=LhhLpXBFgo","excerpt":"mi Koyejo, Pontus Stenetorp, Sharan Narang, and Dieuwke Hupkes. Quantifying variance in evaluation benchmarks. arXiv preprint arXiv:2406.10229, 2024. Oded Maron and Andrew W. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In Advances in Neural Information Processing Systems 6, pp. 59–66, 1993. Evan Miller. Adding error bars to evals: A statistical approach to language model evaluations. arXiv preprint arXiv:2411.00640, 2024. Jinming Nian, Zhiyuan Peng, Hongwei Shang, Dae Hoon Park, and Yi Fang. Submodular evaluation","arxiv_match":true}],"id":"2411.00640","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":918},{"title":"BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset","arxiv_id":"2505.09568","year":2025,"url":"https://arxiv.org/abs/2505.09568","citing_submissions":110,"explicit_arxiv_matches":106,"examples":[{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"englin Cheng, Long Cui, Ruishan Fang, Zhangxuan Gu, Zhicheng Huang, Zhenzhong Lan, Yuanting Lei, et al. Llada-image: Building strong image generators with fully open training recipes. arXiv preprint arXiv:2609.03796, 2026. Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, Saining Xie, Silvio Savarese, et al. Blip3-o: A family of fully open unified multimodal models-architecture, training and dataset. arXiv preprint arXiv:2505.09568, 2025a. Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping L","arxiv_match":true},{"id":"KHODv83hPB","title":"IntReal: A Real-World Dataset and Benchmark for Multimodal Interleaved Generation","forum_url":"https://openreview.net/forum?id=KHODv83hPB","excerpt":"n, Benlin Liu, Yue Huang, Yao Wan, Pan Zhou, and Ranjay Krishna. Interleaved scene graphs for interleaved text-and-image generation assessment. In The Thirteenth International Conference on Learning Representations, 2025a. Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, Saining Xie, Silvio Savarese, et al. Blip3-o: A family of fully open unified multimodal models-architecture, training and dataset. arXiv preprint arXiv:2505.09568, 2025b. Wei Chen, Lin Li, Yongqi Yang, Bin Wen, Fan Yang, Tingting Gao, Yu Wu, and Long Chen. Comm:","arxiv_match":true},{"id":"Khg0qee5LV","title":"OmniPersona: Persona-Embodied Agents for Multimodal Conversation Interaction","forum_url":"https://openreview.net/forum?id=Khg0qee5LV","excerpt":"doi: 10.1109/CVPR52734.2025.01035. URL https://openaccess.thecvf.com/content/CVPR2025/html/Chen_ MAR-3D_Progressive_Masked_Auto-regressor_for_High-Resolution_3D_ Generation_CVPR_2025_paper.html. Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, Saining Xie, Silvio Savarese, Le Xue, Caiming Xiong, and Ran Xu. BLIP3-o: A family of fully open unified multimodal models—architecture, training and dataset. arXiv preprint arXiv:2505.09568, 2025b. Ming Chen, Liyuan Cui, Wenyuan Zhang, Haoxian Zhang, Yan Zhou, Xiaohan Li, Songlin Tang, Ji","arxiv_match":true}],"id":"2505.09568","metadata_source":"Cached arXiv API metadata","rank":919},{"title":"Diffusion policy policy optimization","arxiv_id":"2409.00588","year":2024,"url":"https://arxiv.org/abs/2409.00588","citing_submissions":110,"explicit_arxiv_matches":33,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"port couplings. arXiv preprint arXiv:2505.01179, 2025. Allen Ren, Justin Lidard, Lars Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, and Max Simchowitz. Diffusion policy policy optimization. In International Conference on Learning Representations, volume 2025, pp. 77288–77329, 2025. Yue Su, Sijin Chen, Haixin Shi, Mingyu Liu, Zhengshen Zhang, Ningyuan Huang, Weiheng Zhong, Zhengbang Zhu","arxiv_match":false},{"id":"U6seufWPDg","title":"AMINO: Generative Auto-bidding with Multimodal Trajectory Modeling and Initial Noise Optimization","forum_url":"https://openreview.net/forum?id=U6seufWPDg","excerpt":"ng. Knowledge-Based Systems, 284:111341, 2024. Allen Z. Ren, Justin Lidard, Lars Lien Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, and Max Simchowitz. Diffusion policy policy optimization. In The Thirteenth International Conference on Learning Representations, 2025. Kan Ren, Weinan Zhang, Ke Chang, Yifei Rong, Yong Yu, and Jun Wang. Bidding machine: Learning to bid for directly optimi","arxiv_match":false},{"id":"b4JARCTPGU","title":"Improving Truncated First-Order Gradients for Flow Policies with Surrogate Objective","forum_url":"https://openreview.net/forum?id=b4JARCTPGU","excerpt":"ence on Machine Learning, pp. 41163–41182. PMLR, 2024. Allen Ren, Justin Lidard, Lars Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, and Max Simchowitz. Diffusion Policy Policy Optimization. In International Conference on Learning Representations, volume 2025, pp. 77288–77329, 2025. Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter. Learning to walk in minutes using massively","arxiv_match":false}],"id":"2409.00588","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":920},{"title":"FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving","arxiv_id":"2501.01005","year":2025,"url":"https://arxiv.org/abs/2501.01005","citing_submissions":110,"explicit_arxiv_matches":34,"examples":[{"id":"moFf9vJeAN","title":"WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning","forum_url":"https://openreview.net/forum?id=moFf9vJeAN","excerpt":". http://arxiv.org/abs/2505.09388, May 2025. Zihao Ye, Lequn Chen, Ruihang Lai, Wuwei Lin, Yineng Zhang, Stephanie Wang, Tianqi Chen, Baris Kasikci, Vinod Grover, Arvind Krishnamurthy, and Luis Ceze. FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving. In Eighth Conference on Machine Learning and Systems, May 2025. Ted Zadouri, Markus Hoehnerbach, Jay Shah, Timmy Liu, Vijay Thakkar, and Tri Dao. FlashAttention-4: Algorithm and Kernel Pipelining Co","arxiv_match":false},{"id":"voFUHVlESb","title":"Efficient LLM Inference Rescheduling via Progressive KV Cache Migration","forum_url":"https://openreview.net/forum?id=voFUHVlESb","excerpt":" 2024. URL https://arxiv.org/abs/2411.09317. Zihao Ye, Lequn Chen, Ruihang Lai, Wuwei Lin, Yineng Zhang, Stephanie Wang, Tianqi Chen, Baris Kasikci, Vinod Grover, Arvind Krishnamurthy, and Luis Ceze. Flashinfer: Efficient and customizable attention engine for llm inference serving. In M. Zaharia, G. Joshi, and Y. Lin (eds.), Proceedings of Machine Learning and Systems, volume 7. MLSys, 2025. URL https://proceedings.mlsys.org/paper_files/paper/2025/ file/dbf02b21d77409a2db30e56","arxiv_match":false},{"id":"NwY6n95NIv","title":"CoWindow Attention: Full Causal Coverage Is a Collective Property","forum_url":"https://openreview.net/forum?id=NwY6n95NIv","excerpt":"jjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, et al. Effective long-context scaling of foundation models. arXiv preprint arXiv:2309.16039, 2023. Zihao Ye, Lequn Chen, Ruihang Lai, Wuwei Lin, Yineng Zhang, Stephanie Wang, Tianqi Chen, Baris Kasikci, Vinod Grover, Arvind Krishnamurthy, and Luis Ceze. Flashinfer: Efficient and customizable attention engine for llm inference serving, 2025. URL https://arxiv.org/ abs/2501.01005. Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, YX Wei, L","arxiv_match":true}],"id":"2501.01005","metadata_source":"Cached arXiv API metadata","rank":921},{"title":"MAGI-1: Autoregressive Video Generation at Scale","arxiv_id":"2505.13211","year":2025,"url":"https://arxiv.org/abs/2505.13211","citing_submissions":110,"explicit_arxiv_matches":101,"examples":[{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"e Yu, Fei Huang, Suozhi Huang, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free. arXiv preprint arXiv:2505.06708, 2025. Qwen Team. Qwen3-Next: Towards Ultimate Training & Inference Efficiency. Qwen Team blog, https://qwen.ai/blog?id=qwen3-next, 2025. Sand.ai, Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, et al. MAGI-1: Autoregressive Video Generation at Scale. arXiv preprint arXiv:2505.13211, 2025. Team Seedance et al. Seedance 2.0: Advancing Video Generation for World Complexity. arXiv pr","arxiv_match":true},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"ons, volume 2024, pp. 5260–5274, 2024. Team Seedance, De Chen, Liyang Chen, Xin Chen, Ying Chen, Zhuo Chen, Zhuowei Chen, Feng Cheng, Tianheng Cheng, Yufeng Cheng, et al. Seedance 2.0: Advancing video generation for world complexity. arXiv preprint arXiv:2604.14148, 2026. Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zhang, WQ Zhang, Weifeng Luo, et al. Magi-1: Autoregressive video generation at scale. arXiv preprint arXiv:2505.13211, 2025. Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming","arxiv_match":true},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":"i Han, Tianning Zhang, W. Q. Zhang, Weifeng Luo, Xiaoyang Kang, Yuchen Sun, Yue Cao, Yunpeng Huang, Yutong Lin, Yuxin Fang, Zewei Tao, Zheng Zhang, Zhongshu Wang, Zixun Liu, Dai Shi, Guoli Su, Hanwen Sun, Hong Pan, Jie Wang, Jiexin Sheng, Min Cui, Min Hu, Ming Yan, Shucheng Yin, Siran Zhang, Tingting Liu, Xianping Yin, Xiaoyu Yang, Xin Song, Xuan Hu, Yankai Zhang, and Yuqiao Li. Magi-1: Autoregressive video generation at scale, 2025. URL https://arxiv.org/abs/2505.13211. Zefan Cai, Wen Xiao, Hanshi Sun, Cheng Luo, Yikai Zhang, Ke Wan, Yucheng Li, Yeyang Zhou, Li-Wen C","arxiv_match":true}],"id":"2505.13211","metadata_source":"Cached arXiv API metadata","rank":922},{"title":"Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory","arxiv_id":"2604.08995","year":2026,"url":"https://arxiv.org/abs/2604.08995","citing_submissions":110,"explicit_arxiv_matches":99,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"dels. arXiv:2503.20314, 2025. arXiv preprint Wenhao Wang and Yi Yang. Vidprom: A million-scale real prompt-gallery dataset for text-to-video diffusion models. Advances in Neural Information Processing Systems, 37:65618–65642, 2024. Zile Wang, Zexiang Liu, Jiaxing Li, Kaichen Huang, Baixin Xu, Fei Kang, Mengyin An, Peiyu Wang, Biao Jiang, Yichen Wei, et al. Matrix-game 3.0: Real-time and streaming interactive world model with long-horizon memory. arXiv preprint arXiv:2604.08995, 2026. Zhuguanyu Wu, Ruihao Gong, Yang Yong, Yushi Huang, Xiangyu Fan, Lei Yang, Dahua Lin, and Xia","arxiv_match":true},{"id":"3nSV2brMqb","title":"PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives","forum_url":"https://openreview.net/forum?id=3nSV2brMqb","excerpt":"n, Peiyu Wang, Biao Jiang, Yichen Wei, Yidan Xietian, Jiangbo Pei, Liang Hu, Boyi Jiang, Hua Xue, Zidong Wang, Haofeng Sun, Wei Li, Wanli Ouyang, Xianglong He, Yang Liu, Yangguang Li, and Yahui Zhou. Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory, April 2026. Meiqi Wu, Zhixin Cai, Fufangchen Zhao, Xiaokun Feng, Rujing Dang, Bingze Song, Ruitian Tian, Jiashu Zhu, Jiachen Lei, Hao Dou, Jing Tang, Lei Sun, Jiahong Wu, Xiangxiang Chu, Zeming Liu,","arxiv_match":false},{"id":"KP1C5Vu5Nr","title":"Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction","forum_url":"https://openreview.net/forum?id=KP1C5Vu5Nr","excerpt":"cessing, 13(4):600– 612, 2004. doi: 10.1109/TIP.2003.819861. Zile Wang, Zexiang Liu, Jiaxing Li, Kaichen Huang, Baixin Xu, Fei Kang, Mengyin An, Peiyu Wang, Biao Jiang, Yichen Wei, Yidan Xietian, Jiangbo Pei, Liang Hu, Boyi Jiang, Hua Xue, Zidong Wang, Haofeng Sun, Wei Li, Wanli Ouyang, Xianglong He, Yang Liu, Yangguang Li, and Yahui Zhou. Matrix-game 3.0: Real-time and streaming interactive world model with longhorizon memory, 2026. URL https://arxiv.org/abs/2604.08995. Haoyu Wu, Jiwen Yu, Yingtian Zou, and Xihui Liu. Multiworld: Scalable multi-agent multi-view video","arxiv_match":true}],"id":"2604.08995","metadata_source":"Cached arXiv API metadata","rank":923},{"title":"Mimicgen: A data generation system for scalable robot learning using human demonstrations","arxiv_id":"2310.17596","year":2023,"url":"https://arxiv.org/abs/2310.17596","citing_submissions":110,"explicit_arxiv_matches":39,"examples":[{"id":"vwDnaAnR3T","title":"When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models","forum_url":"https://openreview.net/forum?id=vwDnaAnR3T","excerpt":"learning. Advances in Neural Information Processing Systems, 36:44776–44791, 2023. Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, and Dieter Fox. Mimicgen: A data generation system for scalable robot learning using human demonstrations. In 7th Annual Conference on Robot Learning, 2023. Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard. Calvin: A benchmark for language-conditioned policy learning for long-horizon robo","arxiv_match":false},{"id":"dVXFFMVuTo","title":"ManiUnit: A Manipulation Skill Dataset and Benchmark for Long-Horizon Tasks","forum_url":"https://openreview.net/forum?id=dVXFFMVuTo","excerpt":"4791. Curran Associates, Inc., 2023. Mengyuan Liu et al. Eval-Actions: Fine-grained execution quality evaluation for robotic manipulation. arXiv preprint arXiv:2601.18723, 2026. Ajay Mandlekar et al. MimicGen: A data generation system for scalable robot learning using human demonstrations. In Proceedings of The 7th Conference on Robot Learning, volume 229 of Proceedings of Machine Learning Research, pp. 1820–1864. PMLR, 2023. Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Bu","arxiv_match":false},{"id":"f7uO3rAyIZ","title":"STAR: Practical Backdoor Attacks Against Vision-Language-Action Models via Semantic Triggers","forum_url":"https://openreview.net/forum?id=f7uO3rAyIZ","excerpt":" for embodied ai. IEEE Transactions on Neural Networks and Learning Systems, 2026. Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, and Dieter Fox. Mimicgen: A data generation system for scalable robot learning using human demonstrations. In Conference on Robot Learning, 2023. Tuan Anh Nguyen and Anh Tuan Tran. Input-aware dynamic backdoor attack. In Advances in Neural Information Processing Systems, volume 33, 2020. Tuan Anh Nguyen ","arxiv_match":false}],"id":"2310.17596","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":924},{"title":"SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution","arxiv_id":"2502.18449","year":2025,"url":"https://arxiv.org/abs/2502.18449","citing_submissions":110,"explicit_arxiv_matches":45,"examples":[{"id":"xVUT9vghTf","title":"OARS: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=xVUT9vghTf","excerpt":"rowseComp: A Simple Yet Challenging Benchmark for Browsing Agents. arXiv preprint arXiv:2504.12516, 2025a. URL https://arxiv.org/abs/2504.12516. Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux, Lingming Zhang, Daniel Fried, Gabriel Synnaeve, Rishabh Singh, and Sida I. Wang. SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution. In Advances in Neural Information Processing Systems, 2025b. URL https://arxiv.org/abs/2502.18449. Jialong Wu, Baixuan Li, Runnan Fang, Wenbiao Yin, Liwen Zhang, Zhenglin Wang, Zhengwei Tao, Ding-C","arxiv_match":true},{"id":"1dYqONTHJM","title":"SCOPE: Scaling Counterfactual, Observation-Preserving Environment Families for Terminal Agents","forum_url":"https://openreview.net/forum?id=1dYqONTHJM","excerpt":" 2025. URL https://openreview.net/forum?id=OJd3ayDDoF. Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux, Lingming Zhang, Daniel Fried, Gabriel Synnaeve, Rishabh Singh, and Sida I. Wang. SWE-RL: advancing LLM reasoning via reinforcement learning on open software evolution. In Danielle Belgrave, Cheng Zhang, Laura N. Montoya, Hsuan-Tien Lin, Razvan Pascanu, Piotr Koniusz, Marzyeh Ghassemi, Nancy Chen, Iván Vladimir Meza Ruı́z, and Arturo LoaizaBonilla (eds.), Advances","arxiv_match":false},{"id":"trHfIrhMk3","title":"REINFORCEMENT LEARNING FROM ENVIRONMENT KNOWLEDGE","forum_url":"https://openreview.net/forum?id=trHfIrhMk3","excerpt":"e language models. arXiv preprint arXiv:2305.16291, 2023. Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux, Lingming Zhang, Daniel Fried, Gabriel Synnaeve, Rishabh Singh, and Sida Wang. Swe-rl: Advancing llm reasoning via reinforcement learning on open software evolution. Advances in Neural Information Processing Systems, 38:78500–78525, 2026. Shanli Xing, Yiyan Zhai, Alexander Jiang, Yixin Dong, Yong Wu, Zihao Ye, Charlie F Ruan, Yingyi Huang, Yineng Zhang, Liangshe","arxiv_match":false}],"id":"2502.18449","metadata_source":"Cached arXiv API metadata","rank":925},{"title":"Self-taught optimizer (STOP): Recursively self-improving code generation","arxiv_id":"2310.02304","year":2023,"url":"https://arxiv.org/abs/2310.02304","citing_submissions":110,"explicit_arxiv_matches":56,"examples":[{"id":"gR8f42PYQl","title":"Beneath the Surface: What We Overlook When We Talk About Agent Evolution","forum_url":"https://openreview.net/forum?id=gR8f42PYQl","excerpt":"age model feedback. Nat., 639(8055):609–616, 2025. doi: 10.1038/S41586-025-08661-4. URL https://doi.org/10. 1038/s41586-025-08661-4. Eric Zelikman, Eliana Lorch, Lester Mackey, and Adam Tauman Kalai. Self-taught optimizer (STOP): Recursively self-improving code generation. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=46Zgqo4QIU. Hanrong Zhang, Shicheng Fan, Henry Peng Zou, Yankai Chen, Zhenting Wang, Jiayu Zhou, Chengze Li, Wei-C","arxiv_match":false},{"id":"qNVOX4SRA1","title":"DataOS: A Recursive Self-Improvement System Driven by Environment Data","forum_url":"https://openreview.net/forum?id=qNVOX4SRA1","excerpt":"ames Zou. TextGrad: Automatic Differentiation via Text. arXiv preprint arXiv:2406.07496, 2024. URL https://arxiv.org/abs/2406.07496. Eric Zelikman, Eliana Lorch, Lester Mackey, and Adam Tauman Kalai. Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=1gkePTsAWf. Jenny Zhang, Shengran Hu, Cong Lu, Robert Tjarko Lange, and Jeff Clune. Darwin Godel Machine: Open-End","arxiv_match":false},{"id":"6EWMGy11eE","title":"CreditAgentBench: Measuring Agentic Credit-Model Repair under Production Constraints","forum_url":"https://openreview.net/forum?id=6EWMGy11eE","excerpt":"leventh International Conference on Learning Representations, 2023. I-Cheng Yeh and Che-hui Lien. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2): 2473–2480, 2009. doi: 10.1016/j.eswa.2007.12.020. Eric Zelikman, Eliana Lorch, Lester Mackey, and Adam Tauman Kalai. Self-taught optimizer (stop): Recursively self-improving code generation. arXiv preprint arXiv:2310.02304, 2023. Shujian Zhang, Chengyue Gong, Lemeng Wu, Xingchao Liu, and Mingyuan Zhou. AutoML-GPT: Automa","arxiv_match":true}],"id":"2310.02304","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":926},{"title":"Animatediff: Animate your personalized text-to-image diffusion models without specific tuning","arxiv_id":"2307.04725","year":2023,"url":"https://arxiv.org/abs/2307.04725","citing_submissions":109,"explicit_arxiv_matches":51,"examples":[{"id":"goQTywWWdx","title":"VestEdit: Inversion-Free Video Editing via Spatio-Temporal Transport Stabilization","forum_url":"https://openreview.net/forum?id=goQTywWWdx","excerpt":" Zhang, Mike Zheng Shou, and Kevin Tang. Videoswap: Customized video subject swapping with interactive semantic point correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7621–7630, 2024. Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725, 2023. Yi Huang, Wei Xiong, He Zhang, Chaoqi Chen, Jianzhuang Liu, Mingfu Yan, and Shifeng Chen. Di","arxiv_match":true},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"802–85822, 2026. Google DeepMind. Veo 3.1. https://deepmind.google/models/veo/, 2025. Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. International Conference on Learning Representations, 2024. Yuwei Guo, Ceyuan Yang, Hao He, Yang Zhao, Meng Wei, Zhenheng Yang, Weilin Huang, and Dahua Lin. End-to-end training for autoregressive vi","arxiv_match":false},{"id":"gm7CWhtpxy","title":"Towards Coherent Scene Reconstruction in Video Object Removal","forum_url":"https://openreview.net/forum?id=gm7CWhtpxy","excerpt":" Computer Vision, pp. 713–729. Springer, 2020. URL https:// www.chengao.vision/FGVC/. Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. AnimateDiff: Animate your personalized text-to-image diffusion models without specific tuning. In International Conference on Learning Representations, 2024. URL https://proceedings.iclr.cc/paper_files/paper/2024/hash/ 94894cf9bab20c00f6eaaff3b7f2be70-Abstract-Conference.html. Jonathan Ho, Ti","arxiv_match":false}],"id":"2307.04725","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":927},{"title":"Defending against indirect prompt injection attacks with spotlighting","arxiv_id":"2403.14720","year":2024,"url":"https://arxiv.org/abs/2403.14720","citing_submissions":109,"explicit_arxiv_matches":82,"examples":[{"id":"y5X5ZaufZQ","title":"Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections","forum_url":"https://openreview.net/forum?id=y5X5ZaufZQ","excerpt":"0, 2025b. Yufei He, Juncheng Liu, Zhiyuan Hu, Yulin Chen, Yue Liu, Yuan Sui, Yibo Li, Nuo Chen, Jun Hu, Bryan Hooi, et al. Evoclinician: A self-evolving agent for multi-turn medical diagnosis via test-time evolutionary learning. arXiv preprint arXiv:2601.22964, 2026. Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, and Emre Kiciman. Defending against indirect prompt injection attacks with spotlighting, 2024. URL https:// arxiv.org/abs/2403.14720. Hengle Jiang and Ke Tang. Why agents compromise safety under pressure, 2026. URL https: //arxiv.or","arxiv_match":true},{"id":"81BK0yhhXO","title":"Document-Channel Manipulation in a Text-Extracted AI Review Pipeline","forum_url":"https://openreview.net/forum?id=81BK0yhhXO","excerpt":"v preprint arXiv:2402.10886, 2024. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection. arXiv preprint arXiv:2302.12173, 2023. Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, and Emre Kiciman. Defending Against Indirect Prompt Injection Attacks With Spotlighting. arXiv preprint arXiv:2403.14720, 2024. Maximilian Idahl and Zahra Ahmadi. OpenReviewer: A Specialized Large Language Model for Gene","arxiv_match":true},{"id":"yoJP018EVw","title":"Your Agentic LLMs Can Latently Detect Indirect Prompt-Injection Attacks","forum_url":"https://openreview.net/forum?id=yoJP018EVw","excerpt":"t prompt injection in LLM agents via causal attribution of tool invocations. In USENIX Security, 2026. Kait Healy, Bharathi Srinivasan, Visakh Madathil, and Jing Wu. Internal representations as indicators of hallucinations in agent tool selection. arXiv preprint arXiv:2601.05214, 2026. Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, and Emre Kiciman. Defending against indirect prompt injection attacks with spotlighting. arXiv preprint arXiv:2403.14720, 2024. Kuo-Han Hung, Ching-Yun Ko, Ambrish Rawat, I-Hsin Chung, Winston H. Hsu, and Pin-Yu Chen. At","arxiv_match":true}],"id":"2403.14720","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":928},{"title":"Defining and characterizing reward hacking","arxiv_id":"2209.13085","year":2022,"url":"https://arxiv.org/abs/2209.13085","citing_submissions":109,"explicit_arxiv_matches":44,"examples":[{"id":"9Ou6wrydAL","title":"Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following","forum_url":"https://openreview.net/forum?id=9Ou6wrydAL","excerpt":"t al. DeepSeekMath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger. Defining and characterizing reward hacking. In NeurIPS, 2022. Aman Singh Thakur, Kartik Choudhary, Venkat Srinik Ramayapally, Sankaran Vaidyanathan, and Dieuwke Hupkes. Judging the judges: Evaluating alignment and vulnerabilities in llms-as-j","arxiv_match":false},{"id":"KvWRJQwcme","title":"AUDIT-FIRST VAPO: RISK-CERTIFIED SELECTIVE UPDATES UNDER IMPERFECT VERIFICATION","forum_url":"https://openreview.net/forum?id=KvWRJQwcme","excerpt":" Reflexion: Language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems, 2023. Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, and David Krueger. Defining and characterizing reward hacking. In Advances in Neural Information Processing Systems, 2022. Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. Language models don’t always say what they think: Unfaithful explanations","arxiv_match":false},{"id":"gPfo3Fge2p","title":"RSD-Poker: Structure-Adaptive and Shift-Robust Risk--Utility Certification for Residual Policies in Imperfect-Information Games","forum_url":"https://openreview.net/forum?id=gPfo3Fge2p","excerpt":"lver, Kelsey Allen, Josh Tenenbaum, and Leslie Kaelbling. Residual policy learning. arXiv preprint arXiv:1812.06298, 2018. Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, and David Krueger. Defining and characterizing reward hacking. In Advances in Neural Information Processing Systems, 2022. Oskari Tammelin, Neil Burch, Michael Johanson, and Michael Bowling. Solving heads-up limit texas hold’em. In Proceedings of the Twenty-Fou","arxiv_match":false}],"id":"2209.13085","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":929},{"title":"LLaDA2.0: Scaling Up Diffusion Language Models to 100B","arxiv_id":"2512.15745","year":2025,"url":"https://arxiv.org/abs/2512.15745","citing_submissions":109,"explicit_arxiv_matches":101,"examples":[{"id":"UtSpCnqHk8","title":"Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving","forum_url":"https://openreview.net/forum?id=UtSpCnqHk8","excerpt":"tions, volume 2025, pp. 50726–50753, 2025. Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in neural information processing systems, 34:17981–17993, 2021. Tiwei Bie, Maosong Cao, Kun Chen, Lun Du, Mingliang Gong, Zhuochen Gong, Yanmei Gu, Jiaqi Hu, Zenan Huang, Zhenzhong Lan, et al. LLaDA2.0: Scaling up diffusion language models to 100B. arXiv preprint arXiv:2512.15745, 2025. Tzu-Tao Chang, Benjamin Yuanyang Hong, Kiet Pham, and Shivaram Venkataraman. DiLaServe: High","arxiv_match":true},{"id":"6zSuqyB4a9","title":"What Makes a Good Decoding Path? Selecting Parallel-Decodable Trajectories for Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=6zSuqyB4a9","excerpt":"openreview.net/forum?id=XjcHRIu0iF. Heli Ben-Hamu, Itai Gat, Daniel Severo, Niklas S Nolte, and Brian Karrer. Accelerated sampling from masked diffusion models via entropy bounded unmasking. Advances in Neural Information Processing Systems, 38:55981–56007, 2026. Tiwei Bie, Maosong Cao, Kun Chen, Lun Du, Mingliang Gong, Zhuochen Gong, Yanmei Gu, Jiaqi Hu, Zenan Huang, Zhenzhong Lan, et al. Llada2. 0: Scaling up diffusion language models to 100b. arXiv preprint arXiv:2512.15745, 2025. Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative","arxiv_match":true},{"id":"Xt9oJoQrHM","title":"Light-DiffRetriever: Budget-Flexible Retrieval with Diffusion Language Models via Matryoshka Representative Tokens","forum_url":"https://openreview.net/forum?id=Xt9oJoQrHM","excerpt":"-05961. Tiwei Bie, Maosong Cao, Kun Chen, Lun Du, Mingliang Gong, Zhuochen Gong, Yanmei Gu, Jiaqi Hu, Zenan Huang, Zhenzhong Lan, Chengxi Li, Chongxuan Li, Jianguo Li, Zehuan Li, Huabin Liu, Lin Liu, Guoshan Lu, Xiaocheng Lu, Yuxin Ma, Jianfeng Tan, Lanning Wei, Ji-Rong Wen, Yipeng Xing, Xiaolu Zhang, Junbo Zhao, Da Zheng, Jun Zhou, Junlin Zhou, Zhanchao Zhou, Liwang Zhu, and Yihong Zhuang. LLaDA2.0: Scaling Up Diffusion Language Models to 100B. arXiv preprint arXiv:2512.15745, 2025. doi: 10.48550/ARXIV.2512.15745. URL https://dblp.org/rec/journals/corr/abs-2512-15745. Tiwei","arxiv_match":true}],"id":"2512.15745","metadata_source":"Cached arXiv API metadata","rank":930},{"title":"LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training","arxiv_id":"2509.23661","year":2025,"url":"https://arxiv.org/abs/2509.23661","citing_submissions":109,"explicit_arxiv_matches":95,"examples":[{"id":"BHZRTrLklI","title":"Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference","forum_url":"https://openreview.net/forum?id=BHZRTrLklI","excerpt":" Alvar, Gursimran Singh, Mohammad Akbari, and Yong Zhang. Divprune: Diversitybased visual token pruning for large multimodal models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9392–9401. IEEE, 2025. Xiang An, Yin Xie, Kaicheng Yang, Wenkang Zhang, Xiuwei Zhao, Zheng Cheng, Yirui Wang, Songcen Xu, Changrui Chen, Didi Zhu, et al. Llava-onevision-1.5: Fully open framework for democratized multimodal training. arXiv preprint arXiv:2509.23661, 2025. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, W","arxiv_match":true},{"id":"GGw37srpoS","title":"Safe Representation for Combined Image-Text Generation in Multi-Turn Conversations","forum_url":"https://openreview.net/forum?id=GGw37srpoS","excerpt":"Cheng, Yirui Wang, Songcen Xu, Changrui Chen, Chunsheng Wu, Huajie Tan, Chunyuan Li, Jing Yang, Jie Yu, Xiyao Wang, Bin Qin, Yumeng Wang, Zizhen Yan, Ziyong Feng, Ziwei Liu, Bo Li, and Jiankang Deng. Llava-onevision-1.5: Fully open framework for democratized multimodal training. In arXiv, 2025. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding, local","arxiv_match":false},{"id":"YHLCbk9cpy","title":"RxBrain: Embodied Foundation Model with Joint Language-Visual Subgoal Planning","forum_url":"https://openreview.net/forum?id=YHLCbk9cpy","excerpt":" Niket Agarwal, Arslan Ali, Jon Allen, Martin Antolini, Adeline Aubame, Alisson Azzolini, Junjie Bai, Maciej Bala, Yogesh Balaji, Josh Bapst, et al. Cosmos 3: Omnimodal world models for physical ai. arXiv preprint arXiv:2606.02800, 2026. Xiang An, Yin Xie, Kaicheng Yang, Wenkang Zhang, Xiuwei Zhao, Zheng Cheng, Yirui Wang, Songcen Xu, Changrui Chen, Didi Zhu, et al. Llava-onevision-1.5: Fully open framework for democratized multimodal training. arXiv preprint arXiv:2509.23661, 2025. Mahmoud Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, M","arxiv_match":true}],"id":"2509.23661","metadata_source":"Cached arXiv API metadata","rank":931},{"title":"Progent: Securing AI Agents with Privilege Control","arxiv_id":"2504.11703","year":2025,"url":"https://arxiv.org/abs/2504.11703","citing_submissions":109,"explicit_arxiv_matches":105,"examples":[{"id":"ogCG1QdTOB","title":"Act on Your Behalf, Beyond Your Authorization: Benchmarking LLM Agent Authorization Alignment","forum_url":"https://openreview.net/forum?id=ogCG1QdTOB","excerpt":"instein, and Charles E Youman. Role-based access control models. Computer, 29(2):38–47, 1996. Yijia Shao, Tianshi Li, Weiyan Shi, Yanchen Liu, and Diyi Yang. Privacylens: Evaluating privacy norm awareness of language models in action. Advances in Neural Information Processing Systems, 37:89373–89407, 2024. Tianneng Shi, Jingxuan He, Zhun Wang, Linyu Wu, Hongwei Li, Wenbo Guo, and Dawn Song. Progent: Programmable privilege control for llm agents. arXiv preprint arXiv:2504.11703, 2025. Jeremy Tien, Abishek Anand, Yu-Rou Tuan, Yuchen Shen, J Zico Kolter, and Aran Nayebi. Rogue:","arxiv_match":true},{"id":"FCnWjmOlAg","title":"PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust","forum_url":"https://openreview.net/forum?id=FCnWjmOlAg","excerpt":" Computer Aided Verification, pp. 161–179, Cham, 2019. Springer International Publishing. ISBN 978-3-030-25540-4. Tianneng Shi, Jingxuan He, Zhun Wang, Hongwei Li, Linyu Wu, Wenbo Guo, and Dawn Song. Progent: Securing AI Agents with Privilege Control. arXiv, 2025. URL https://doi.org/ 10.48550/arXiv.2504.11703. Saksham Sahai Srivastava and Haoyu He. MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval. arXiv, 2025. U","arxiv_match":false},{"id":"TO4R4c6u7V","title":"From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents","forum_url":"https://openreview.net/forum?id=TO4R4c6u7V","excerpt":"ent approvals and security. URL https://learn.chatgpt.com/docs/agent -approvals-security. Yubin Qu, Ying Zhang, Yanjun Zhang, Gelei Deng, Yuekang Li, Leo Yu Zhang, and Yi Liu. Overeager coding agents: Measuring out-of-scope actions on benign tasks, 2026. URL https: //arxiv.org/abs/2605.18583. Tianneng Shi, Jingxuan He, Zhun Wang, Hongwei Li, Linyu Wu, Wenbo Guo, and Dawn Song. Progent: Securing ai agents with privilege control, 2026. URL https://arxiv.org/abs/ 2504.11703. Ankur Singh, Jinqiu Yang, and Tse-Hsun Chen. Issuetrojanbench: Benchmarking ai coding agents again","arxiv_match":true}],"id":"2504.11703","metadata_source":"Cached arXiv API metadata","rank":932},{"title":"REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization","arxiv_id":"2501.03262","year":2025,"url":"https://arxiv.org/abs/2501.03262","citing_submissions":109,"explicit_arxiv_matches":108,"examples":[{"id":"SDpgQpUVmC","title":"SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents","forum_url":"https://openreview.net/forum?id=SDpgQpUVmC","excerpt":"hang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Liu, Z. T. Xie, Y. K. Huang, Y. Q. Zhang, W. Q. Li, Z. H. Gou, Z. Shao, et al. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. Jian Hu, Jason Klein Liu, Haotian Xu, and Wei Shen. REINFORCE++: Stabilizing critic-free policy optimization with global advantage normalization. arXiv preprint arXiv:2501.03262, 2025. Shiqi Huang, Shuting He, and Bihan Wen. RSGround-R1: Rethinking remote sensing visual ground","arxiv_match":true},{"id":"V7gMQjOqcP","title":"Learning from Single-Feedback Deployment Experience","forum_url":"https://openreview.net/forum?id=V7gMQjOqcP","excerpt":"imization for agentic reinforcement learning. arXiv preprint arXiv:2607.07508, 2026. URL https:// arxiv.org/abs/2607.07508. HKUDS. nanobot: Ultra-lightweight, open-source, self-hosted personal AI agent framework. https://github.com/HKUDS/nanobot, 2026. Jian Hu, Jason Klein Liu, Haotian Xu, and Wei Shen. REINFORCE++: Stabilizing critic-free policy optimization with global advantage normalization. arXiv preprint arXiv:2501.03262, 2025. URL https://arxiv.org/abs/2501.03262. Jian Hu, Yifan Zhang, Hao Zhang, Binfeng Xu, Shaokun Zhang, Hongqing Peng, Zhiding Yu, Pavlo Molch","arxiv_match":true},{"id":"9J4mJ0maK0","title":"Learning from Hard Prompts: Difficulty-aware Advantage Amplification in Dynamic Sampling","forum_url":"https://openreview.net/forum?id=9J4mJ0maK0","excerpt":"Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the MATH dataset. In Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=7Bywt2mQsCe. Jian Hu, Jason Klein Liu, Haotian Xu, and Wei Shen. Reinforce++: Stabilizing critic-free policy optimization with global advantage normalization, 2025. URL https://arxiv.org/abs/2501. 03262. Mengqi Liao, Xiangyu Xi, Chen Ruinian, Jia Leng, Yangen Hu, Ke Zeng, Shuai Liu, and Huaiyu Wan. En","arxiv_match":true}],"id":"2501.03262","metadata_source":"Cached arXiv API metadata","rank":933},{"title":"Remasking Discrete Diffusion Models with Inference-Time Scaling","arxiv_id":"2503.00307","year":2025,"url":"https://arxiv.org/abs/2503.00307","citing_submissions":109,"explicit_arxiv_matches":43,"examples":[{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"ith verbal reinforcement learning. In Advances in Neural Information Processing Systems, volume 36, 2023. URL https://proceedings.neurips.cc/paper_files/paper/2023/hash/ 1b44b878bb782e6954cd888628510e90-Abstract-Conference.html. Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo, and Volodymyr Kuleshov. Remasking discrete diffusion models with inference-time scaling. In Advances in Neural Information Processing Systems, volume 38, 2025. URL https://arxiv.org/abs/2503.00307. Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and","arxiv_match":true},{"id":"9GDStxR3kP","title":"Inter-Trajectory Dependence Limits Answer Coverage in Local dLLM Repair","forum_url":"https://openreview.net/forum?id=9GDStxR3kP","excerpt":"s Lourie, and Jonathan Berant. Commonsenseqa: A question answering challenge targeting commonsense knowledge. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4149–4158, 2019. Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo, and Volodymyr Kuleshov. Remasking discrete diffusion models with inference-time scaling. arXiv preprint arXiv:2503.00307, 2025. Xinyun Wang, Min Zhang, Sen Cui, Zhikang Chen, Bo Jiang, Kun Kuang, and Mingbao Lin. Reversi","arxiv_match":true},{"id":"vwvDGT3GUw","title":"When Anchors Commit, Coherence Breaks","forum_url":"https://openreview.net/forum?id=vwvDGT3GUw","excerpt":"al Conference on Learning Representations, 2025. URL https://openreview. net/forum?id=zGb4WgCW5i. Lipeng Wan, Junjie Ma, Jianhui Gu, Zeyang Liu, Xuyang Lu, and Xuguang Lan. Geoblock: Inferring block granularity from dependency geometry in diffusion language models. arXiv preprint arXiv:2603.26675, 2026. Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo, and Volodymyr Kuleshov. Remasking discrete diffusion models with inference-time scaling (2025). arXiv preprint arXiv:2503.00307. Kangyu Wang, Zhiyun Jiang, Haibo Feng, Weijia Zhao, Lin Liu, Jianguo Li, Zhenzhong Lan, and Weiyao","arxiv_match":true}],"id":"2503.00307","metadata_source":"Cached arXiv API metadata","rank":934},{"title":"Retrieval head mechanistically explains long-context factuality","arxiv_id":"2404.15574","year":2024,"url":"https://arxiv.org/abs/2404.15574","citing_submissions":109,"explicit_arxiv_matches":31,"examples":[{"id":"jOFCJVBaVA","title":"Exploring the Boundary of Sparse Attention: Retrievability, Head Stability, and ANN Approximation","forum_url":"https://openreview.net/forum?id=jOFCJVBaVA","excerpt":". RazorAttention: Efficient KV cache compression through retrieval heads. In International Conference on Learning Representations, 2025. Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng, and Yao Fu. Retrieval head mechanistically explains long-context factuality. In International Conference on Learning Representations, 2025. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks. In Inter","arxiv_match":false},{"id":"KI4Zhu4F05","title":"Extrapolation Collapse Is a Readout Failure: Long-Context Models Retrieve What They Cannot Say","forum_url":"https://openreview.net/forum?id=KI4Zhu4F05","excerpt":"Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024. Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng, and Yao Fu. Retrieval head mechanistically explains long-context factuality. In International Conference on Learning Representations (ICLR), 2025. Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks. I","arxiv_match":false},{"id":"j9341cAeqX","title":"ViFlow-KV: Visual-Flow Guided KV Cache Eviction For Multimodal Reasoning","forum_url":"https://openreview.net/forum?id=j9341cAeqX","excerpt":"ing gaps in realistic chart understanding in multimodal llms. Advances in Neural Information Processing Systems, 37:113569–113697, 2024. Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng, and Yao Fu. Retrieval head mechanistically explains long-context factuality. In International Conference on Learning Representations, volume 2025, pp. 62143–62156, 2025. Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Junxian Guo, Shang Yang, Haotian Tang, Yao Fu, and Song Han. D","arxiv_match":false}],"id":"2404.15574","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":935},{"title":"TabArena: A Living Benchmark for Machine Learning on Tabular Data","arxiv_id":"2506.16791","year":2025,"url":"https://arxiv.org/abs/2506.16791","citing_submissions":109,"explicit_arxiv_matches":24,"examples":[{"id":"m51bD7lgjx","title":"Characterizing the Separability Gap in Prior-Data Fitted Networks","forum_url":"https://openreview.net/forum?id=m51bD7lgjx","excerpt":"ecified Models. Statistica Sinica, 23(1):169–187, 2013. doi: 10.5705/ss.2010.239. Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Desai, David Salinas, and Frank Hutter. Tabarena: A living benchmark for machine learning on tabular data. Advances in Neural Information Processing Systems, 38, 2026. Fabian Falck, Ziyu Wang, and Christopher C. Holmes. Is in-context learning in large language models bayesian? A martingale perspective. I","arxiv_match":false},{"id":"mZ3FfjH6Gm","title":"Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=mZ3FfjH6Gm","excerpt":". TabLib: A dataset of 627M tables with context. arXiv: 2310.07875, 2023. Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, and Frank Hutter. TabArena: A living benchmark for machine learning on tabular data. In Advances in Neural Information Processing Systems, 2025. Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, and Yonglong Tian. Scaling Laws of Synthetic Images for Model Trainin","arxiv_match":false},{"id":"wSHvuy1308","title":"Training Fair Tabular Foundation Models","forum_url":"https://openreview.net/forum?id=wSHvuy1308","excerpt":"mputer Science Conference, 2012. ISBN 9781450311151. doi: 10.1145/2090236.2090255. Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Desai, David Salinas, and Frank Hutter. TabArena: A Living Benchmark for Machine Learning on Tabular Data. In Advances in Neural Information Processing Systems, volume 38. Curran Associates, Inc., 2025. doi: 10.52202/085713-0519. Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by back","arxiv_match":false}],"id":"2506.16791","metadata_source":"Cached arXiv API metadata","rank":936},{"title":"Videophy: Evaluating physical commonsense for video generation","arxiv_id":"2406.03520","year":2024,"url":"https://arxiv.org/abs/2406.03520","citing_submissions":109,"explicit_arxiv_matches":40,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"yaWorld Team, Kaipeng Zhang, Chuanhao Li, Yifan Zhan, Yongtao Ge, Yuanyang Yin, et al. AlayaWorld: Interactive Long-Horizon World Modeling – Full Technical Report. arXiv preprint arXiv:2607.18367, 2026. URL https://arxiv.org/abs/2607.18367. Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai-Wei Chang, and Aditya Grover. VideoPhy: Evaluating physical commonsense for video generation. arXiv preprint arXiv:2406.03520, 2024. Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe ","arxiv_match":true},{"id":"rtlzkXdJe9","title":"PhysiCalWorld: In-Context Physics Calibration for World Models","forum_url":"https://openreview.net/forum?id=rtlzkXdJe9","excerpt":"and planning. arXiv preprint arXiv:2506.09985, 2025. 2 Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai-Wei Chang, and Aditya Grover. Videophy: Evaluating physical commonsense for video generation. In International Conference on Learning Representations, volume 2025, pp. 102075–102121, 2025. 3 Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, and Alexei Efros. Visual prompting via im","arxiv_match":false},{"id":"BKhScrAhtQ","title":"Phase-Aware Video Generation for Physics-Grounded Dynamics and Interactions","forum_url":"https://openreview.net/forum?id=BKhScrAhtQ","excerpt":" Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai-Wei Chang, and Aditya Grover. VideoPhy: Evaluating physical commonsense for video generation. arXiv preprint arXiv:2406.03520, 2024. Hritik Bansal, Clark Peng, Yonatan Bitton, Roman Goldenberg, Aditya Grover, and Kai-Wei Chan","arxiv_match":true}],"id":"2406.03520","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":937},{"title":"Agent-as-a-judge: Evaluate agents with agents","arxiv_id":"2410.10934","year":2024,"url":"https://arxiv.org/abs/2410.10934","citing_submissions":108,"explicit_arxiv_matches":44,"examples":[{"id":"1roHwQ9Wdi","title":"Certified Selective Automation of LLM Agent Evaluation","forum_url":"https://openreview.net/forum?id=1roHwQ9Wdi","excerpt":"ume 2024, pp. 15585–15606, 2024. Lianghui Zhu, Xinggang Wang, and Xinlong Wang. Judgelm: Fine-tuned large language models are scalable judges. In International Conference on Learning Representations, volume 2025, pp. 51257–51296, 2025. Mingchen Zhuge, Changsheng Zhao, Dylan Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, et al. Agent-asa-judge: Evaluate agents with agents. arXiv preprint arXiv:2410.10934, 2024. A Proof of Proposition 1. Let ϕ : [0, 1] → [0, 1] be strictly increasing and s̃ = ϕ ◦ s. The","arxiv_match":true},{"id":"c2TBI8RzwD","title":"Audit Without Verification: When LLM Accountability Layers Relay Rather Than Check","forum_url":"https://openreview.net/forum?id=c2TBI8RzwD","excerpt":"angsheng Zhao, Dylan R. Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, and Jürgen Schmidhuber. Agent-as-a-judge: Evaluate agents with agents. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 80569–80611. PMLR, 2025. URL https: //proceedings.mlr.press/v267/","arxiv_match":false},{"id":"aCc0AKg8B4","title":"What Can a Leaderboard Certify? Compositional Controllability for Fair Evaluation and Training of Biomedical Literature-Review Agents","forum_url":"https://openreview.net/forum?id=aCc0AKg8B4","excerpt":"angsheng Zhao, Dylan R. Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, and Jürgen Schmidhuber. Agent-as-a-judge: Evaluate agents with agents. In Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, and Jerry Zhu (eds.), Forty-second International Conference on Machine Learning, ICML ","arxiv_match":false}],"id":"2410.10934","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":938},{"title":"Language model beats diffusion – tokenizer is key to visual generation","arxiv_id":"2310.05737","year":2023,"url":"https://arxiv.org/abs/2310.05737","citing_submissions":108,"explicit_arxiv_matches":30,"examples":[{"id":"xAVonyP1SQ","title":"VQ-DSGAN: Mitigating Latent Representation Collapse via Dual-Space Discrimination","forum_url":"https://openreview.net/forum?id=xAVonyP1SQ","excerpt":"d VQGAN. arXiv preprint arXiv:2110.04627, 2021. Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion– tokenizer is key to visual generation. In Proceedings of the International Conference on Learning Representations, 2024. Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and Liang-Chieh Chen. Randomized autoregressive visual generation. In ","arxiv_match":false},{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"itesh Bharadwaj Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G. Hauptmann, Boqing Gong, Ming-Hsuan Yang, Irfan Essa, David A. Ross, and Lu Jiang. Language model beats diffusion - tokenizer is key to visual generation. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. URL https://openreview. net/forum?id=gzqrANCF4g. Lunbin Zeng, J","arxiv_match":false},{"id":"GM9LePOwrX","title":"Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=GM9LePOwrX","excerpt":"itesh Bharadwaj Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G. Hauptmann, Boqing Gong, Ming-Hsuan Yang, Irfan Essa, David A. Ross, and Lu Jiang. Language model beats diffusion - tokenizer is key to visual generation. In ICLR. OpenReview.net, 2024. Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion trans","arxiv_match":false}],"id":"2310.05737","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":939},{"title":"Latent Visual Reasoning","arxiv_id":"2509.24251","year":2025,"url":"https://arxiv.org/abs/2509.24251","citing_submissions":108,"explicit_arxiv_matches":31,"examples":[{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"14, pp. 235–251. Springer, 2016. Ang Li, Charles Wang, Deqing Fu, Kaiyu Yue, Zikui Cai, Wang Bill Zhu, Ollie Liu, Peng Guo, Willie Neiswanger, Furong Huang, Tom Goldstein, and Micah Goldblum. Zebra-cot: A dataset for interleaved vision language reasoning, 2025a. URL https://arxiv.org/abs/2507.16746. Bangzheng Li, Ximeng Sun, Jiang Liu, Ze Wang, Jialian Wu, Xiaodong Yu, Hao Chen, Emad Barsoum, Muhao Chen, and Zicheng Liu. Latent visual reasoning. arXiv preprint arXiv:2509.24251, 2025b. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image","arxiv_match":true},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749, 2025. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. Bangzheng Li, Ximeng Sun, Jiang Liu, Ze Wang, Jialian Wu, Xiaodong Yu, Hao Chen, Emad Barsoum, Muhao Chen, and Zicheng Liu. Latent visual reasoning. arXiv preprint arXiv:2509.24251, 2025a. Kelvin Li, Chuyi Shang, Leonid Karlinsky, Rogerio Feris, Trevor Darrell, and Roei Herzig. L","arxiv_match":true},{"id":"YoY3Gk7Syu","title":"Region-Level Policy Optimization for Fine-grained MLLM Perception","forum_url":"https://openreview.net/forum?id=YoY3Gk7Syu","excerpt":"l search. arXiv preprint arXiv:2509.07969, 2025. Jewon Lee, Wooksu Shin, Seungmin Yang, Ki-Ung Song, DongUk Lim, Jaeyeon Kim, Tae-Ho Kim, and Bo-Kyeong Kim. Ergo: Efficient high-resolution visual understanding for vision-language models. In International Conference on Learning Representations, 2026. Bangzheng Li, Ximeng Sun, Jiang Liu, Ze Wang, Jialian Wu, Xiaodong Yu, Hao Chen, Emad Barsoum, Muhao Chen, and Zicheng Liu. Latent visual reasoning. arXiv preprint arXiv:2509.24251, 2025. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zha","arxiv_match":true}],"id":"2509.24251","metadata_source":"Cached arXiv API metadata","rank":940},{"title":"Learning to Reason under Off-Policy Guidance","arxiv_id":"2504.14945","year":2025,"url":"https://arxiv.org/abs/2504.14945","citing_submissions":108,"explicit_arxiv_matches":44,"examples":[{"id":"hoQmkt2Ajc","title":"From Imitation to Reward Discovery: On-Policy Warmup for Agentic RL","forum_url":"https://openreview.net/forum?id=hoQmkt2Ajc","excerpt":"i Lu, Fan Zhang, Yuhao Shen, Lang Feng, Haoran Luo, Zheng Lian, Shuai Zhang, Zhengqi Wen, and Jianhua Tao. SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning. arXiv preprint arXiv:2607.14777, 2026. URL https: //arxiv.org/abs/2607.14777. Jianhao Yan, Yafu Li, Zican Hu, Zhi Wang, Ganqu Cui, Xiaoye Qu, Yu Cheng, and Yue Zhang. Learning to Reason under Off-Policy Guidance. arXiv preprint arXiv:2504.14945, 2025. URL https://arxiv.org/abs/2504.14945. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Cheng","arxiv_match":true},{"id":"7NBlFCtd2e","title":"Radioactive Feedback: Tracing Models Trained by Proprietary LLM Judges","forum_url":"https://openreview.net/forum?id=7NBlFCtd2e","excerpt":"cting the misuse of open-source large language models via watermarking. arXiv preprint arXiv:2503.04636, 2025. Jianhao Yan, Yafu Li, Zican Hu, Zhi Wang, Ganqu Cui, Xiaoye Qu, Yu Cheng, and Yue Zhang. Learning to reason under off-policy guidance. In Advances in Neural Information Processing Systems, 2025. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 techni","arxiv_match":false},{"id":"w5JR2zPMQM","title":"OP-GRPO: Efficient Off-Policy GRPO for Flow-Matching Models","forum_url":"https://openreview.net/forum?id=w5JR2zPMQM","excerpt":"mage generation. Advances in Neural Information Processing Systems, 36:15903–15935, 2023. Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al. Dancegrpo: Unleashing grpo on visual generation. arXiv preprint arXiv:2505.07818, 2025. Jianhao Yan, Yafu Li, Zican Chen, Chao Fang, Fandong Yin, Zhenghao Wang, Chao Wang, and Yue Zhang. Learning to reason under off-policy guidance. arXiv preprint arXiv:2504.14945, 2025. Xiaofeng Yang, Cheng Chen, xulei yang, Fayao Liu, and Guosheng Lin. Text-to-image rectified ","arxiv_match":true}],"id":"2504.14945","metadata_source":"Cached arXiv API metadata","rank":941},{"title":"ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models","arxiv_id":"2505.24864","year":2025,"url":"https://arxiv.org/abs/2505.24864","citing_submissions":108,"explicit_arxiv_matches":48,"examples":[{"id":"9J4mJ0maK0","title":"Learning from Hard Prompts: Difficulty-aware Advantage Amplification in Dynamic Sampling","forum_url":"https://openreview.net/forum?id=9J4mJ0maK0","excerpt":"al Methods in Natural Language Processing, November 2025. URL https://aclanthology.org/2025.emnlp-main.75/. Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, and Yi Dong. ProRL: Prolonged reinforcement learning expands reasoning boundaries in large language models. In Proceedings of the Thirty-ninth Annual Conference on Neural Information Processing Systems, 2026a. URL https://openreview.net/forum?id=YPsJha5HXQ. Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao,","arxiv_match":false},{"id":"IkzBak1MYU","title":"RL Starts Before RL: Rethinking Policy Initialization for Outcome-Based RL","forum_url":"https://openreview.net/forum?id=IkzBak1MYU","excerpt":" arXiv:2604.13016, 2026b. URL https://arxiv.org/abs/2604.13016. Jianhua Lin. Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory, 37(1):145–151, 1991. doi: 10.1109/18.61115. Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, and Yi Dong. ProRL: Prolonged reinforcement learning expands reasoning boundaries in large language models. arXiv preprint arXiv:2505.24864, 2025. URL https://arxiv.org/abs/2505. 24864. Tao Liu, Taiqiang Wu, Mao Zheng, Xuan Luo, Runming Yang, Xuewei Yang, Junjie Wang, and Yujiu Yang.","arxiv_match":true},{"id":"DMczRQpvPK","title":"Beyond Prompt Count: How Data Shapes Transfer in On-Policy Distillation","forum_url":"https://openreview.net/forum?id=DMczRQpvPK","excerpt":"https://proceedings.neurips.cc/paper_files/paper/2023/ hash/43e9d647ccd3e4b7b5baab53f0368686-Abstract.html. Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, and Yi Dong. ProRL: Prolonged reinforcement learning expands reasoning boundaries in large language models. In Advances in Neural Information Processing Systems, volume 38, pp. 20475–20508, 2025. doi: 10.52202/085713-0608. URL https://proceedings.neurips.cc/paper_files/paper/2025/hash/ 1a22b912945fb7c0bdd","arxiv_match":false}],"id":"2505.24864","metadata_source":"Cached arXiv API metadata","rank":942},{"title":"Understanding deep learning requires rethinking generalization","arxiv_id":"1611.03530","year":2016,"url":"https://arxiv.org/abs/1611.03530","citing_submissions":108,"explicit_arxiv_matches":36,"examples":[{"id":"cTSE1miE8K","title":"Exact Finite-Sample Variance Decomposition of Subagging: A Spectral Filtering Perspective","forum_url":"https://openreview.net/forum?id=cTSE1miE8K","excerpt":"g the success of adaboost and random forests as interpolating classifiers. Journal of Machine Learning Research, 18(48):1–33, 2017. Tianning Xu, Ruoqing Zhu, and Xiaofeng Shao. On variance estimation of random forests with infinite-order u-statistics. Electronic Journal of Statistics, 18(1):2135–2207, 2024. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530, 2016. Zhengze Zhou, Lucas Mentch, and Giles Hooker. V-statistics and variance estimation. Journal ","arxiv_match":true},{"id":"Fhj95cY1wD","title":"Decoupling Token Roles in Autoregressive Pretraining","forum_url":"https://openreview.net/forum?id=Fhj95cY1wD","excerpt":"anguage models: Part 2.2, how to learn from mistakes on grade-school math problems. arXiv preprint arXiv:2408.16293, 2024. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning requires rethinking generalization. In International Conference on Learning Representations, 2017. Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A. Smith. How language model hallucinations can snowball. In International","arxiv_match":false},{"id":"POKMfOQvLW","title":"Analysis and Mitigation of Memorization in Multi-Modal Diffusion Models","forum_url":"https://openreview.net/forum?id=POKMfOQvLW","excerpt":"nference over event descriptions. Transactions of the association for computational linguistics, 2:67–78, 2014. Ge Yuan, Xiaodong Cun, Yong Zhang, Maomao Li, Chenyang Qi, Xintao Wang, Ying Shan, and Huicheng Zheng. Inserting anybody in diffusion models via celeb basis. arXiv preprint arXiv:2306.00926, 2023. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530, 2016. Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonab","arxiv_match":true}],"id":"1611.03530","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":943},{"title":"EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents","arxiv_id":"2502.09560","year":2025,"url":"https://arxiv.org/abs/2502.09560","citing_submissions":107,"explicit_arxiv_matches":49,"examples":[{"id":"4MqlhwiDQb","title":"ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills","forum_url":"https://openreview.net/forum?id=4MqlhwiDQb","excerpt":"eng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, et al. Skillrl: Evolving agents via recursive skill-augmented reinforcement learning. arXiv preprint arXiv:2602.08234, 2026. Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, et al. Embodiedbench: Comprehensive benchmarking multi-modal large language models for vision-driven embodied agents. arXiv preprint arXiv:2502.09560, 2025. Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei G","arxiv_match":true},{"id":"1FAprBGimt","title":"Embodied Reasoning and Action in Complex Environments","forum_url":"https://openreview.net/forum?id=1FAprBGimt","excerpt":"25. arXiv:2505.11886. Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, Heng Ji, Huan Zhang, and Tong Zhang. EmbodiedBench: Comprehensive benchmarking multi-modal large language models for visiondriven embodied agents. In International Conference on Machine Learning (ICML), 2025. Michał Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, and Sergey Levine. Robotic control via embodied chain-of-thought r","arxiv_match":false},{"id":"7yegfIsdbX","title":"RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents","forum_url":"https://openreview.net/forum?id=7yegfIsdbX","excerpt":" real world. arXiv preprint arXiv:2606.19980, 2026. Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, et al. Embodiedbench: Comprehensive benchmarking multi-modal large language models for vision-driven embodied agents. In International Conference on Machine Learning, pp. 70576–70631. PMLR, 2025. Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, and Jeff Clune. Darwin gödel machine: open-ended evolution of self-imp","arxiv_match":false}],"id":"2502.09560","metadata_source":"Cached arXiv API metadata","rank":944},{"title":"Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation","arxiv_id":"2507.10524","year":2025,"url":"https://arxiv.org/abs/2507.10524","citing_submissions":107,"explicit_arxiv_matches":39,"examples":[{"id":"56JPx9PTqd","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","forum_url":"https://openreview.net/forum?id=56JPx9PTqd","excerpt":"L https://aclanthology.org/2023.emnlp-main.298/. Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji, Aaron Courville, and Se-Young Yun. Mixtureof-recursions: Learning dynamic recursive depths for adaptive token-level computation. In Advances in Neural Information Processing Systems, volume 38, pp. 96572–96617, 2025. URL https://proceedings.neurips.cc/paper_files/paper/2025/ file/8b08bbf8b420faa6eeb4020720582ec7-Paper-Confere","arxiv_match":false},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"arning Representations, volume 2025, pp. 34282–34327, 2025. Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji, Aaron Courville, et al. Mixture-of-recursions: Learning dynamic recursive depths for adaptive token-level computation. Advances in Neural Information Processing Systems, 38:96572–96617, 2026. Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yux","arxiv_match":false},{"id":"iaMmGPCgNV","title":"Recursive Inference for Test-Time Compute Allocation in Time-Series Foundation Models","forum_url":"https://openreview.net/forum?id=iaMmGPCgNV","excerpt":"ing, 2025. URL https://arxiv.org/abs/2510.15821. Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji, Aaron Courville, and Se-Young Yun. Mixture-of-recursions: Learning dynamic recursive depths for adaptive token-level computation. In Advances in Neural Information Processing Systems, volume 38, Main Conference, pp. 96572–96617, 2025. doi: 10.52202/085713-3229. Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. Deep equilibrium ","arxiv_match":false}],"id":"2507.10524","metadata_source":"Cached arXiv API metadata","rank":945},{"title":"ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving","arxiv_id":"2506.08052","year":2025,"url":"https://arxiv.org/abs/2506.08052","citing_submissions":107,"explicit_arxiv_matches":31,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"ift and aggregation for motion planning. arXiv preprint arXiv:2607.14507, 2026. Kaixin Xiong, Xiangyu Guo, Fang Li, Sixu Yan, Gangwei Xu, Lijun Zhou, Long Chen, Haiyang Sun, Bing Wang, Kun Ma, et al. Recogdrive: A reinforced cognitive framework for end-to-end autonomous driving. In International Conference on Learning Representations, volume 2026, pp. 157518–157556, 2026. Jiawei Xu, Zhizhou Zhong, Zhijian Shu, Mingkai Jia, Mingxiao Li, Jia-Wang Bian, Qian Zhang, Kaicheng Zh","arxiv_match":false},{"id":"cbEkVPsGSJ","title":"DriveBeyond: Rethinking Group Advantage for Driving Policy Optimization","forum_url":"https://openreview.net/forum?id=cbEkVPsGSJ","excerpt":"odal understanding and generation. In ICLR, volume 2025, pp. 28240–28264, 2025. Kaixin Xiong, Xiangyu Guo, Fang Li, Sixu Yan, Gangwei Xu, Lijun Zhou, Long Chen, Haiyang Sun, Bing Wang, Kun Ma, et al. ReCogDrive: A reinforced cognitive framework for end-to-end autonomous driving. In ICLR, 2026. Yifang Xu, Jiahao Cui, Zhihao Zhu, Hanlin Shang, Shan Luan, Mingwang Xu, Feipeng Cai, Neng Zhang, Yaoyi Li, Jia Cai, et al. WAM-Flow: Parallel coarse-to-fine motion planning via discr","arxiv_match":false},{"id":"4mnt3eA9ir","title":"ActionGuide: Learning Transferable Action-Grounded Reasoning for Closed-Loop Driving","forum_url":"https://openreview.net/forum?id=4mnt3eA9ir","excerpt":"ers. In CVPR, 2026. Yongkang Li, Kaixin Xiong, Xiangyu Guo, Fang Li, Sixu Yan, Gangwei Xu, Lijun Zhou, Long Chen, Haiyang Sun, Bing Wang, Kun Ma, Guang Chen, Hangjun Ye, Wenyu Liu, and Xinggang Wang. Recogdrive: A reinforced cognitive framework for end-to-end autonomous driving. In International Conference on Learning Representations, volume 2026, pp. 157518–157556, 2026. Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M. Alvarez. Is ego status all ","arxiv_match":false}],"id":"2506.08052","metadata_source":"Cached arXiv API metadata","rank":946},{"title":"Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity","arxiv_id":"2607.00248","year":2026,"url":"https://arxiv.org/abs/2607.00248","citing_submissions":107,"explicit_arxiv_matches":73,"examples":[{"id":"BGtau6E80m","title":"Image Editing without Typing","forum_url":"https://openreview.net/forum?id=BGtau6E80m","excerpt":"preprint arXiv:2511.16857, 2025. Black Forest Labs. FLUX.2: Frontier Visual Intelligence. https://bfl.ai/blog/flux-2, 2025. ByteDance Seed. Seedream 4.0 Officially Released: Beyond Drawing, Into Imagination. https: //seed.bytedance.com/en/blog/seedream-4-0-officially-released-b eyond-drawing-into-imagination, 2025. Accessed: 2026-09-22. Bytedance Seed. Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity, 2026. URL https://arxiv.org/abs/2607.00248. ByteDance Seed. Deeper Thinking, More Accurate Generation — Introducing Seedream 5.0 Lite. https:/","arxiv_match":true},{"id":"bp0HEA9om7","title":"Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters","forum_url":"https://openreview.net/forum?id=bp0HEA9om7","excerpt":"gineering, 2020. Qwen Team. Qwen3.5: Towards native multimodal agents, February 2026. URL https://qwen. ai/blog?id=qwen3.5. Adele Schlombs. Huai-su and the beginnings of wild cursive script in Chinese calligraphy. Franz Steiner Verlag, 1998. Bytedance Seed. Seed1.8 Model Card: Towards generalized real-world agency. arXiv preprint arXiv:2603.20633, 2026a. Bytedance Seed. Seed2.0 Model Card: Towards intelligence frontier for real-world complexity. arXiv preprint arXiv:2607.00248, 2026b. Yongxin Shi, Chongyu Liu, Dezhi Peng, Cheng Jian, Jiarong Huang, and Lianwen Jin. M5HisDoc:","arxiv_match":true},{"id":"IMcQz8TBut","title":"DreamUI: Scaling Interactive Mobile Agent Environments with Generative UI World Model","forum_url":"https://openreview.net/forum?id=IMcQz8TBut","excerpt":"ndroidWorld: A dynamic benchmarking environment for autonomous agents. arXiv preprint arXiv:2405.14573, 2024. ByteDance Seed. Seed1.5-vl technical report. arXiv preprint arXiv:2505.07062, 2025a. Bytedance Seed. Seed1.8 model card: Towards generalized real-world agency. arXiv preprint, December 2025b. Technical Report. ByteDance Seed. Ui-tars-1.5, 2025c. Bytedance Seed. Seed2. 0 model card: Towards intelligence frontier for real-world complexity. arXiv preprint arXiv:2607.00248, 2026. Zhengyang Tang, Xin Lai, Pengyuan Lyu, Xinyuan Wang, Tianyi Bai, Chenxin Li, Yiduo Guo, Huaw","arxiv_match":true}],"id":"2607.00248","metadata_source":"Cached arXiv API metadata","rank":947},{"title":"The concrete distribution: A continuous relaxation of discrete random variables","arxiv_id":"1611.00712","year":2016,"url":"https://arxiv.org/abs/1611.00712","citing_submissions":107,"explicit_arxiv_matches":41,"examples":[{"id":"vSjw3bWmyF","title":"Who Contributes? Counterfactual Attribution in Sparse Mixture-of-Experts","forum_url":"https://openreview.net/forum?id=vSjw3bWmyF","excerpt":"1. Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 2017. Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. The concrete distribution: A continuous relaxation of discrete random variables. In International Conference on Learning Representations, 2017. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in GPT. In Advances in Neural Inf","arxiv_match":false},{"id":"90oZpTMT1u","title":"SofT-GRPO: Surpassing Discrete-Token LLM Reinforcement Learning via Gumbel-Reparameterized Soft-Thinking Policy Optimization","forum_url":"https://openreview.net/forum?id=90oZpTMT1u","excerpt":" a deep dive into rl for llm reasoning. arXiv preprint arXiv:2508.08221, 2025c. Michael Luo, Sijun Tan, Justin Wong, Xiaoxiang Shi, William Y. Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica. Deepscaler: Surpassing o1-preview with a 1.5b model by scaling rl, 2025. Notion Blog. Chris J Maddison, Andriy Mnih, and Yee Whye Teh. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712, 2016. Sadegh Mahdavi, Muchen Li, Kaiwen Liu, Renjie Liao, and Christos Thrampoulidis. Beyond accur","arxiv_match":true},{"id":"x6HbrJXR6m","title":"IDG: Intrinsic Assessment and Information-Theoretic Learning for Dynamic Graph Neural Networks","forum_url":"https://openreview.net/forum?id=x6HbrJXR6m","excerpt":"ural networks. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp. 719–728, 2020. Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. The concrete distribution: A continuous relaxation of discrete random variables. In International Conference on Learning Representations, 2017. Alessio Micheli and Domenico Tortorella. Discrete-time dynamic graph echo state networks. Neurocomputing, 496:85–95, 2022. doi: 10.1016","arxiv_match":false}],"id":"1611.00712","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":948},{"title":"Unsupervised dense information retrieval with contrastive learning","arxiv_id":"2112.09118","year":2021,"url":"https://arxiv.org/abs/2112.09118","citing_submissions":107,"explicit_arxiv_matches":43,"examples":[{"id":"Xt9oJoQrHM","title":"Light-DiffRetriever: Budget-Flexible Retrieval with Diffusion Language Models via Matryoshka Representative Tokens","forum_url":"https://openreview.net/forum?id=Xt9oJoQrHM","excerpt":"0.18653/V1/2026.FINDINGS-ACL.676. URL https://dblp.org/rec/conf/acl/HuyVTTVNL26. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retrieval with contrastive learning. Transactions on Machine Learning Research, 2022. URL https://openreview.net/forum?id=jKN1pXi7b0. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and ","arxiv_match":false},{"id":"vvAAVy99dI","title":"Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking","forum_url":"https://openreview.net/forum?id=vvAAVy99dI","excerpt":". https://quoradata.quora.com/ First-Quora-Dataset-Release-Question-Pairs, 2017. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retrieval with contrastive learning. Transactions on Machine Learning Research, 2022. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. Pubmedqa: A dataset for biomedical research question answering. In Proceeding","arxiv_match":false},{"id":"lzZpEiW6aq","title":"Implicit Ranking Leakage in Black-Box RAG: Retrieval Stability-Guided Knowledge Corruption Attacks","forum_url":"https://openreview.net/forum?id=lzZpEiW6aq","excerpt":"boundary analysis of deep learning models. IEEE Transactions on Industrial Informatics, 19(5):6982–6992, 2022. Wassily Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American statistical association, 58(301):13–30, 1963. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118, 2021. Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot","arxiv_match":true}],"id":"2112.09118","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":949},{"title":"When and why vision-language models behave like bags-of-words, and what to do about it?","arxiv_id":"2210.01936","year":2022,"url":"https://arxiv.org/abs/2210.01936","citing_submissions":107,"explicit_arxiv_matches":41,"examples":[{"id":"UcFbcNhyQO","title":"PhysAlign: A Benchmark for Evidence-Grounded Role Alignment in Multimodal Physics Reasoning","forum_url":"https://openreview.net/forum?id=UcFbcNhyQO","excerpt":"tevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9556–9567, 2024. Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. When and why vision-language models behave like bags-of-words, and what to do about it?, 2023. URL https://arxiv.org/abs/2210.01936. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, ","arxiv_match":true},{"id":"DMpcZnLnAC","title":"True Record, Wrong Subject: Diagnosing Visual Memory Misbinding in Personalized Multimodal Models","forum_url":"https://openreview.net/forum?id=DMpcZnLnAC","excerpt":" https: //arxiv.org/abs/2608.16805. Barry Menglong Yao, Sijia Wang, Yu Chen, Qifan Wang, Minqian Liu, Zhiyang Xu, Licheng Yu, and Lifu Huang. Ameli: Enhancing multimodal entity linking with fine-grained attributes, 2023. URL https://arxiv.org/abs/2305.14725. Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. When and why vision-language models behave like bags-of-words, and what to do about it?, 2022. URL https://arxiv.org/abs/2210.01936. Qiuyang Zhan, Rui Zhang, Sheng Guo, Lepeng Zhao, and Zhuotao Liu. When memory becomes authority: B","arxiv_match":true},{"id":"yCQTsqfuf6","title":"Adaptive Global and Fine-Grained Perceptual Fusion for MLLM Embeddings Compatible with Hard Negative Amplification","forum_url":"https://openreview.net/forum?id=yCQTsqfuf6","excerpt":". IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025. Ya-Qi Yu, Minghui Liao, Jihao Wu, Yongxin Liao, Xiaoyu Zheng, and Wei Zeng. TextHawk: Exploring efficient fine-grained perception of multimodal large language models. arXiv preprint arXiv:2404.09204, 2024. Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. When and why vision-language models behave like bags-of-words, and what to do about it? arXiv preprint arXiv:2210.01936, 2022. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for languag","arxiv_match":true}],"id":"2210.01936","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":950},{"title":"Attention Residuals","arxiv_id":"2603.15031","year":2026,"url":"https://arxiv.org/abs/2603.15031","citing_submissions":106,"explicit_arxiv_matches":60,"examples":[{"id":"c4eh9PW3da","title":"SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation","forum_url":"https://openreview.net/forum?id=c4eh9PW3da","excerpt":"s. In CVPR, 2024. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. In ICML, 2020. Kimi Team. Kimi Linear: An Expressive, Efficient Attention Architecture. arXiv preprint arXiv:2510.26692, 2025. Kimi Team. Kimi K3: Open Frontier Intelligence. Moonshot AI technical blog, https://www.kimi.com/blog/kimi-k3, 2026a. Kimi Team. Attention Residuals. arXiv preprint arXiv:2603.15031, 2026b. Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, et al. HunyuanVideo: A Systematic Framework Fo","arxiv_match":true},{"id":"U9lsoJxNae","title":"Beyond Confidence? Auditing Routing Entropy in Attention-Residual Transformers","forum_url":"https://openreview.net/forum?id=U9lsoJxNae","excerpt":" in Neural Information Processing Systems, 31, 2018. Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C Mozer, and Becca Roelofs. Soft calibration objectives for neural networks. Advances in Neural Information Processing Systems, 34:29768–29779, 2021. Kimi Team, Guangyu Chen, Yu Zhang, Jianlin Su, Weixin Xu, Siyuan Pan, Yaoyu Wang, Yucheng Wang, Guanduo Chen, Bohong Yin, et al. Attention residuals. arXiv preprint arXiv:2603.15031, 2026. Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth Malik, and Yarin Gal. Sema","arxiv_match":true},{"id":"1wsVNB7eBv","title":"WHEN ROUTING PROBES IMPROVE WITHOUT NEW INFORMATION: AUDITING UNCERTAINTY BEYOND MODEL OUTPUTS","forum_url":"https://openreview.net/forum?id=1wsVNB7eBv","excerpt":"it Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, and Becca Roelofs. Soft calibration objectives for neural networks. In Advances in Neural Information Processing Systems, volume 34, pp. 29768– 29779, 2021. URL https://proceedings.neurips.cc/paper/2021/hash/ f8905bd3df64ace64a68e154ba72f24c-Abstract.html. Kimi Team et al. Attention residuals. arXiv preprint arXiv:2603.15031, 2026. URL https: //arxiv.org/abs/2603.15031. S. I. Lee, D. Shin, and J. Park. Unseen data detection using routing entropy in mixture-of-experts","arxiv_match":true}],"id":"2603.15031","metadata_source":"Cached arXiv API metadata","rank":951},{"title":"Bridging language and items for retrieval and recommendation","arxiv_id":"2403.03952","year":2024,"url":"https://arxiv.org/abs/2403.03952","citing_submissions":106,"explicit_arxiv_matches":59,"examples":[{"id":"R80bVPjG4H","title":"TDE: Target-conditioned Decision Evidence for Generative Recommendation","forum_url":"https://openreview.net/forum?id=R80bVPjG4H","excerpt":"mmendation. In Proceedings of the 42nd International Conference on Machine Learning, ICML’25. JMLR.org, 2025. Yupeng Hou, Jiacheng Li, Xiangjun Fu, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley. Bridging language and items for retrieval and recommendation: Benchmarking llms as semantic encoders. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3251–3265, 2026. Bowen Jin, Hansi Zeng","arxiv_match":false},{"id":"mUKHYzi78E","title":"Your Best Retriever May Not Be Best for Your Reranker: Adaptive Coordination for LLM Recommendation Cascades","forum_url":"https://openreview.net/forum?id=mUKHYzi78E","excerpt":" A Konstan. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015. Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939, 2015. Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley. Bridging language and items for retrieval and recommendation. arXiv preprint arXiv:2403.03952, 2024. Junjie Huang, Jiarui Qin, Jianghao Lin, Ziming Feng, Weinan Zhang, and Yong Yu. Unleashing t","arxiv_match":true},{"id":"2iwSiIUJtP","title":"Don't Let Conflicting Rewards Pull Generative Recommendations Off Target","forum_url":"https://openreview.net/forum?id=2iwSiIUJtP","excerpt":"eiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015. Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley. Bridging language and items for retrieval and recommendation. arXiv preprint arXiv:2403.03952, 2024. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang,","arxiv_match":true}],"id":"2403.03952","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":952},{"title":"Clevrer: Collision events for video representation and reasoning","arxiv_id":"1910.01442","year":2019,"url":"https://arxiv.org/abs/1910.01442","citing_submissions":106,"explicit_arxiv_matches":40,"examples":[{"id":"ePgOqXilZ8","title":"SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models","forum_url":"https://openreview.net/forum?id=ePgOqXilZ8","excerpt":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12–22, 2023. Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B. Tenenbaum. CLEVRER: Collision events for video representation and reasoning. In International Conference on Learning Representations, 2020. Jiahui Zhang, Yurui Chen, Yueming Xu, Ze Huang, Jilin Mei, Chunhui Chen, Yanpeng Zhou, Yu-Jie Yuan, Xinyue Cai, Guowei Huang, Xingyue Q","arxiv_match":false},{"id":"LZZ8HTQFcE","title":"Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics","forum_url":"https://openreview.net/forum?id=LZZ8HTQFcE","excerpt":"ideo chapters at scale. Advances in Neural Information Processing Systems, 36:49428–49444, 2023. Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B. Tenenbaum. CLEVRER: collision events for video representation and reasoning. In ICLR, 2020. Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynetqa: A dataset for understanding complex web videos via question answering. In Proceedings o","arxiv_match":false},{"id":"RYAAeHLU5G","title":"Backward Latent Certainty: When More Certain Forecasts Lie Farther","forum_url":"https://openreview.net/forum?id=RYAAeHLU5G","excerpt":"n with object-centric models. arXiv preprint arXiv:2210.05861, 2022. Ziyi Wu, Jingyu Hu, Wuyue Lu, Igor Gilitschenski, and Animesh Garg. Slotdiffusion: Objectcentric generative modeling with diffusion models. Advances in Neural Information Processing Systems, 36:50932–50958, 2023. Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum. CLEVRER: Collision events for video representation and reasoning. arXiv preprint arXiv:1910.01442, 2019. Andrii Zadaianchuk, Maximilian Seitzer, and Georg Martius. Object-centric learning for real-","arxiv_match":true}],"id":"1910.01442","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":953},{"title":"Deformable detr: Deformable transformers for end-to-end object detection","arxiv_id":"2010.04159","year":2020,"url":"https://arxiv.org/abs/2010.04159","citing_submissions":106,"explicit_arxiv_matches":34,"examples":[{"id":"BsS6OGsjc6","title":"CamoWorld: Towards Image World Modeling for Camouflaged Object Understanding","forum_url":"https://openreview.net/forum?id=BsS6OGsjc6","excerpt":"review.net/forum?id=ydopy-e6Dg. Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. UNet++: A nested U-Net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11. Springer, 2018. Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable DETR: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159, 2020. A ","arxiv_match":true},{"id":"CquxtFcjgG","title":"Reasoning-DETR: Contextual and Relational Query Learning for Aerial Object Detection","forum_url":"https://openreview.net/forum?id=CquxtFcjgG","excerpt":"f the IEEE/CVF International Conference on Computer Vision Workshops, pp. 2778–2788, 2021a. doi: 10.1109/ICCVW54120.2021.00312. Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. In International Conference on Learning Representations, 2021b. Algorithm 1 Aerial Object Detection with Reasoning-DETR Input: training image and annotations (I, Y ); base-query number Kb ; maximum ","arxiv_match":false},{"id":"GLBYxBmu52","title":"Deformable Vision Query Connector: Letting LLMs Query Vision Information through Position-Grounded Lookup","forum_url":"https://openreview.net/forum?id=GLBYxBmu52","excerpt":"ms, volume 37, pp. 90009–90029. Curran Associates, Inc., 2024. doi: 10.52202/079017-2858. URL https://proceedings.neurips.cc/paper_files/paper/2024/hash/ a3eadeebbc9eecd621086f6978865a85-Abstract-Conference.html. Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable DETR: Deformable Transformers for End-to-End Object Detection. In ICLR, March 2021. doi: 10.48550/ arXiv.2010.04159. URL http://arxiv.org/abs/2010.04159. arXiv:2010.04159 [cs.CV]. A ","arxiv_match":true}],"id":"2010.04159","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":954},{"title":"G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems","arxiv_id":"2506.07398","year":2025,"url":"https://arxiv.org/abs/2506.07398","citing_submissions":106,"explicit_arxiv_matches":35,"examples":[{"id":"CpeeKoACAd","title":"EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory","forum_url":"https://openreview.net/forum?id=CpeeKoACAd","excerpt":"aniel B Ospina, and Simon Suo. Parsebench: A document parsing benchmark for ai agents. arXiv preprint arXiv:2604.08538, 2026a. Guibin Zhang, Muxin Fu, Kun Wang, Frank Wan, Miao Yu, and Shuicheng Yan. G-memory: Tracing hierarchical memory for multi-agent systems. Advances in Neural Information Processing Systems, 38:12988–13018, 2026b. Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, and Weiming Lu. A","arxiv_match":false},{"id":"BJlDHn1L8T","title":"AECG: Asymmetric Experience Consolidation and Governance In Multi-Agent Systems","forum_url":"https://openreview.net/forum?id=BJlDHn1L8T","excerpt":"nqi Li, and Hao Peng. Useful memories become faulty when continuously updated by llms. arXiv preprint arXiv:2605.12978, 2026a. Guibin Zhang, Muxin Fu, Kun Wang, Frank Wan, Miao Yu, and Shuicheng Yan. G-memory: Tracing hierarchical memory for multi-agent systems. Advances in Neural Information Processing Systems, 38:12988–13018, 2026b. Wen Zhang, Lingfei Deng, Lei Zhang, and Dongrui Wu. A survey on negative transfer. IEEE/CAA Journal of Automatica Sinica, 10","arxiv_match":false},{"id":"jfq0Rcq97f","title":"Learning Role-Context Graph Policies for Adaptive Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=jfq0Rcq97f","excerpt":"-agent llm collaboration. In The International Conference on Learning Representations, volume 2026, pp. 19745–19760, 2026. Guibin Zhang, Muxin Fu, Kun Wang, Guancheng Wan, Miao Yu, and Shuicheng YAN. G-memory: Tracing hierarchical memory for multi-agent systems. In The Annual Conference on Neural Information Processing Systems, 2025a. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, LEI BAI, and Xiang Wang. Multi-agent architecture search via agentic super","arxiv_match":false}],"id":"2506.07398","metadata_source":"Cached arXiv API metadata","rank":955},{"title":"Improving Video Generation with Human Feedback","arxiv_id":"2501.13918","year":2025,"url":"https://arxiv.org/abs/2501.13918","citing_submissions":106,"explicit_arxiv_matches":43,"examples":[{"id":"NOBzyFqdOi","title":"From Preference to Flow: Turning Pairwise Preferences into Local Flow Supervision","forum_url":"https://openreview.net/forum?id=NOBzyFqdOi","excerpt":"ence optimization for text-to-video diffusion. ArXiv preprint, 2026. Jie Liu, Gongye Liu, Jiajun Liang, Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Menghan Xia, Xintao Wang, et al. Improving video generation with human feedback. In Neural Information Processing Systems, 2025a. Runtao Liu, Haoyu Wu, Ziqiang Zheng, Chen Wei, Yingqing He, Renjie Pi, and Qifeng Chen. Videodpo: Omni-preference alignment for video diffusion gener","arxiv_match":false},{"id":"0XEgr94mKk","title":"ACT: Agentic Text–Scene Co-Design and Dual Evolution for Informative Video Generation","forum_url":"https://openreview.net/forum?id=0XEgr94mKk","excerpt":"hing for generative modeling. arXiv preprint arXiv:2210.02747, 2022. Jie Liu, Gongye Liu, Jiajun Liang, Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Menghan Xia, Xintao Wang, et al. Improving video generation with human feedback. Advances in Neural Information Processing Systems, 38:82155–82192, 2026a. Junle Liu, Peirong Zhang, Yuyi Zhang, Pengyu Yan, Hui Zhou, Xinyue Zhou, Fengjun Guo, and Lianwen Jin. Posterverse: A full-w","arxiv_match":false},{"id":"tKBF2XBXVx","title":"SyncReward: Human-Aligned Synchronization Reward for Audio-Visual Generation","forum_url":"https://openreview.net/forum?id=tKBF2XBXVx","excerpt":" December 5, 2025, 2025b. URL http://papers.nips.cc/paper_files/paper/2025/hash/ 76227feb18ea0ee40bd15cf02c33e18e-Abstract-Conference.html. Jie Liu, Gongye Liu, Jiajun Liang, Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Menghan Xia, Xintao Wang, Xiaohong Liu, Fei Yang, Pengfei Wan, Di Zhang, Kun Gai, Yujiu Yang, and Wanli Ouyang. Improving video generation with human feedback. arXiv preprint arXiv:2501.13918, 2025c. URL https://arxiv.org/abs/2501.13918. Kai Liu, Wei Li, Lai Chen, Shengqiong Wu, Yanhao Zheng, Jiayi Ji, Fan Zhou, Rongxin Jiang, Jiebo L","arxiv_match":true}],"id":"2501.13918","metadata_source":"Cached arXiv API metadata","rank":956},{"title":"Long context transfer from language to vision","arxiv_id":"2406.16852","year":2024,"url":"https://arxiv.org/abs/2406.16852","citing_submissions":106,"explicit_arxiv_matches":78,"examples":[{"id":"LoTAR1MOVN","title":"Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs","forum_url":"https://openreview.net/forum?id=LoTAR1MOVN","excerpt":":2304.14178, 2023. Haoji Zhang, Xin Gu, Jiawen Li, Chixiang Ma, Sule Bai, Chubin Zhang, Bowen Zhang, Zhichao Zhou, Dongliang He, and Yansong Tang. Thinking with videos: Multimodal tool-augmented reinforcement learning for long video reasoning. arXiv:2508.04416, 2025a. Peiyuan Zhang, Kaichen Zhang, Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, and Ziwei Liu. Long context transfer from language to vision. arXiv:2406.16852, 2024a. Shaojie Zhang, Jiahui Yang, Jianqin Yin, Zhenbo Luo, and Jian Luan. Q-frame: Query-aware fr","arxiv_match":true},{"id":"9XqqxYouLj","title":"Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning","forum_url":"https://openreview.net/forum?id=9XqqxYouLj","excerpt":"eo multi-hop reasoning. arXiv preprint arXiv:2601.23224, 2026. 14 Peiyuan Zhang, Kaichen Zhang, Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, and Ziwei Liu. Long context transfer from language to vision. Trans. Mach. Learn. Res., 2024. 6 Xiaofeng Zhang, Yihao Quan, Chen Shen, Xiaosong Yuan, Shaotian Yan, Liang Xie, Wenxiao Wang, Chaochen Gu, Hao Tang, and Jieping Ye. From redundancy to relevance: In","arxiv_match":false},{"id":"r2ccgSQzyH","title":"Supervising the Visible State: Separating Representation from View Selection in Streaming Video","forum_url":"https://openreview.net/forum?id=r2ccgSQzyH","excerpt":"asoning with short videos. arXiv preprint arXiv:2410.02763, 2024. Peiyuan Zhang, Kaichen Zhang, Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, and Ziwei Liu. Long context transfer from language to vision. Transactions on Machine Learning Research, 2025b. Yaolun Zhang, Ruohui Wang, Jiahao Wang, Yepeng Tang, Xuanyu Zheng, Haonan Duan, Hao Lu, Hanming Deng, and Lewei Lu. EVA: Efficient reinforcement lea","arxiv_match":false}],"id":"2406.16852","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":957},{"title":"Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model","arxiv_id":"2503.24290","year":2025,"url":"https://arxiv.org/abs/2503.24290","citing_submissions":106,"explicit_arxiv_matches":57,"examples":[{"id":"BTTuPGcoJL","title":"Learning from Correctness Trends: Sample-Wise Historical Advantage Shaping for Mathematical Reasoning","forum_url":"https://openreview.net/forum?id=BTTuPGcoJL","excerpt":" collapse in group relative policy optimization: Diagnosis and mitigation. arXiv preprint arXiv:2605.21125, 2026. Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model. Advances in Neural Information Processing Systems, 38:162239–162262, 2026. Ke Hu, Zhehuai Chen, Chao-Han Huck Yang, Piotr Żelasko, Oleksii Hrinchuk, Vitaly Lavrukhin, Jagadeesh Balam, and Boris Gin","arxiv_match":false},{"id":"1TfY9hAQoJ","title":"Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening","forum_url":"https://openreview.net/forum?id=1TfY9hAQoJ","excerpt":".11902. Zhenyu Hou, Yujiang Li, Jie Tang, and Yuxiao Dong. Single-rollout asynchronous optimization for agentic reinforcement learning. arXiv preprint arXiv:2607.07508, 2026. URL https: //arxiv.org/abs/2607.07508. Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. OpenReasoner-Zero: An open source approach to scaling up reinforcement learning on the base model. arXiv preprint arXiv:2503.24290, 2025. URL https://arxiv.org/abs/2503.24290. Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger, and Sepp Hochreiter. RREDCoT: Segment-le","arxiv_match":true},{"id":"FxHYVRmaCJ","title":"Rethinking Probability-Based Reinforcement Learning From Posterior Concentration","forum_url":"https://openreview.net/forum?id=FxHYVRmaCJ","excerpt":"well Forbes, and Yejin Choi. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751, 2019. Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model. Advances in Neural Information Processing Systems, 38:162239–162262, 2026. Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. How can we know when language models know? on the calibration of ","arxiv_match":false}],"id":"2503.24290","metadata_source":"Cached arXiv API metadata","rank":958},{"title":"Progressive growing of gans for improved quality, stability, and variation","arxiv_id":"1710.10196","year":2017,"url":"https://arxiv.org/abs/1710.10196","citing_submissions":106,"explicit_arxiv_matches":34,"examples":[{"id":"XS1t7JP5FD","title":"Hidden in Plain Sight: Recovering Latent Generative Diversity in Visual Autoregressive Models","forum_url":"https://openreview.net/forum?id=XS1t7JP5FD","excerpt":"Conference on Computer Vision and Pattern Recognition, 2023d. Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak, Jaesik Park, Eli Shechtman, Jun-Yan Zhu, and Taesung Park. Distilling diffusion models into conditional gans. In European Conference on Computer Vision, pp. 428–447. Springer, 2024. Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Improved p","arxiv_match":true},{"id":"RafiZ0ikvb","title":"IVT-Guard: All-in-One Reasoning Model for AI-Generated Content Detection","forum_url":"https://openreview.net/forum?id=RafiZ0ikvb","excerpt":"lin Feng, Baichuan Zhou, Bin Wang, Dahua Lin, Linfeng Zhang, et al. Legion: Learning to ground and explain for synthetic image detection. In ICCV, 2025. Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scaling up gans for text-to-image synthesis. In CVPR, 2023. Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generativ","arxiv_match":true},{"id":"joqZYFW6kS","title":"Understanding and Improving Diffusion Training via Cross-Timestep Task Affinity","forum_url":"https://openreview.net/forum?id=joqZYFW6kS","excerpt":"enko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022. Adrián Javaloy and Isabel Valera. Rotograd: Gradient homogenization in multitask learning. arXiv preprint arXiv:2103.02631, 2021. Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generativ","arxiv_match":true}],"id":"1710.10196","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":959},{"title":"Self-Forcing++: Towards Minute-Scale High-Quality Video Generation","arxiv_id":"2510.02283","year":2025,"url":"https://arxiv.org/abs/2510.02283","citing_submissions":106,"explicit_arxiv_matches":49,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"stent autoregressive video generation with long context. arXiv preprint arXiv:2602.06028, 2026. Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, and ChoJui Hsieh. Self-forcing++: Towards minute-scale high-quality video generation. In International Conference on Learning Representations, 2026. Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning. Transactions on Machine Learni","arxiv_match":false},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"sz Kaiser, et al. Rethinking attention with performers. arXiv preprint arXiv:2009.14794, 2020. Jiaxing Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, and ChoJui Hsieh. Self-forcing++: Towards minute-scale high-quality video generation. In International Conference on Learning Representations, volume 2026, pp. 85802–85822, 2026. Google DeepMind. Veo 3.1. https://deepmind.google/models/veo/, 2025. Yuwei Guo, Ceyuan Yang, Anyi Rao, Zh","arxiv_match":false},{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":"er animation and replacement with holistic replication. arXiv preprint arXiv:2509.14055, 2025. Jiaxing Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, and ChoJui Hsieh. Self-forcing++: Towards minute-scale high-quality video generation. In International Conference on Learning Representations, volume 2026, pp. 85802–85822, 2026. Tri Dao. Flashattention-2: Faster attention with better parallelism and work partitioning. In Internation","arxiv_match":false}],"id":"2510.02283","metadata_source":"Cached arXiv API metadata","rank":960},{"title":"minif2f: A cross-system benchmark for formal olympiad-level mathematics","arxiv_id":"2109.00110","year":2021,"url":"https://arxiv.org/abs/2109.00110","citing_submissions":106,"explicit_arxiv_matches":46,"examples":[{"id":"F6XC1gfjyT","title":"Rethinking Supervision Granularity: Segment-Level Learning for LLM-Based Theorem Proving","forum_url":"https://openreview.net/forum?id=F6XC1gfjyT","excerpt":" with retrieval-augmented language models. Advances in Neural Information Processing Systems, 36:21573–21612, 2023. Huaiyuan Ying, Zijian Wu, Yihan Geng, Jiayu Wang, Dahua Lin, and Kai Chen. Lean workbook: A large-scale lean problem set formalized from natural language math problems. arXiv preprint arXiv:2406.03847, 2024. Kunhao Zheng, Jesse Michael Han, and Stanislas Polu. miniF2F: a cross-system benchmark for formal olympiad-level mathematics. arXiv preprint arXiv:2109.00110, 2021. A ","arxiv_match":true},{"id":"ql0b3IwNyT","title":"LLM-based Automated Theorem Proving Hinges on Scalable Synthetic Data Generation","forum_url":"https://openreview.net/forum?id=ql0b3IwNyT","excerpt":", Prayag Tiwari, and Benyou Wang. Natural language reasoning, a survey. ACM Computing Surveys, 56(12):1–39, 2024. Jingyuan Zhang, Qi Wang, Xingguang Ji, Yahui Liu, Yang Yue, Fuzheng Zhang, Di Zhang, Guorui Zhou, and Kun Gai. Leanabell-prover: Posttraining scaling in formal reasoning. arXiv preprint arXiv:2504.06122, 2025. Kunhao Zheng, Jesse Michael Han, and Stanislas Polu. Minif2f: a cross-system benchmark for formal olympiad-level mathematics. arXiv preprint arXiv:2109.00110, 2021. A ","arxiv_match":true},{"id":"v9ewIpjd1F","title":"Monotonic Reference-Free Refinement for Autoformalization","forum_url":"https://openreview.net/forum?id=v9ewIpjd1F","excerpt":"rem: Advancing llm reasoning for theorem proving through natural language and reinforcement learning, 2025e. URL https://arxiv.org/abs/2505.23754. Kunhao Zheng, Jesse Michael Han, and Stanislas Polu. minif2f: a cross-system benchmark for formal olympiad-level mathematics. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=9ZPegFuFTFv. A P ROOF OF T HEOREM 2.2 Proof. Recall: wi πi (x) i∈Dsoft X JOA (x) = πFV (x) LCBδ (x)","arxiv_match":false}],"id":"2109.00110","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":961},{"title":"Alignment faking in large language models","arxiv_id":"2412.14093","year":2024,"url":"https://arxiv.org/abs/2412.14093","citing_submissions":105,"explicit_arxiv_matches":98,"examples":[{"id":"FtpegwsLFx","title":"Algorithmic Monopsony: Reasoning-Mode Wage Suppression in LLM Agents","forum_url":"https://openreview.net/forum?id=FtpegwsLFx","excerpt":". ISSN 0034-6527. doi: 10.2307/2296617. URL https://doi.org/10. 2307/2296617. Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian Michael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, and Evan Hubinger. Alignment faking in large language models, 2024. URL https://arxiv.org/abs/2412.14093. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, ","arxiv_match":true},{"id":"yVGzyZ8zMc","title":"Offline Scores Overestimate Online Oversight: Timely Interception of LLM Agents with Causal-Cone Monitoring","forum_url":"https://openreview.net/forum?id=yVGzyZ8zMc","excerpt":"gle DeepMind, arXiv tech report. Nicholas Goldowsky-Dill, Bilal Chughtai, Stefan Heimersheim, and Marius Hobbhahn. Detecting Strategic Deception Using Linear Probes, 2025. URL https://arxiv.org/abs/2502. 03407. Apollo Research. Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, et al. Alignment Faking in Large Language Models, 2024a. URL https://arxiv.org/abs/2412.14093. Anthropic/Redwood Research. Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan, and Fabien Roger. AI ","arxiv_match":true},{"id":"DStFCYLdVC","title":"LLMs Can Unlearn Refusal with Only 1,000 Benign Samples","forum_url":"https://openreview.net/forum?id=DStFCYLdVC","excerpt":" Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian Michael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, and Evan Hubinger. Alignment faking in large language models. CoRR, abs/2412.14093, 2024. Melody Y. Guan, Manas Joglekar, Eric Wallace, Saachi Jain, Boaz Barak, Alec Helyar, Rachel Dias, Andrea Vallone, Hongyu Ren, Jason Wei, Hyung Won Chung, Sam Toyer, Johann","arxiv_match":false}],"id":"2412.14093","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":962},{"title":"EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video","arxiv_id":"2505.11709","year":2025,"url":"https://arxiv.org/abs/2505.11709","citing_submissions":105,"explicit_arxiv_matches":33,"examples":[{"id":"cpwyesJlAJ","title":"InfiniHand: Streaming World-Space Hand Motion Estimation from Egocentric Video","forum_url":"https://openreview.net/forum?id=cpwyesJlAJ","excerpt":"ition, 2020. Shreyas Hampali, Sayan Deb Sarkar, Mahdi Rad, and Vincent Lepetit. Keypoint transformer: Solving joint identification in challenging hands and object interactions for accurate 3d pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. Ryan Hoque, Peide Huang, David J. Yoon, Mouli Sivapurapu, and Jian Zhang. EgoDex: Learning dexterous manipulation from large-scale egocentric video. arXiv preprint arXiv:2505.11709, 2025. Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Kaixuan Wang, Hao Chen, Gang Yu, Chun","arxiv_match":true},{"id":"gb498OmydX","title":"Physis-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution","forum_url":"https://openreview.net/forum?id=gb498OmydX","excerpt":"ndy Huang, and Dhruv Shah. Grounding robot generalization in training data via retrieval-augmented vlms. arXiv preprint arXiv:2603.11426, 2026. Ryan Hoque, Peide Huang, David Yoon, Jian Zhang, et al. Egodex: Learning dexterous manipulation from large-scale egocentric video. In International Conference on Learning Representations, volume 2026, pp. 4218–4237, 2026. Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Sor","arxiv_match":false},{"id":"YHLCbk9cpy","title":"RxBrain: Embodied Foundation Model with Joint Language-Visual Subgoal Planning","forum_url":"https://openreview.net/forum?id=YHLCbk9cpy","excerpt":" the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13869–13920, 2025a. Yanjiang Guo, Lucy Xiaoyang Shi, Jianyu Chen, and Chelsea Finn. Ctrl-world: A controllable generative world model for robot manipulation. arXiv preprint arXiv:2510.10125, 2025b. Ryan Hoque, Peide Huang, David J. Yoon, Mouli Sivapurapu, and Jian Zhang. EgoDex: Learning dexterous manipulation from large-scale egocentric video. arXiv preprint arXiv:2505.11709, 2025. Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sr","arxiv_match":true}],"id":"2505.11709","metadata_source":"Cached arXiv API metadata","rank":963},{"title":"GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks","arxiv_id":"2510.04374","year":2025,"url":"https://arxiv.org/abs/2510.04374","citing_submissions":105,"explicit_arxiv_matches":51,"examples":[{"id":"BdXmcg59t4","title":"WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution","forum_url":"https://openreview.net/forum?id=BdXmcg59t4","excerpt":"026. URL https://arxiv.org/abs/2607.28074. Tejal Patwardhan, Rachel Dias, Elizabeth Proehl, Grace Kim, Michele Wang, Olivia Watkins, Simón Posada Fishman, Marwan Aljubeh, Phoebe Thacker, Laurance Fauconnet, Natalie S. Kim, Patrick Chao, Samuel Miserendino, Gildas Chabot, David Li, Michael Sharman, Alexandra Barr, Amelia Glaese, and Jerry Tworek. GDPval: Evaluating AI model performance on real-world economically valuable tasks, 2025. URL https://arxiv.org/abs/2510.04374. Jiangweizhi Peng, Yuanxin Liu, Ruida Zhou, Charles Fleming, Zhaoran Wang, Alfredo Garcia, and Ming","arxiv_match":true},{"id":"1YacpejHL6","title":"miniFlux: Exploring Agent-Directed Harness Evolution for Online Adaptation","forum_url":"https://openreview.net/forum?id=1YacpejHL6","excerpt":"DS/OpenSpace. Accessed: 2026-09-18. Tejal Patwardhan, Rachel Dias, Elizabeth Proehl, Grace Kim, Michele Wang, Olivia Watkins, Simon Fishman, Marwan Aljubeh, Phoebe Thacker, Laurance Fauconnet, et al. Gdpval: Evaluating ai model performance on real-world economically valuable tasks. In International Conference on Learning Representations, volume 2026, pp. 24005–24040, 2026. Prithvi Rajasekaran. Harness design for long-running application development, March 2026. URL https://www","arxiv_match":false},{"id":"GMbvkyJBaz","title":"Agentic-WorkBench: Evaluating Multimodal Agents on Complex Visual Workflows","forum_url":"https://openreview.net/forum?id=GMbvkyJBaz","excerpt":"shman, Marwan Aljubeh, Phoebe Thacker, Laurance Fauconnet, Natalie S. Kim, Patrick Chao, Samuel Miserendino, Gildas Chabot, David Li, Michael Sharman, Alexandra Barr, Amelia Glaese, and Jerry Tworek. Gdpval: Evaluating ai model performance on real-world economically valuable tasks. ArXiv, abs/2510.04374, 2025. URL https://api. semanticscholar.org/CorpusID:281843768. QwenTeam. Qwen3.8-max: A new bar for coding and cowork, 2026. URL https://qwen.ai/ blog?id=qwen3.8. Dan Shepard ","arxiv_match":false}],"id":"2510.04374","metadata_source":"Cached arXiv API metadata","rank":964},{"title":"Irasim: A fine-grained world model for robot manipulation","arxiv_id":"2406.14540","year":2024,"url":"https://arxiv.org/abs/2406.14540","citing_submissions":105,"explicit_arxiv_matches":36,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"botics: Science and Systems, 2025a. doi: 10.15607/RSS.2025.XXI.015. URL https://www.roboticsproceedings.org/rss21/p015.html. Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam Cheang, and Tao Kong. IRASim: A Fine-Grained World Model for Robot Manipulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9834–9844, 2025b. URL https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_IRASim_ A_Fine-Grained_World_Mode","arxiv_match":false},{"id":"3nSV2brMqb","title":"PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives","forum_url":"https://openreview.net/forum?id=3nSV2brMqb","excerpt":"ractive video world model evaluation. arXiv preprint arXiv:2605.25874, 2026. Haoyi Zhu, Haozhe Liu, Yuyang Zhao, Tian Ye, Junsong Chen, Jincheng Yu, Tong He, Song Han, and Enze Xie. SANA-WM: Efficient minute-scale world modeling with hybrid linear diffusion transformer, 2026a. URL https://arxiv.org/abs/2605.15178. Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam Cheang, and Tao Kong. IRASim: A fine-grained world model for robot manipulation. arXiv preprint arXiv:2406.14540, 2024. Yixuan Zhu, Jiaqi Feng, Wenzhao Zheng, Yuan Gao, Xin Tao, Pengfei Wan, Jie Zhou, and Jiwen L","arxiv_match":true},{"id":"xE1Rd5Luma","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","forum_url":"https://openreview.net/forum?id=xE1Rd5Luma","excerpt":"dels: Coupling video and action diffusion for pretraining on large robotic datasets. arXiv preprint arXiv:2504.02792, 2025a. Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam Cheang, and Tao Kong. Irasim: A finegrained world model for robot manipulation. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9834–9844. IEEE, 2025b. A ","arxiv_match":false}],"id":"2406.14540","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":965},{"title":"On the measure of intelligence","arxiv_id":"1911.01547","year":2019,"url":"https://arxiv.org/abs/1911.01547","citing_submissions":105,"explicit_arxiv_matches":96,"examples":[{"id":"wm97mtaTWL","title":"SolveEdit: Benchmarking Visual Problem Solving in Generative Models","forum_url":"https://openreview.net/forum?id=wm97mtaTWL","excerpt":"185–24198, 2023. URL https://api.semanticscholar.org/CorpusID:266521410. Jaemin Cho, Yushi Hu, Jason Baldridge, Roopal Garg, Peter Anderson, Ranjay Krishna, Mohit Bansal, Jordi Pont-Tuset, and Su Wang. Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-to-image generation. In International conference on learning representations, volume 2024, pp. 15625–15645, 2024. François Chollet. On the measure of intelligence. arXiv preprint arXiv:1911.01547, 2019. En Ci, Shanyan Guan, Yanhao Ge, Yilin Zhang, Wei Li, Zhenyu Zhang, Jian Yang, and Ying Tai. ","arxiv_match":true},{"id":"KTMlKsLZV2","title":"SimLife: Pattern Understanding for Long-Horizon Human-Agent Partnership","forum_url":"https://openreview.net/forum?id=KTMlKsLZV2","excerpt":"_1950_078_0001_vofeit_2_0_co_2.xml. Guo Chen, Lidong Lu, Yicheng Liu, Liangrui Dong, Lidong Zou, Jixin Lv, Zhenquan Li, Xinyi Mao, Baoqi Pei, Shihao Wang, Zhiqi Li, Karan Sapra, Fuxiao Liu, Yin-Dong Zheng, Yifei Huang, Limin Wang, Zhiding Yu, Andrew Tao, Guilin Liu, and Tong Lu. Towards multimodal lifelong understanding: A dataset and agentic baseline, 2026. URL https://arxiv.org/abs/2603. 05484. François Chollet. On the measure of intelligence. arXiv preprint arXiv:1911.01547, 2019. Francois Chollet, Mike Knoop, Gregory Kamradt, Bryan Landers, and Henry Pinkard. ARC-AGI-2: ","arxiv_match":true},{"id":"dVbEJobpzS","title":"VideoCogQA: A Controllable Benchmark for Cognitive Abilities in Video-Language Models","forum_url":"https://openreview.net/forum?id=dVbEJobpzS","excerpt":"rl to long videos. Advances in Neural Information Processing Systems, 38:172842–172870, 2026. Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, and Lidong Bing. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476, 2024. URL https://arxiv.org/abs/2406.07476. François Chollet. On the measure of intelligence. arXiv preprint arXiv:1911.01547, 2019. Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Haotian Wang, Ming Liu, and Bing Qin","arxiv_match":true}],"id":"1911.01547","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":966},{"title":"AIDE: AI-Driven Exploration in the Space of Code","arxiv_id":"2502.13138","year":2025,"url":"https://arxiv.org/abs/2502.13138","citing_submissions":104,"explicit_arxiv_matches":81,"examples":[{"id":"aDHQ65QPxg","title":"How Can LLM Agents Become Wireless Communication Algorithm Engineers?","forum_url":"https://openreview.net/forum?id=aDHQ65QPxg","excerpt":"t arXiv:2601.06640, 2026. doi: 10.48550/arXiv.2601.06640. URL https://arxiv.org/abs/2601.06640. Zhengyao Jiang, Dominik Schmidt, Dhruv Srikanth, Dixing Xu, Ian Kaplan, Deniss Jacenko, and Yuxiang Wu. AIDE: AI-driven exploration in the space of code, 2025. URL https://arxiv. org/abs/2502.13138. Andrej Karpathy. autoresearch: Ai agents running research on single-gpu nanochat training automatically. https://github.com/karpathy/autoresearch, 2026. ","arxiv_match":false},{"id":"PzlJfjWklS","title":"Caelum: Self-Evolving Compilation of Forward–Inverse CAE Optimizers","forum_url":"https://openreview.net/forum?id=PzlJfjWklS","excerpt":"nzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren (eds.). Automated machine learning: Methods, systems, challenges. Springer, 2019. Zhengyao Jiang, Dominik Schmidt, Dhruv Srikanth, Dixing Xu, Ian Kaplan, Deniss Jacenko, and Yuxiang Wu. AIDE: AI-driven exploration in the space of code. arXiv preprint arXiv:2502.13138, 2025. Sayash Kapoor and Arvind Narayanan. Leakage and the reproducibility crisis in machine-learni","arxiv_match":true},{"id":"QIfIKZ1qGX","title":"CAES: Cost-Aware Experiment Selection via Online Shadow Prices for Autonomous Research","forum_url":"https://openreview.net/forum?id=QIfIKZ1qGX","excerpt":"earning experimentation. In Proceedings of the 41st International Conference on Machine Learning (ICML), volume 235 of Proceedings of Machine Learning Research, pp. 20271–20309. PMLR, 2024. URL https://proceedings.mlr.press/ v235/huang24y.html. Zhengyao Jiang, Dominik Schmidt, Dhruv Srikanth, Dixing Xu, Ian Kaplan, Deniss Jacenko, and Yuxiang Wu. AIDE: AI-driven exploration in the space of code. arXiv preprint arXiv:2502.13138, 2025. URL https://arxiv.org/abs/2502.13138. Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, and Barnabás Póczos. Multi-fidelity Bay","arxiv_match":true}],"id":"2502.13138","metadata_source":"Cached arXiv API metadata","rank":967},{"title":"Kimi-VL Technical Report","arxiv_id":"2504.07491","year":2025,"url":"https://arxiv.org/abs/2504.07491","citing_submissions":104,"explicit_arxiv_matches":94,"examples":[{"id":"PxFICAgn1X","title":"Omnimodal Dataset Distillation via High-Order Proxy Alignment","forum_url":"https://openreview.net/forum?id=PxFICAgn1X","excerpt":"multimodal models, 2023. arXiv preprint arXiv:2312.11805. Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune. Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data. In ICML, pp. 9206–9216, 2020. Kimi Team, Angang Du, Bohong Yin, Bowei Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chenzhuang Du, Chu Wei, et al. Kimi-vl technical report, 2025. arXiv preprint arXiv:2504.07491. Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao","arxiv_match":true},{"id":"aETfL2eyX3","title":"ContextRL: Addressing Policy Information Bottlenecks with Context-Augmented RL","forum_url":"https://openreview.net/forum?id=aETfL2eyX3","excerpt":"Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. A diagram is worth a dozen images. arXiv preprint arXiv:1603.07396, 2016. Kimi Team, Angang Du, Bohong Yin, Bowei Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chenzhuang Du, Chu Wei, et al. Kimi-VL technical report. arXiv preprint arXiv:2504.07491, 2025. Kwai Keye Team. Kwai keye-vl 1.5 technical report. arXiv preprint arXiv:2509.01563, 2025. Kw","arxiv_match":true},{"id":"VHnY0nzXxv","title":"Co-Evolving Skills, Memory, and Policy for Personalized Research Automation","forum_url":"https://openreview.net/forum?id=VHnY0nzXxv","excerpt":"8705, 2025. InternAgent Team, Bo Zhang, Shiyang Feng, Xiangchao Yan, Jiakang Yuan, Runmin Ma, Yusong Hu, Zhiyin Yu, Xiaohan He, Songtao Huang, et al. Internagent: When agent becomes the scientist– building closed-loop system from hypothesis to verification. arXiv preprint arXiv:2505.16938, 2025a. Kimi Team, Angang Du, Bohong Yin, Bowei Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chenzhuang Du, Chu Wei, et al. Kimi-vl technical report. arXiv preprint arXiv:2504.07491, 2025b. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothé","arxiv_match":true}],"id":"2504.07491","metadata_source":"Cached arXiv API metadata","rank":968},{"title":"Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale","arxiv_id":"2504.14225","year":2025,"url":"https://arxiv.org/abs/2504.14225","citing_submissions":104,"explicit_arxiv_matches":59,"examples":[{"id":"VpOCat31oP","title":"MemEnv: Evaluating Multi-dimensional Long-Term Memory via Graph-Grounded Dynamic Interaction","forum_url":"https://openreview.net/forum?id=VpOCat31oP","excerpt":"ng llms’ limits on multi-turn instruction following with a benchmark evolving framework. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9574–9590, 2026. Bowen Jiang, Zhuoqun Hao, Young-Min Cho, Bryan Li, Yuan Yuan, Sihao Chen, Lyle Ungar, Camillo J Taylor, and Dan Roth. Know me, respond to me: Benchmarking llms for dynamic user profiling and personalized responses at scale. arXiv preprint arXiv:2504.14225, 2025a. Bowen Jiang, Yuan Yuan, Maohao Shen, Zhuoqun Hao, Zhangchen Xu, Zichen Chen, Ziyi Liu, Anve","arxiv_match":true},{"id":"X4WbRy6BGr","title":"MemFlow: Learning Compact Soft Memory for Long-Context Personalization via On-Policy Optimization","forum_url":"https://openreview.net/forum?id=X4WbRy6BGr","excerpt":"alin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al. Perceiver io: A general architecture for structured inputs & outputs. In International Conference on Learning Representations, 2022. Bowen Jiang, Zhuoqun Hao, Young-Min Cho, Bryan Li, Yuan Yuan, Sihao Chen, Lyle Ungar, Camillo J Taylor, and Dan Roth. Know me, respond to me: Benchmarking llms for dynamic user profiling and personalized responses at scale. arXiv preprint arXiv:2504.14225, 2025. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. Llmlingua: Compressing ","arxiv_match":true},{"id":"7Ch3AHWDtZ","title":"Identify Then Reinforce: Action-Level Credit for Graph-Structured Memory Agents","forum_url":"https://openreview.net/forum?id=7Ch3AHWDtZ","excerpt":" pp. 287–318. PMLR, 2023. URL https://proceedings.mlr.press/v205/ichter23a.html. Bowen Jiang, Zhuoqun Hao, Young-Min Cho, Bryan Li, Yuan Yuan, Sihao Chen, Lyle Ungar, Camillo J. Taylor, and Dan Roth. Know me, respond to me: Benchmarking LLMs for dynamic user profiling and personalized responses at scale. In Proceedings of the Conference on Language Modeling, 2025a. URL https://openreview.net/forum?id=6ox8XZGOqP. Bowen Jiang, Yuan Yuan, Maohao Shen, Zhuoqun Hao, Zhangchen Xu, Zichen Chen, Ziyi Liu, A","arxiv_match":false}],"id":"2504.14225","metadata_source":"Cached arXiv API metadata","rank":969},{"title":"Mme-realworld: Could your multimodal llm challenge high-resolution real-world scenarios that are difficult for humans?","arxiv_id":"2408.13257","year":2024,"url":"https://arxiv.org/abs/2408.13257","citing_submissions":104,"explicit_arxiv_matches":31,"examples":[{"id":"6ewwTZLXbK","title":"Fused Multi-Latent Reasoning: Unified Multimodal Reasoning in Latent Space","forum_url":"https://openreview.net/forum?id=6ewwTZLXbK","excerpt":"anyu Xie, Mingming Cheng, and Xiang Li. Crystal: Spontaneous emergence of visual latents in mllms, 2026. URL https://arxiv.org/abs/2602. 20980. Yi-Fan Zhang, Huanyu Zhang, Haochen Tian, Chaoyou Fu, Shuangqing Zhang, Junfei Wu, Feng Li, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. Mme-realworld: Could your multimodal llm challenge high-resolution real-world scenarios that are difficult for humans?, 2025d. URL https://arxiv.org/abs/2408.13257. Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. D","arxiv_match":true},{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":", Yuxuan Li, Xin Zhang, Tianyu Xie, Mingming Cheng, and Xiang Li. Crystal: Spontaneous emergence of visual latents in mllms. arXiv preprint arXiv:2602.20980, 2026a. Yi-Fan Zhang, Huanyu Zhang, Haochen Tian, Chaoyou Fu, Shuangqing Zhang, Junfei Wu, Feng Li, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. Mmerealworld: Could your multimodal llm challenge high-resolution real-world scenarios that are difficult for humans? arXiv preprint arXiv:2408.13257, 2024b. Yi-Fan Zhang, Xingyu Lu, Shukang Yin, Chaoyou Fu, Wei Chen, Xiao Hu, Bin Wen, Kaiyu Jiang, ","arxiv_match":true},{"id":"sBvEgtiF5l","title":"Counterfactual Visual On-Policy Distillation","forum_url":"https://openreview.net/forum?id=sBvEgtiF5l","excerpt":" Yuan Lu, Weiwen Liu, et al. Vad: Attributing visual evidence for target reconstruction in multimodal on-policy distillation. arXiv preprint arXiv:2607.28590, 2026. Yi-Fan Zhang, Huanyu Zhang, Haochen Tian, Chaoyou Fu, Shuangqing Zhang, Junfei Wu, Feng Li, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. Mmerealworld: Could your multimodal llm challenge high-resolution real-world scenarios that are difficult for humans? arXiv preprint arXiv:2408.13257, 2024. Yi-Fan Zhang, Xingyu Lu, Shukang Yin, Chaoyou Fu, Wei Chen, Xiao Hu, Bin Wen, Kaiyu Jiang, C","arxiv_match":true}],"id":"2408.13257","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":970},{"title":"Multi-agent Architecture Search via Agentic Supernet","arxiv_id":"2502.04180","year":2025,"url":"https://arxiv.org/abs/2502.04180","citing_submissions":104,"explicit_arxiv_matches":52,"examples":[{"id":"JmKCJSrhxX","title":"FlowCompile: An Optimizing Compiler for Structured LLM Workflows","forum_url":"https://openreview.net/forum?id=JmKCJSrhxX","excerpt":"r (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15549–15572, Vienna, Austria, July 2025. Association for Computational Linguistics. ISBN 979-8-89176-251-0. doi: 10.18653/v1/2025.acl-long.757. URL https://aclanthology. org/2025.acl-long.757/. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. Multi-agent architecture search via agentic supernet. arXiv preprint arXiv:2502.04180, 2025. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhu","arxiv_match":true},{"id":"0XTyMGNG5R","title":"ReLay: Are We Ready for Latent Routing for Multi-Agent Systems?","forum_url":"https://openreview.net/forum?id=0XTyMGNG5R","excerpt":" Xinbing Wang, and Zhouhan Lin. Pretraining with token-level adaptive latent chainof-thought. arXiv preprint arXiv:2602.08220, 2026. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. Multi-agent architecture search via agentic supernet. In Proceedings of the ICML 2025, ICML’25. JMLR.org, 2025a. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. Multi-agent architecture search via agentic supernet. arXiv preprint arXiv:2502.04180, 2025b. Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeff","arxiv_match":true},{"id":"jfq0Rcq97f","title":"Learning Role-Context Graph Policies for Adaptive Multi-Agent Collaboration","forum_url":"https://openreview.net/forum?id=jfq0Rcq97f","excerpt":"acing hierarchical memory for multi-agent systems. In The Annual Conference on Neural Information Processing Systems, 2025a. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, LEI BAI, and Xiang Wang. Multi-agent architecture search via agentic supernet. In The International Conference on Machine Learning, 2025b. Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeffrey Yu, and Tianlong Chen. Cut the crap: An eco","arxiv_match":false}],"id":"2502.04180","metadata_source":"Cached arXiv API metadata","rank":971},{"title":"SkyReels-V2: Infinite-length Film Generative Model","arxiv_id":"2504.13074","year":2025,"url":"https://arxiv.org/abs/2504.13074","citing_submissions":104,"explicit_arxiv_matches":95,"examples":[{"id":"2B4fM59dDy","title":"DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation","forum_url":"https://openreview.net/forum?id=2B4fM59dDy","excerpt":"rence on Machine Learning, 2026. Boyuan Chen, Diego Martı́ Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. In Advances in Neural Information Processing Systems, 2024. Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng Ma, et al. Skyreels-v2: Infinite-length film generative model. arXiv preprint arXiv:2504.13074, 2025. Shuo Chen, Cong Wei, Sun Sun, Ping Nie, Kai Zhou, Ge Zhang, Ming-Hsuan Yang, and Wenhu Chen.","arxiv_match":true},{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"s, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024. URL https://openai.com/research/ video-generation-models-as-world-simulators. Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng Ma, et al. Skyreels-v2: Infinite-length film generative model. arXiv preprint arXiv:2504.13074, 2025. Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Y","arxiv_match":true},{"id":"WksSdpWwJq","title":"Semantically Aligned KV Cache Compression for Efficient Agentic Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=WksSdpWwJq","excerpt":":60980–61005, 2025. Boyuan Chen, Diego Martı́ Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. Advances in Neural Information Processing Systems, 37:24081–24125, 2024. Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng Ma, et al. Skyreels-v2: Infinite-length film generative model. arXiv preprint arXiv:2504.13074, 2025. Hanmo Chen, Chenghao Xu, Xu Yang, Xuan Chen, and Cheng Deng. Past-and future-informed kv cac","arxiv_match":true}],"id":"2504.13074","metadata_source":"Cached arXiv API metadata","rank":972},{"title":"MoBA: Mixture of Block Attention for Long-Context LLMs","arxiv_id":"2502.13189","year":2025,"url":"https://arxiv.org/abs/2502.13189","citing_submissions":103,"explicit_arxiv_matches":58,"examples":[{"id":"4TxphVYPCA","title":"Hierarchical Adaptive Sparse Attention over a Multi-Resolution Cache Index","forum_url":"https://openreview.net/forum?id=4TxphVYPCA","excerpt":"r Computational Linguistics (Volume 1: Long Papers), pp. 3214–3252, 2022. Enzhe Lu, Zhejun Jiang, Jingyuan Liu, Yulun Du, Tao Jiang, Chao Hong, Shaowei Liu, Weiran He, Enming Yuan, Yuzhi Wang, et al. MoBA: Mixture of block attention for long-context LLMs. Advances in Neural Information Processing Systems, 38:17790–17815, 2025. Peter McCullagh. Regression models for ordinal data. Journal of the Royal Statistical Society: Series B, 42(2):109–142, 1980.","arxiv_match":false},{"id":"JMgs0JW3HF","title":"Simplified Sparse Attention via Gist Tokens","forum_url":"https://openreview.net/forum?id=JMgs0JW3HF","excerpt":"amework for ai-driven scientific and algorithmic discovery. In Proceedings of the ACM Conference on AI and Agentic Systems, CAIS ’26, pp. 1223–1227. Association for Computing Machinery, 2026. doi: 10.1145/3786335.3813221. URL https: //doi.org/10.1145/3786335.3813221. Enzhe Lu, Zhejun Jiang, Jingyuan Liu, Yulun Du, Tao Jiang, Chao Hong, Shaowei Liu, Weiran He, Enming Yuan, Yuzhi Wang, et al. Moba: Mixture of block attention for long-context llms. arXiv preprint arXiv:2502.13189, 2025. Meta AI. Llama 3.2: Revolutionizing edge ai and vision with open, customizable models. https","arxiv_match":true},{"id":"Rb57sy6lCT","title":"SHARP-WAM: Sparse Hierarchical Attention with Probe Tokens for Bounded-Context World Action Modeling","forum_url":"https://openreview.net/forum?id=Rb57sy6lCT","excerpt":"ation Processing Systems, volume 36, 2023. Datasets and Benchmarks Track. Enzhe Lu, Zhejun Jiang, Jingyuan Liu, Yulun Du, Tao Jiang, Chao Hong, Shaowei Liu, Weiran He, Enming Yuan, Yuzhi Wang, et al. MoBA: Mixture of block attention for long-context LLMs. In Advances in Neural Information Processing Systems, volume 38, 2025. Hao Luo, Wanpeng Zhang, Yicheng Feng, Sipeng Zheng, Haiweng Xu, Chaoyi Xu, Ziheng Xi, Yuhui Fu, and Zongqing Lu. Being-H0.7: A ","arxiv_match":false}],"id":"2502.13189","metadata_source":"Cached arXiv API metadata","rank":973},{"title":"Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding","arxiv_id":"2601.10611","year":2026,"url":"https://arxiv.org/abs/2601.10611","citing_submissions":103,"explicit_arxiv_matches":41,"examples":[{"id":"XtiSjXV6kO","title":"Counterfactual Interventions for Latent Visual Reasoning","forum_url":"https://openreview.net/forum?id=XtiSjXV6kO","excerpt":"rint arXiv:2505.16782, 2025. Christopher Clark, Jieyu Zhang, Zixian Ma, Jae Sung Park, Mohammadreza Salehi, Rohun Tripathi, Sangho Lee, Zhongzheng Ren, Chris Dongjoo Kim, Yinuo Yang, Vincent Shao, Yue Yang, Weikai Huang, Ziqi Gao, Taira Anderson, Jianrui Zhang, Jitesh Jain, George Stoica, Winson Han, Ali Farhadi, and Ranjay Krishna. Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding, 2026. URL https://arxiv.org/abs/2601. 10611v1. Jingcheng Deng, Zihao Wei, Liang Pang, Junhong Wu, Shicheng Xu, Zenghao Duan, and Huawei Shen. Lat","arxiv_match":true},{"id":"SsjhVTW9zo","title":"PhysCount: Do MLLMs Count Visual Appearances or Physical Entities?","forum_url":"https://openreview.net/forum?id=SsjhVTW9zo","excerpt":"ammadreza Salehi, Jason Ren, Chris Dongjoo Kim, Yinuo Yang, Vincent Shao, Yue Yang, Weikai Huang, Ziqi Gao, Taira Anderson, Jianrui Zhang, Jitesh Jain, George Stoica, Ali Farhadi, and Ranjay Krishna. Molmo2: Open weights and data for vision-language models with video understanding and grounding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 28652–28668, June 2026. DeepSeek-AI. DeepSeek-V4-Flash-Vision: Vision input release notes, 2026. URL ","arxiv_match":false},{"id":"y01WnZj2L0","title":"Seeing More, Refusing Less: Two Mechanisms of Safety Erosion in Video Large Language Models","forum_url":"https://openreview.net/forum?id=y01WnZj2L0","excerpt":"lligence, volume 40, pp. 35331–35339, 2026. Christopher Clark, Jieyu Zhang, Zixian Ma, Jae Sung Park, Rohun Tripathi, Sangho Lee, Mohammadreza Salehi, Jason Ren, Chris Dongjoo Kim, Yinuo Yang, et al. Molmo2: Open weights and data for vision-language models with video understanding and grounding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 28652–28668, 2026. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit","arxiv_match":false}],"id":"2601.10611","metadata_source":"Cached arXiv API metadata","rank":974},{"title":"SORRY-bench: Systematically evaluating large language model safety refusal","arxiv_id":"2406.14598","year":2024,"url":"https://arxiv.org/abs/2406.14598","citing_submissions":103,"explicit_arxiv_matches":30,"examples":[{"id":"yECLVPwR3R","title":"Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation","forum_url":"https://openreview.net/forum?id=yECLVPwR3R","excerpt":"Vision – ECCV 2020, pp. 322–339. Springer, Cham, 2020. doi: 10.1007/978-3-030-58577-8 20. URL https://link.springer.com/chapter/10. 1007/978-3-030-58577-8_20. Tinghao Xie, Xiangyu Qi, Yi Zeng, et al. SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal. In International Conference on Learning Representations, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/hash/ 9622163c87b67fd5a4a0ec3247cf356e-Abstract-Conference.html. Yuan Xiong, Ziq","arxiv_match":false},{"id":"DkgHpC6dOc","title":"The Other Side of Answering: How Language Models Respond Beyond Direct Answers","forum_url":"https://openreview.net/forum?id=DkgHpC6dOc","excerpt":"ches public beta, 2026. URL https: //mimo.mi.com/docs/en-US/news/latest/v2.5-news. Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, and Prateek Mittal. SORRY-bench: Systematically evaluating large language model safety refusal. arXiv preprint arXiv:2406.14598, 2024. doi: 10.48550/arXiv.2406.14598. URL https://arxiv.org/abs/2406.14598. Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. Do large language","arxiv_match":true},{"id":"I35nG4HR52","title":"When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making","forum_url":"https://openreview.net/forum?id=I35nG4HR52","excerpt":"n real computer environments. In Advances in Neural Information Processing Systems, 2024. Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, and Prateek Mittal. Sorry-bench: Systematically evaluating large language model safety refusal. In International Conference on Learning Representations, 2025. URL https: //arxiv.org/abs/2406.14598. Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong, Weiming Wu, Jiayang Sun, Jiamin Song, Kaiqian Cui, Bowe","arxiv_match":true}],"id":"2406.14598","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":975},{"title":"Vip: Towards universal visual reward and representation via value-implicit pre-training","arxiv_id":"2210.00030","year":2022,"url":"https://arxiv.org/abs/2210.00030","citing_submissions":103,"explicit_arxiv_matches":38,"examples":[{"id":"DTubPUTX1o","title":"World Model Control by Trajectory Reachability Models","forum_url":"https://openreview.net/forum?id=DTubPUTX1o","excerpt":" for latent world models. arXiv preprint arXiv:2605.07278, 2026. URL https://arxiv.org/abs/2605.07278. Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang. VIP: Towards universal visual reward and representation via value-implicit pre-training. In International Conference on Learning Representations, 2023. URL https://iclr.cc/virtual/2023/ poster/11127. Lucas Maes, Quentin Le Lidec, Luiz Facury, Nassim Massaudi, Ayush Chaurasia, Francesco ","arxiv_match":false},{"id":"4abOrbSBt5","title":"Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4abOrbSBt5","excerpt":". Faster-wam: Do world action models need deep action modules? arXiv preprint arXiv:2608.02365, 2026b. Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang. Vip: Towards universal visual reward and representation via value-implicit pre-training. In The Eleventh International Conference on Learning Representations, 2023. Kaichun Mo, Leonidas J Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani. Where2act: From pixels to actions for","arxiv_match":false},{"id":"ubiZuUXpr6","title":"Copper-Policy: Focus on the Representation for Robust Robot Manipulation","forum_url":"https://openreview.net/forum?id=ubiZuUXpr6","excerpt":"Being-H0.7: A latent world-action model from egocentric videos. arXiv preprint arXiv:2605.00078, 2026. Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang. VIP: Towards universal visual reward and representation via value-implicit pre-training. In The Eleventh International Conference on Learning Representations, 2023. URL https: //openreview.net/forum?id=YJ7o2wetJ2. Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, and Randall Bale","arxiv_match":false}],"id":"2210.00030","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":976},{"title":"A dynamic llm-powered agent network for task-oriented agent collaboration","arxiv_id":"2310.02170","year":2023,"url":"https://arxiv.org/abs/2310.02170","citing_submissions":102,"explicit_arxiv_matches":51,"examples":[{"id":"x9qK9ehqNi","title":"Coordinated Mixed-Precision KV Cache Quantization for Efficient LLM-based Multi-agent Systems","forum_url":"https://openreview.net/forum?id=x9qK9ehqNi","excerpt":"of importance hypothesis for llm kv cache compression at test time. Advances in Neural Information Processing Systems, 36:52342–52364, 2023. Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang. A dynamic llm-powered agent network for task-oriented agent collaboration. In First Conference on Language Modeling, 2024b. URL https://openreview.net/forum?id=XII0Wp1XA9. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and X","arxiv_match":false},{"id":"sN2LsMHXUF","title":"ECHO: Evolving Coordination from Human Operations for Training-Free LLM-Based Multi-Agent Decision Making","forum_url":"https://openreview.net/forum?id=sN2LsMHXUF","excerpt":" et al. Agentbench: Evaluating llms as agents. In International Conference on Learning Representations, volume 2024, pp. 52989–53046, 2024a. Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang. A dynamic llm-powered agent network for task-oriented agent collaboration. In First Conference on Language Modeling, 2024b. Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. Multiagent actor-critic for mixed cooperative-competitive environ","arxiv_match":false},{"id":"0a8ebRImO1","title":"Towards Reliable Financial Agents: Mitigating Financial Hallucinations through Evidence-Grounded Execution and Verified Experience","forum_url":"https://openreview.net/forum?id=0a8ebRImO1","excerpt":"ution of llm-based agents. arXiv preprint arXiv:2601.06818, 2026b. Zhaowei Liu, Xin Guo, Zhi Yang, Fangqi Lou, Lingfeng Zeng, Jinyi Niu, Mengping Li, Qi Qi, Zhiqiang Liu, Yiyang Han, et al. Fin-r1: A large language model for financial reasoning through reinforcement learning. arXiv preprint arXiv:2503.16252, 2025. Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang. A dynamic llm-powered agent network for task-oriented agent collaboration. arXiv preprint arXiv:2310.02170, 2023. Jiaxuan Lu, Kong Wang, Yemin Wang, Qingmei Tang, Hongwei Zeng, Xiang Chen, Jiahao Pi, Shujia","arxiv_match":true}],"id":"2310.02170","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":977},{"title":"CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation","arxiv_id":"2603.22435","year":2026,"url":"https://arxiv.org/abs/2603.22435","citing_submissions":102,"explicit_arxiv_matches":80,"examples":[{"id":"Pb1ZeERbRM","title":"Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation","forum_url":"https://openreview.net/forum?id=Pb1ZeERbRM","excerpt":"lanning with parallel allocation and recomposition across tasks, 2026. URL https://arxiv.org/abs/2506.06683. Letian Fu, Justin Yu, Karim El-Refai, Ethan Kou, Haoru Xue, Huang Huang, Wenli Xiao, Guanzhi Wang, Dantong Niu, Fei-Fei Li, Guanya Shi, Jiajun Wu, Shankar Sastry, Yuke Zhu, Ken Goldberg, and Linxi Fan. CaP-X: A framework for benchmarking and improving coding agents for robot manipulation. arXiv preprint arXiv:2603.22435, 2026. URL https://arxiv.org/ abs/2603.22435. Dibya Ghosh, Homer Rich Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, To","arxiv_match":true},{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"dfellow, and Sergey Levine. Unsupervised learning for physical interaction through video prediction, 2016. URL https://arxiv.org/abs/1605.07157. Letian Fu, Justin Yu, Karim El-Refai, Ethan Kou, Haoru Xue, Huang Huang, Wenli Xiao, Guanzhi Wang, Dantong Niu, Fei-Fei Li, Guanya Shi, Jiajun Wu, Shankar Sastry, Yuke Zhu, Ken Goldberg, and Linxi Fan. Cap-x: A framework for benchmarking and improving coding agents for robot manipulation. In ICML, 2026. arXiv:2603.22435. Shenyuan Gao, William Liang, Kaiyuan Zheng, Ayaan Malik, Seonghyeon Ye, Sihyun Yu, WeiCheng Tseng,","arxiv_match":true},{"id":"u6Or5JaCFb","title":"RoboBridge: A Self-Evolving Embodied Agent Framework for Sim-to-Real Transfer","forum_url":"https://openreview.net/forum?id=u6Or5JaCFb","excerpt":"anhe Yu, et al. Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378, 2023. Letian Fu, Justin Yu, Karim El-Refai, Ethan Kou, Haoru Xue, Huang Huang, Wenli Xiao, Guanzhi Wang, Dantong Niu, Fei-Fei Li, Guanya Shi, Jiajun Wu, Shankar Sastry, Yuke Zhu, Ken Goldberg, and Linxi ”Jim” Fan. CaP-X: A framework for benchmarking and improving coding agents for robot manipulation. arXiv preprint arXiv:2603.22435, 2026. URL https: //arxiv.org/abs/2603.22435. Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tom","arxiv_match":true}],"id":"2603.22435","metadata_source":"Cached arXiv API metadata","rank":978},{"title":"DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos","arxiv_id":"2602.06949","year":2026,"url":"https://arxiv.org/abs/2602.06949","citing_submissions":102,"explicit_arxiv_matches":84,"examples":[{"id":"MmX67BPA1M","title":"Agentic World Action Model","forum_url":"https://openreview.net/forum?id=MmX67BPA1M","excerpt":"n Zheng, Ayaan Malik, Seonghyeon Ye, Sihyun Yu, WeiCheng Tseng, Yuzhu Dong, Kaichun Mo, Chen-Hsuan Lin, Qianli Ma, Seungjun Nah, Loic Magne, Jiannan Xiang, Yuqi Xie, Ruijie Zheng, Dantong Niu, You Liang Tan, K.R. Zentner, George Kurian, Suneel Indupuru, Pooya Jannaty, Jinwei Gu, Jun Zhang, Jitendra Malik, Pieter Abbeel, Ming-Yu Liu, Yuke Zhu, Joel Jang, and Linxi ”Jim” Fan. Dreamdojo: A generalist robot world model from large-scale human videos. arXiv preprint arXiv:2602.06949, 2026. Gemini Robotics Team. Gemini robotics: Bringing AI into the physical world. arXiv preprint a","arxiv_match":true},{"id":"rtlzkXdJe9","title":"PhysiCalWorld: In-Context Physics Calibration for World Models","forum_url":"https://openreview.net/forum?id=rtlzkXdJe9","excerpt":". 3 Letian Fu, Huang Huang, Gaurav Datta, Lawrence Yunliang Chen, William Chung-Ho Panitch, Fangchen Liu, Hui Li, and Ken Goldberg. In-context imitation learning via next-token prediction. arXiv preprint arXiv:2408.15980, 2024. 2 Shenyuan Gao, William Liang, Kaiyuan Zheng, Ayaan Malik, Seonghyeon Ye, Sihyun Yu, Wei-Cheng Tseng, Yuzhu Dong, Kaichun Mo, Chen-Hsuan Lin, et al. Dreamdojo: A generalist robot world model from large-scale human videos. arXiv preprint arXiv:2602.06949, 2026. 1, 2 Yanjiang Guo, Lucy Shi, Jianyu Chen, and Chelsea Finn. Ctrl-world: A controllable gener","arxiv_match":true},{"id":"tTLcslQ9R4","title":"EnactPhys: Executing Evolving Physical Processes in Video Diffusion","forum_url":"https://openreview.net/forum?id=tTLcslQ9R4","excerpt":"nghuai Zhang, Kaiyuan Zhang, Pranav Bodapati, Alicia Yu, Fnu Suya, Mohammad Rostami, Jiaqi Ma, and Yuan Tian. What-if world: A causal benchmark for general world models in embodied scenarios. arXiv preprint arXiv:2605.27589, 2026. Shenyuan Gao, William Liang, Kaiyuan Zheng, Ayaan Malik, Seonghyeon Ye, Sihyun Yu, WeiCheng Tseng, Yuzhu Dong, Kaichun Mo, Chen-Hsuan Lin, et al. Dreamdojo: A generalist robot world model from large-scale human videos. arXiv preprint arXiv:2602.06949, 2026. Daniel Geng, Charles Herrmann, Junhwa Hur, Forrester Cole, Serena Zhang, Tobias Pfaff, Tatia","arxiv_match":true}],"id":"2602.06949","metadata_source":"Cached arXiv API metadata","rank":979},{"title":"KTO: Model alignment as prospect theoretic optimization","arxiv_id":"2402.01306","year":2024,"url":"https://arxiv.org/abs/2402.01306","citing_submissions":102,"explicit_arxiv_matches":71,"examples":[{"id":"EPREfh3h40","title":"TRACE: Trajectory Return Attribution and Contrastive Erasure for Multi-Turn Safety","forum_url":"https://openreview.net/forum?id=EPREfh3h40","excerpt":", and Eric Wong. JailbreakBench: An open robustness benchmark for jailbreaking large language models. In Proceedings of NeurIPS Datasets and Benchmarks Track, 2024. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. QLoRA: Efficient finetuning of quantized LLMs. In Proceedings of NeurIPS, 2023. Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. KTO: Model alignment as prospect theoretic optimization. arXiv preprint arXiv:2402.01306, 2024. Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, and Sijia Liu. S","arxiv_match":true},{"id":"hbEJ10jEL7","title":"Gap-Adaptive Per-Objective Regularization against Differential Overoptimization in Multi-Objective Alignment","forum_url":"https://openreview.net/forum?id=hbEJ10jEL7","excerpt":"pal, Alekh Agarwal, Ahmad Beirami, Alex D’Amour, DJ Dvijotham, Adam Fisch, Katherine Heller, Stephen Pfohl, Deepak Ramachandran, Peter Shaw, and Jonathan Berant. Helping or herding? reward model ensembles mitigate but do not eliminate reward hacking, 2024. URL https://arxiv.org/abs/2312.09244. Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. Kto: Model alignment as prospect theoretic optimization, 2024. URL https://arxiv.org/abs/ 2402.01306. Leo Gao, John Schulman, and Jacob Hilton. Scaling laws for reward model overoptimization, 2022. UR","arxiv_match":true},{"id":"mCcMNyHoSX","title":"RALA: Reliability-Aware Latent Alignment under Heterogeneous Feedback","forum_url":"https://openreview.net/forum?id=mCcMNyHoSX","excerpt":"chnologies (Volume 1: Long Papers), 2024. Sujan Dutta, Sayantan Mahinder, Raviteja Anantha, and Bortik Bandyopadhyay. Applying rlaif for code generation with api-usage in lightweight llms. In Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ ACL 2024), pp. 39–45, 2024. Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. Kto: Model alignment as prospect theoretic optimization. arXiv preprint arXiv:2402.01306, 2024. Jack Gallifant, Majid Afshar, Saleem Ameen, Yindalon Aphinyanaphongs, Shan Chen, Giovanni Ca","arxiv_match":true}],"id":"2402.01306","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":980},{"title":"Long-tail learning via logit adjustment","arxiv_id":"2007.07314","year":2020,"url":"https://arxiv.org/abs/2007.07314","citing_submissions":102,"explicit_arxiv_matches":30,"examples":[{"id":"8d5p87u499","title":"Fisher-Consistent Evidential Learning under Prior Shift","forum_url":"https://openreview.net/forum?id=8d5p87u499","excerpt":"ceedings.neurips.cc/paper_files/paper/2018/ file/3ea2db50e62ceefceaf70a9d9a56a6f4-Paper.pdf. Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. Long-tail learning via logit adjustment. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=37nvvqkCo5. OpenAI, :, Sandhini Agarwal, Lama Ahmad, and Others. gpt-oss-120b & gpt-oss-20b model c","arxiv_match":false},{"id":"QGMsQCSLE9","title":"Variance-Reduced Conformal Prediction via Shrinkage for Long-Tailed Recognition","forum_url":"https://openreview.net/forum?id=QGMsQCSLE9","excerpt":" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. Long-tail learning via logit adjustment. In International Conference on Learning Representations, 2021. Harris Papadopoulos, Kostas Proedrou, Vladimir Vovk, and Alex Gammerman. Inductive confidence machines for regression. In European Conf","arxiv_match":false},{"id":"pC4xCacvme","title":"A Long-Tail Perspective on Group Fairness: Fair Logit Adjustment via Prior Correction","forum_url":"https://openreview.net/forum?id=pC4xCacvme","excerpt":"ings of Machine Learning Research, pp. 4051–4060. PMLR, 09–15 Jun 2019. URL https://proceedings.mlr. press/v97/liu19f.html. Aditya Krishna Menon and Robert C Williamson. The cost of fairness in binary classification. In Conference on Fairness, accountability and transparency, pp. 107–118. PMLR, 2018. Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. Long-tail learning via logit adjustment. arXiv preprint arXiv:2007.07314, 2020. Jiawei Ren, Cunjun Yu, shunan sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, and hongsheng Li. Balanc","arxiv_match":true}],"id":"2007.07314","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":981},{"title":"On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability","arxiv_id":"2608.30320","year":2026,"url":"https://arxiv.org/abs/2608.30320","citing_submissions":102,"explicit_arxiv_matches":81,"examples":[{"id":"djPfi1owSc","title":"Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents","forum_url":"https://openreview.net/forum?id=djPfi1owSc","excerpt":"ntsafety.openai.com/gpt-5-6/gpt-5-6.pdf. URL https:// Qwen Team. Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All, April 2026a. URL https://qwen.ai/blog?id=qwen3.6-35b-a3b. Qwen Team. Qwen3.8-Max: A New Bar for Coding and Cowork, August 2026b. URL https: //qwen.ai/blog?id=qwen3.8. Qwen Team. On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability. Technical report, Alibaba Group, August 2026c. URL https://arxiv.org/abs/ 2608.30320. Qwen Team. Qwen-Agent. https://github.com/QwenLM/Qwen-Agent, 2026d. Software repository, accessed ","arxiv_match":true},{"id":"gkOVUNKjOT","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","forum_url":"https://openreview.net/forum?id=gkOVUNKjOT","excerpt":"essed: 202510-27. NVIDIA. GPUDirect. https://developer.nvidia.com/gpudirect, 2025b. Accessed: 2025-10-27. OpenAI, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman, Andy Applebaum, Edwin Arbus, Rahul K Arora, Yu Bai, Bowen Baker, Haiming Bao, et al. gpt-oss-120b & gpt-oss-20b model card. arXiv preprint arXiv:2508.10925, 2025. Qwen Team. On the design of Qwen3.8-Next architecture: Evaluation, efficiency, and training stability, 2026. URL https://arxiv.org/abs/2608.30320. Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. ZeRO: Memory optimizations towar","arxiv_match":true},{"id":"xzOnjQHMr9","title":"LoopLA: Causal State Refinement for Looped Linear Attention","forum_url":"https://openreview.net/forum?id=xzOnjQHMr9","excerpt":"n, Bochao Mao, Chengruidong Zhang, Fan Zhou, Hao Luo, Haofeng Huang, Haoran Lian, Haoyan Huang, Hongqing Chen, Jianwei Zhang, Jing Xu, Junjie Wang, Langshi Chen, Liangyu Wang, Linlang Jiang, Man Yuan, Minmin Sun, Peng Jin, Siqi Zhang, Siyu Wang, Xingzhang Ren, Yakai Wang, Yi Zhang, Yiming Dong, Yizhong Cao, Yubo Ma, Yunfei Mao, Bo Zheng, and Dayiheng Liu. On the design of Qwen3.8-Next architecture: Evaluation, efficiency, and training stability. arXiv preprint arXiv:2608.30320, 2026. Qwen Team. Qwen3.5-0.8B-Base. Hugging Face model card, 2026. huggingface.co/Qwen/Qwen3.5-0.8","arxiv_match":true}],"id":"2608.30320","metadata_source":"Cached arXiv API metadata","rank":982},{"title":"PonderNet: Learning to ponder","arxiv_id":"2107.05407","year":2021,"url":"https://arxiv.org/abs/2107.05407","citing_submissions":102,"explicit_arxiv_matches":69,"examples":[{"id":"25DdNVKXg6","title":"Shared Computation, Different Readouts: Depth Compatibility in Looped Transformers","forum_url":"https://openreview.net/forum?id=25DdNVKXg6","excerpt":"vid Lopez-Paz. Invariant Risk Minimization. arXiv preprint arXiv:1907.02893, 2019. Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. Deep Equilibrium Models. arXiv preprint arXiv:1909.01377, 2019. András Balogh and Márk Jelasity. How not to Stitch Representations to Measure Similarity: Task Loss Matching versus Direct Matching. arXiv preprint arXiv:2412.11299, 2024. Andrea Banino, Jan Balaguer, and Charles Blundell. PonderNet: Learning to Ponder. arXiv preprint arXiv:2107.05407, 2021. Yamini Bansal, Preetum Nakkiran, and Boaz Barak. Revisiting Model Stitching to Compare Neura","arxiv_match":true},{"id":"m61N49mzzc","title":"Improving Test-Time Scaling with Adaptive Looped Transformers","forum_url":"https://openreview.net/forum?id=m61N49mzzc","excerpt":"sive Depths for Adaptive Token-Level Computation. arXiv preprint arXiv:2507.10524, 2025. URL https://arxiv.org/abs/2507.10524. Junyeob Baek, Mingyu Jo, Minsu Kim, Mengye Ren, Yoshua Bengio, and Sungjin Ahn. Generative Recursive Reasoning. arXiv preprint arXiv:2605.19376, 2026. URL https://arxiv.org/ abs/2605.19376. Andrea Banino, Jan Balaguer, and Charles Blundell. PonderNet: Learning to Ponder. arXiv preprint arXiv:2107.05407, 2021. URL https://arxiv.org/abs/2107.05407. Hugh Blayney, Álvaro Arroyo, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Michael M.","arxiv_match":true},{"id":"4neS6BDROU","title":"Stress-Adaptive Inference Scaling: Learning When to Allocate Compute at Test Time","forum_url":"https://openreview.net/forum?id=4neS6BDROU","excerpt":"Jiang, and Larry S. Davis. SkipNet: Learning dynamic routing in convolutional networks. In European Conference on Computer Vision (ECCV), 2018. [4] Gabriel Banino, Jan Balaguer, and Charles Blundell. PonderNet: Learning to ponder. In International Conference on Learning Representations (ICLR), 2021. [5] Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy Lin. DeeBERT: Dynamic early exiting for accelerating BERT inference","arxiv_match":false}],"id":"2107.05407","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":983},{"title":"Reformer: The efficient transformer","arxiv_id":"2001.04451","year":2020,"url":"https://arxiv.org/abs/2001.04451","citing_submissions":102,"explicit_arxiv_matches":36,"examples":[{"id":"XIvhtY214J","title":"SOLAR: SVD-Optimized Lifelong Attention for Recommendation","forum_url":"https://openreview.net/forum?id=XIvhtY214J","excerpt":". Transformers are rnns: Fast autoregressive transformers with linear attention, 2020. URL http://proceedings. mlr.press/v119/katharopoulos20a.html. Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer, 2020. URL https://openreview.net/forum?id=rkgNKkHtvB. Yehuda Koren, Robert M. Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009. doi: 10.","arxiv_match":false},{"id":"SaboBZsQiW","title":"KernelWeaver: A Shape-Aware Kernel Agent for GPU Kernel Optimization","forum_url":"https://openreview.net/forum?id=SaboBZsQiW","excerpt":" Jaber and Osama Jaber. Autokernel: Autonomous gpu kernel optimization via iterative agentdriven search, 2026. URL https://arxiv.org/abs/2603.21331. Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. In International Conference on Learning Representations, 2020. URL https://openreview. net/forum?id=rkgNKkHtvB. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph","arxiv_match":false},{"id":"1ihfTCOu91","title":"Adamas: Hadamard Sparse Attention for Efficient Long-context Inference","forum_url":"https://openreview.net/forum?id=1ihfTCOu91","excerpt":"1–1611, Vancouver, Canada, July 2017. Association for Computational Linguistics. doi: 10.18653/v1/P17-1147. URL https://aclanthology.org/ P17-1147/. Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. In International Conference on Learning Representations, 2020. URL https://openreview. net/forum?id=rkgNKkHtvB. Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Ga","arxiv_match":false}],"id":"2001.04451","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":984},{"title":"The Diffusion Duality","arxiv_id":"2506.10892","year":2025,"url":"https://arxiv.org/abs/2506.10892","citing_submissions":102,"explicit_arxiv_matches":17,"examples":[{"id":"bkrXDY9uXm","title":"Ordered-DLM: Ordering Token Generation through Syntactic Hierarchy","forum_url":"https://openreview.net/forum?id=bkrXDY9uXm","excerpt":"nguage models. Advances in Neural Information Processing Systems, 37:130136–130184, 2024. Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T Chiu, and Volodymyr Kuleshov. The diffusion duality. In International Conference on Machine Learning, pp. 52584–52619. PMLR, 2025. Yair Schiff, Subham Sahoo, Hao Phung, Guanghan Wang, Sam Boshar, Hugo Dalla-Torre, Bernardo Almeida, Alexander Rush, Tho","arxiv_match":false},{"id":"l5ad06xjLE","title":"One Latent, Many Tokens: Jointly Learning Compressed Embeddings for Efficient Language Diffusion","forum_url":"https://openreview.net/forum?id=l5ad06xjLE","excerpt":"/ paper_files/paper/2024/hash/eb0b13cc515724ab8015bc978fdde0ad-Abstract-Conference.html. Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T. Chiu, and Volodymyr Kuleshov. The diffusion duality. In International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 52584–52619. PMLR, 2025. URL https://proceedings.mlr.press/v267/sahoo25a.html. Alexander ","arxiv_match":false},{"id":"FhkR2SEwBZ","title":"Simplex Relaxation for Discrete Diffusion","forum_url":"https://openreview.net/forum?id=FhkR2SEwBZ","excerpt":"ons, 2006. Justin Deschenaux and Caglar Gulcehre. Language modeling with hyperspherical flows, 2026. URL https://arxiv.org/abs/2605.11125. Justin Deschenaux, Caglar Gulcehre, and Subham Sekhar Sahoo. The diffusion duality, chapter II: Ψ-samplers and efficient curriculum. In The Fourteenth International Conference on Learning Representations, 2026. Griffin Floto, Thorsteinn Jonsson, Mihai Nica, Scott Sanner, and Eric Z","arxiv_match":false}],"id":"2506.10892","metadata_source":"Cached arXiv API metadata","rank":985},{"title":"Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions","arxiv_id":"2502.06768","year":2025,"url":"https://arxiv.org/abs/2502.06768","citing_submissions":102,"explicit_arxiv_matches":34,"examples":[{"id":"6zSuqyB4a9","title":"What Makes a Good Decoding Path? Selecting Parallel-Decodable Trajectories for Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=6zSuqyB4a9","excerpt":"ation for Computational Linguistics. doi: 10.18653/v1/2024. eacl-long.144. URL https://aclanthology.org/2024.eacl-long.144/. Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, and Sitan Chen. Train for the worst, plan for the best: Understanding token ordering in masked diffusions. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum? id=DjJmre5IkP. Seo Hyun Kim, Sunwoo Hong, Hojung Jung, Youngrok Park, and Se-Young Yun. Klass: K","arxiv_match":false},{"id":"IhXXms16ty","title":"A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models","forum_url":"https://openreview.net/forum?id=IhXXms16ty","excerpt":"h Palrecha, and Stefano Ermon. Mercury: Ultra-fast language models based on diffusion. arXiv e-prints, pp. arXiv–2506, 2025. Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, and Sitan Chen. Train for the worst, plan for the best: Understanding token ordering in masked diffusions. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum? id=DjJmre5IkP. Thomas M. Liggett. Continuous time Markov processes, volume 113 of Graduate Studi","arxiv_match":false},{"id":"pwQGcebZ9p","title":"Smarter Than Random, or Simply Busier? Where Diffusion-LLM Scheduling Earns","forum_url":"https://openreview.net/forum?id=pwQGcebZ9p","excerpt":"el decoding for dLLMs. In ICLR, 2026. arXiv:2509.26488. Feng Hong, Geng Yu, Yushi Ye, Haicheng Huang, Huangjie Zheng, Ya Zhang, Yanfeng Wang, and Jiangchao Yao. Wide-in, narrow-out: Revokable decoding for efficient and effective DLLMs. In ICLR, 2026. arXiv:2507.18578. Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, and Sitan Chen. Train for the worst, plan for the best: Understanding token ordering in masked diffusions. In ICML, 2025. arXiv:2502.06768. Sanghyun Lee, Seungryong Kim, Jongho Park, and Dongmin Park. Lookahead unmasking elicits accurate ","arxiv_match":true}],"id":"2502.06768","metadata_source":"Cached arXiv API metadata","rank":986},{"title":"VGGT-$Ω$","arxiv_id":"2605.15195","year":2026,"url":"https://arxiv.org/abs/2605.15195","citing_submissions":102,"explicit_arxiv_matches":102,"examples":[{"id":"am5fMt1sKR","title":"VITAL: From Visual Motion to Precise Action","forum_url":"https://openreview.net/forum?id=am5fMt1sKR","excerpt":"nomous driving. arXiv preprint arXiv:2603.29163, 2026. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Jianyuan Wang, Minghao Chen, Shangzhan Zhang, Nikita Karaev, Johannes Schönberger, Patrick Labatut, Piotr Bojanowski, David Novotny, Andrea Vedaldi, and Christian Rupprecht. VGGT-ω. arXiv preprint arXiv:2605.15195, 2026a. Junke Wang, Qihang Zhang, Shuai Yang, Yiming Luo, Yujun Shen, Zuxuan Wu, Yu-Gang Jiang, and","arxiv_match":true},{"id":"uqg7gDThwD","title":"Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation","forum_url":"https://openreview.net/forum?id=uqg7gDThwD","excerpt":"9.09676, 2025a. Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5294–5306. IEEE, 2025b. Jianyuan Wang, Minghao Chen, Shangzhan Zhang, Nikita Karaev, Johannes Schönberger, Patrick Labatut, Piotr Bojanowski, David Novotny, Andrea Vedaldi, and Christian Rupprecht. VGGT-ω. arXiv preprint arXiv:2605.15195, 2026a. Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geo","arxiv_match":true},{"id":"h0PK0nlkuc","title":"Seek-and-View Reasoning for Multi-View Spatial Understanding","forum_url":"https://openreview.net/forum?id=h0PK0nlkuc","excerpt":"6. IEEE, 2025a. Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5294–5306. IEEE, 2025b. Jianyuan Wang, Minghao Chen, Shangzhan Zhang, Nikita Karaev, Johannes Schönberger, Patrick Labatut, Piotr Bojanowski, David Novotny, Andrea Vedaldi, and Christian Rupprecht. Vggt-ω. arXiv preprint arXiv:2605.15195, 2026a. Kangrui Wang, Pingyue Zhang, Zihan Wang, Yaning Gao, Linjie Li, Qineng Wang, Hanyang Chen, ","arxiv_match":true}],"id":"2605.15195","metadata_source":"Cached arXiv API metadata","rank":987},{"title":"Cameractrl: Enabling camera control for text-to-video generation","arxiv_id":"2404.02101","year":2024,"url":"https://arxiv.org/abs/2404.02101","citing_submissions":101,"explicit_arxiv_matches":85,"examples":[{"id":"Ml7SujKmPd","title":"Orbia: Do Generated Videos Form a Consistent 3D World?","forum_url":"https://openreview.net/forum?id=Ml7SujKmPd","excerpt":"Bingjie Gao, Rui Chen, Geng Li, Jifan Li, Qishuai Wen, Li Niu, Jing Tang, Xiangxiang Chu, and Junqiao Zhao. R2M-Bench: Evaluating revisit memory via relative consistency in interactive video world models. arXiv preprint arXiv:2608.27328, 2026b. Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. CameraCtrl: Enabling Camera Control for Text-to-Video Generation. arXiv preprint arXiv:2404.02101, 2024. URL https://arxiv.org/abs/2404.02101. Xianglong He, Chunli Peng, Zexiang Liu, Boyang Wang, et al. Matrix-Game 2.0: An open-source real-t","arxiv_match":true},{"id":"mYfMacZ4WG","title":"FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow","forum_url":"https://openreview.net/forum?id=mYfMacZ4WG","excerpt":"sual foundation model. arXiv preprint arXiv:2601.03233, 2025. Seokil Ham, Sangmin Woo, Jin-Young Kim, Hyojun Go, Byeongjun Park, and Changick Kim. Diffusion model patching via mixture-of-prompts. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 17023–17031, 2025. Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation. arXiv preprint arXiv:2404.02101, 2024. Jonathan Ho and Tim Salimans. arXiv:2207.12598, 2022. Classifier-free diffusion guidance. ar","arxiv_match":true},{"id":"9kxTLJwbxB","title":"TraceWorld: Trajectory-Indexed Memory for Spatially Consistent Video World Model","forum_url":"https://openreview.net/forum?id=9kxTLJwbxB","excerpt":"r Zabari, Ori Gordon, et al. Ltx-video: Realtime video latent diffusion. arXiv preprint arXiv:2501.00103, 2024. Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation, 2024. Hao He, Ceyuan Yang, Shanchuan Lin, Yinghao Xu, Meng Wei, Liangke Gui, Qi Zhao, Gordon Wetzstein, Lu Jiang, and Hongsheng Li. Cameractrl ii: Dynamic scene exploration via cameracontrolled vide","arxiv_match":false}],"id":"2404.02101","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":988},{"title":"Generative Modeling via Drifting","arxiv_id":"2602.04770","year":2026,"url":"https://arxiv.org/abs/2602.04770","citing_submissions":101,"explicit_arxiv_matches":92,"examples":[{"id":"WxOx7Kok46","title":"Intrinsic Distribution Matching","forum_url":"https://openreview.net/forum?id=WxOx7Kok46","excerpt":"w: Realizing one-step generation on large models with self-adversarial flows. arXiv preprint arXiv:2512.05150, 2025. Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, Guang Shi, and Haoqi Fan. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505.14683, 2025. Mingyang Deng, He Li, Tianhong Li, Yilun Du, and Kaiming He. Generative modeling via drifting. arXiv preprint arXiv:2602.04770, 2026. Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Y","arxiv_match":true},{"id":"WsLUWEDwZr","title":"Amortized Moment Matching for Visual Generation","forum_url":"https://openreview.net/forum?id=WsLUWEDwZr","excerpt":"A family of fully open unified multimodal models-architecture, training and dataset. arXiv preprint arXiv:2505.09568, 2025a. Junying Chen, Zhenyang Cai, Pengcheng Chen, Shunian Chen, Ke Ji, Xidong Wang, Yunjin Yang, and Benyou Wang. Sharegpt-4o-image: Aligning multimodal models with gpt-4o-level image generation. arXiv preprint arXiv:2506.18095, 2025b. Mingyang Deng, He Li, Tianhong Li, Yilun Du, and Kaiming He. Generative modeling via drifting. arXiv preprint arXiv:2602.04770, 2026. Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arX","arxiv_match":true},{"id":"qXSBBxxUKa","title":"DriftCF: Generative Modeling of Joint Potential Outcome Distributions via One-Step Particle Drifting","forum_url":"https://openreview.net/forum?id=qXSBBxxUKa","excerpt":" On inductive biases for heterogeneous treatment effect estimation. In Advances in Neural Information Processing Systems, volume 34, 2021. Mingyang Deng, He Li, Tianhong Li, Yilun Du, and Kaiming He. Generative modeling via drifting, 2026. Richard Duong, Nicolaj Rux, Viktor Stein, and Gabriele Steidl. Wasserstein gradient flows of maximum mean discrepancy functionals with distance kernels under sobolev regularization. Philosophi","arxiv_match":false}],"id":"2602.04770","metadata_source":"Cached arXiv API metadata","rank":989},{"title":"HunyuanVideo 1.5 Technical Report","arxiv_id":"2511.18870","year":2025,"url":"https://arxiv.org/abs/2511.18870","citing_submissions":101,"explicit_arxiv_matches":98,"examples":[{"id":"zF3Ca9yM0r","title":"AttnForcing: Learning Full-Context Attention Proxies for Streaming Video Generation","forum_url":"https://openreview.net/forum?id=zF3Ca9yM0r","excerpt":"l. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314, 2025. Wenhao Wang and Yi Yang. Vidprom: A million-scale real prompt-gallery dataset for text-to-video diffusion models. Advances in Neural Information Processing Systems, 37:65618–65642, 2024. Bing Wu, Chang Zou, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Jack Peng, Jianbing Wu, Jiangfeng Xiong, Jie Jiang, et al. Hunyuanvideo 1.5 technical report. arXiv preprint arXiv:2511.18870, 2025a. Ruiqi Wu, Xuanhua He, Meng Cheng, Tianyu Yang, Yong Zhang, Zhuoliang Kang, Xunliang Cai, Xi","arxiv_match":true},{"id":"GM9LePOwrX","title":"Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation","forum_url":"https://openreview.net/forum?id=GM9LePOwrX","excerpt":"ri. Fast autoregressive video diffusion and world models with temporal cache compression and sparse attention. CoRR, abs/2602.01801, 2026. Tencent Hunyuan Foundation Model Team. abs/2511.18870, 2025. Hunyuanvideo 1.5 technical report. CoRR, Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zhang, W. Q. Zhang, Weifeng Luo, Xiaoyang Kang, Yuchen Sun, Yue Cao, Yunpeng Huang, Yutong Lin, Yuxin ","arxiv_match":false},{"id":"wm97mtaTWL","title":"SolveEdit: Benchmarking Visual Problem Solving in Generative Models","forum_url":"https://openreview.net/forum?id=wm97mtaTWL","excerpt":"e on computer vision and pattern recognition (cvpr), pp. 18359–18369. IEEE, 2023. Zhenyu Wang, Aoxue Li, Zhenguo Li, and Xihui Liu. Genartist: Multimodal llm as an agent for unified image generation and editing. Advances in Neural Information Processing Systems, 37: 128374–128395, 2024b. Bing Wu, Chang Zou, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Jack Peng, Jianbing Wu, Jiangfeng Xiong, Jie Jiang, et al. Hunyuanvideo 1.5 technical report. arXiv preprint arXiv:2511.18870, 2025a. Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan. Visual ch","arxiv_match":true}],"id":"2511.18870","metadata_source":"Cached arXiv API metadata","rank":990},{"title":"MMaDA: Multimodal Large Diffusion Language Models","arxiv_id":"2505.15809","year":2025,"url":"https://arxiv.org/abs/2505.15809","citing_submissions":101,"explicit_arxiv_matches":35,"examples":[{"id":"WyqZuE0Rdb","title":"CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models","forum_url":"https://openreview.net/forum?id=WyqZuE0Rdb","excerpt":"ent block-diffusion vlm via direct conversion from autoregressive vlm, 2026. URL https://arxiv.org/abs/ 2604.06832. Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang. Mmada: Multimodal large diffusion language models. In Danielle Belgrave, Cheng Zhang, Laura N. Montoya, Hsuan-Tien Lin, Razvan Pascanu, Piotr Koniusz, Marzyeh Ghassemi, Nancy Chen, Iván Vladimir Meza Ruı́z, and Arturo Loaiza-Bonilla (eds.), Advance","arxiv_match":false},{"id":"KHODv83hPB","title":"IntReal: A Real-World Dataset and Benchmark for Multimodal Interleaved Generation","forum_url":"https://openreview.net/forum?id=KHODv83hPB","excerpt":"u, Deqing Li, Hao Meng, Kuan Cao, et al. Qwen-image-2.0-rl technical report. arXiv preprint arXiv:2606.27608, 2026. Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang. Mmada: Multimodal large diffusion language models. Advances in Neural Information Processing Systems, 38:138867–138907, 2026. Shuai Yang, Yuying Ge, Yang Li, Yukang Chen, Yixiao Ge, Ying Shan, and Ying-Cong Chen. Seed-story: Multimodal long story ge","arxiv_match":false},{"id":"vwvDGT3GUw","title":"When Anchors Commit, Coherence Breaks","forum_url":"https://openreview.net/forum?id=vwvDGT3GUw","excerpt":"iwei Wang. Unveiling the potential of diffusion large language model in controllable generation. arXiv preprint arXiv:2507.04504, 2025. Haojin Yang, Rui Hu, Zequn Sun, Rui Zhou, Yujun Cai, and Yiwei Wang. Wavefrontdiffusion: Dynamic decoding schedule for improved reasoning. arXiv preprint arXiv:2511.19473, 2025a. Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang. Mmada: Multimodal large diffusion language models. arXiv preprint arXiv:2505.15809, 2025b. Lin Yao. Remask, don’t replace: Token-to-mask refinement in masked diffusion language model","arxiv_match":true}],"id":"2505.15809","metadata_source":"Cached arXiv API metadata","rank":991},{"title":"MonST3R: A simple approach for estimating geometry in the presence of motion","arxiv_id":"2410.03825","year":2024,"url":"https://arxiv.org/abs/2410.03825","citing_submissions":101,"explicit_arxiv_matches":30,"examples":[{"id":"dhCe4Klcq7","title":"FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute","forum_url":"https://openreview.net/forum?id=dhCe4Klcq7","excerpt":" pp. 12–22, 2023. Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splatting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 19447–19456, 2024. Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang. Monst3r: A simple approach for estimating geometry in the presence of motion. arXiv preprint arXiv:2410.03825, 2024. Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Min","arxiv_match":true},{"id":"dmWiArTSoB","title":"Validation over Contexts: Robust Long-Context Inference for 3D Reconstruction Models","forum_url":"https://openreview.net/forum?id=dmWiArTSoB","excerpt":"la Dai. Scannet++: A highfidelity dataset of 3d indoor scenes. In ICCV, 2023. Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang. Monst3r: A simple approach for estimating geometry in the presence of motion. ICLR, 2025. Junyi Zhang, Charles Herrmann, Junhwa Hur, Chen Sun, Ming-Hsuan Yang, Forrester Cole, Trevor Darrell, and Deqing Sun. Loger: Long-context geometric reconstruction with hybrid memory. ECC","arxiv_match":false},{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":" Xu, and Hanli Wang. Learning 4D geometric priors for inference-efficient world action models. arXiv preprint arXiv:2607.05468, 2026a. URL https://arxiv.org/abs/2607.05468. Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming-Hsuan Yang. MonST3R: A simple approach for estimating geometry in the presence of motion. In International Conference on Learning Representations, 2025b. URL https://arxiv.org/abs/2410.03825. Zhanguang Zhang, Zhiyuan Li, Behnam Rahmati, Rui Heng Yang, Yintao Ma, Amir Rasouli, Sajjad Pakdam","arxiv_match":true}],"id":"2410.03825","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":992},{"title":"RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts","arxiv_id":"2411.15114","year":2024,"url":"https://arxiv.org/abs/2411.15114","citing_submissions":101,"explicit_arxiv_matches":51,"examples":[{"id":"CxFAO6u9vL","title":"Dr.RL-Bench: Diagnosing Reinforcement Learning Runs with AI Agents","forum_url":"https://openreview.net/forum?id=CxFAO6u9vL","excerpt":"rischler, and Matthew Hausknecht. ALFWorld: Aligning text and embodied environments for interactive learning. In International Conference on Learning Representations, 2021. URL https://arxiv.org/ abs/2010.03768. Hjalmar Wijk, Tao Lin, Joel Becker, Sami Jawhar, Neev Parikh, Thomas Broadley, Lawrence Chan, Michael Chen, Josh Clymer, Jai Dhyani, et al. Re-bench: Evaluating frontier ai r&d capabilities of language model agents against human experts. arXiv preprint arXiv:2411.15114, 2024. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, et al. Qwen3 technic","arxiv_match":true},{"id":"UxFvKRhZHz","title":"Do LLMs See the Gorilla in the Data? A Benchmark for Off-Objective Scientific Discovery","forum_url":"https://openreview.net/forum?id=UxFvKRhZHz","excerpt":"ani, Elena Ericheva, Katharyn Garcia, Brian Goodrich, Nikola Jurkovic, Megan Kinniment, Aron Lajko, Seraphina Nix, Lucas Jun Koba Sato, William Saunders, Maksym Taran, Ben West, and Elizabeth Barnes. RE-Bench: Evaluating frontier AI r&d capabilities of language model agents against human experts. In International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 66772–66832. PMLR, 2025. Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris ","arxiv_match":false},{"id":"QIfIKZ1qGX","title":"CAES: Cost-Aware Experiment Selection via Online Shadow Prices for Autonomous Research","forum_url":"https://openreview.net/forum?id=QIfIKZ1qGX","excerpt":"ani, Elena Ericheva, Katharyn Garcia, Brian Goodrich, Nikola Jurkovic, Megan Kinniment, Aron Lajko, Seraphina Nix, Lucas Jun Koba Sato, William Saunders, Maksym Taran, Ben West, and Elizabeth Barnes. RE-bench: Evaluating frontier AI r&d capabilities of language model agents against human experts. In Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, and Jerry Zhu (eds.), Proceedings of the 42nd International Conference on Machine Lear","arxiv_match":false}],"id":"2411.15114","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":993},{"title":"Simplified state space layers for sequence modeling","arxiv_id":"2208.04933","year":2022,"url":"https://arxiv.org/abs/2208.04933","citing_submissions":101,"explicit_arxiv_matches":33,"examples":[{"id":"Lo2xo0dCVF","title":"Decoding Beyond Hemodynamic Delay: Delay-Coupled Graph Dynamics for fMRI-to-Image Reconstruction","forum_url":"https://openreview.net/forum?id=Lo2xo0dCVF","excerpt":"dings of the International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 44038–44059, 2024. Jimmy T. H. Smith, Andrew Warrington, and Scott W. Linderman. Simplified state space layers for sequence modeling. In International Conference on Learning Representations, 2023. Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representat","arxiv_match":false},{"id":"GnVjZ6jp1F","title":"DO STATE SPACE MODELS NEED SEPARATE B AND C PROJECTIONS? REVISITING B-C TYING FROM S4D TO MAMBA","forum_url":"https://openreview.net/forum?id=GnVjZ6jp1F","excerpt":"he output embedding to improve language models. Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2017. Smith, J. T. H., Warrington, A., and Linderman, S. W. Simplified state space layers for sequence modeling. International Conference on Learning Representations (ICLR), 2023. Shazeer, N. Fast transformer decoding: One write-head is all you need. arXiv:1911.02150, 2019. A arXiv preprint M EMORY CAPACITY: E","arxiv_match":false},{"id":"nWwfthpfjE","title":"ViS: Vision State-Space Token Mixer","forum_url":"https://openreview.net/forum?id=nWwfthpfjE","excerpt":"on mamba with non-causal state space duality. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10819–10829. IEEE, 2025. Jimmy TH Smith, Andrew Warrington, and Scott Linderman. Simplified state space layers for sequence modeling. In The Eleventh International Conference on Learning Representations, 2023. Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary posi","arxiv_match":false}],"id":"2208.04933","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":994},{"title":"Unified Vision-Language-Action Model","arxiv_id":"2506.19850","year":2025,"url":"https://arxiv.org/abs/2506.19850","citing_submissions":101,"explicit_arxiv_matches":17,"examples":[{"id":"xE1Rd5Luma","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","forum_url":"https://openreview.net/forum?id=xE1Rd5Luma","excerpt":"ynamics with heterogeneous masked autoregression. arXiv preprint arXiv:2502.04296, 2025. Yuqi Wang, Xinghang Li, Wenxuan Wang, Junbo Zhang, Yingyan Li, Yuntao Chen, Xinlong Wang, and Zhaoxiang Zhang. Unified vision-language-action model. In International Conference on Learning Representations, volume 2026, pp. 80929–80944, 2026b. Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and ","arxiv_match":false},{"id":"Y8naMKuOst","title":"TARGET-VLA: Mask-Guided Visual Retrieval for Robotic Manipulation","forum_url":"https://openreview.net/forum?id=Y8naMKuOst","excerpt":"onference on Computer Vision and Pattern Recognition (CVPR), pp. 5261–5271. IEEE, 2025b. Yuqi Wang, Xinghang Li, Wenxuan Wang, Junbo Zhang, Yingyan Li, Yuntao Chen, Xinlong Wang, and Zhaoxiang Zhang. Unified vision-language-action model. In International Conference on Learning Representations, volume 2026, pp. 80929–80944, 2026d. Wei Wu, Fan Lu, Yunnan Wang, Shuai Yang, Shi Liu, Fangjing Wang, Qian Zhu, He Sun, Yong Wang, Shuailei M","arxiv_match":false},{"id":"4OYtK4X3En","title":"Acting in Meters: Learning Metric Interactions for Precise Robotic Manipulation","forum_url":"https://openreview.net/forum?id=4OYtK4X3En","excerpt":"icies with 3d scene flow as latent motion prior. arXiv preprint arXiv:2603.25399, 2026b. Yuqi Wang, Xinghang Li, Wenxuan Wang, Junbo Zhang, Yingyan Li, Yuntao Chen, Xinlong Wang, and Zhaoxiang Zhang. Unified vision-language-action model. In International Conference on Learning Representations, volume 2026, pp. 80929–80944, 2026c. Zhi Wang, Botao He, Kelin Yu, Seungjae Lee, Ruohan Gao, Furong Huang, and Yiannis Aloimonos. HumanEgo: Z","arxiv_match":false}],"id":"2506.19850","metadata_source":"Cached arXiv API metadata","rank":995},{"title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","arxiv_id":"2401.09417","year":2024,"url":"https://arxiv.org/abs/2401.09417","citing_submissions":101,"explicit_arxiv_matches":32,"examples":[{"id":"0M6c2bonFJ","title":"QuEST: Quality-guided Evidence Selection and Temporal State Control for Event-based Object Detection","forum_url":"https://openreview.net/forum?id=0M6c2bonFJ","excerpt":" features. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6023–6032, 2019. Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision Mamba: Efficient visual representation learning with bidirectional state space model. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 62429–62442. PMLR, 2024. URL https: //proceedings.mlr.press/v235/","arxiv_match":false},{"id":"DDFEBjXIO2","title":"Width Expansion via Parallel Block Interaction for Vision Model Training","forum_url":"https://openreview.net/forum?id=DDFEBjXIO2","excerpt":" Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 633–641, 2017. Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. ArXiv preprint, abs/2401.09417, 2024. URL https://arxiv.org/abs/2401.09417. A D ISCUSSION ON R ESTORING L ATERAL C ONNECTIONS AT THE B LOCK L EVEL PBI restores lateral inform","arxiv_match":true},{"id":"qcXYhyyzVO","title":"Vision Fluxer with Dynamic Global Receptive Field","forum_url":"https://openreview.net/forum?id=qcXYhyyzVO","excerpt":"ng of scenes through the ADE20K dataset. International Journal of Computer Vision (IJCV), 127:302–321, 2019. 6, A7 Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision Mamba: Efficient visual representation learning with bidirectional state space model. In Proceedings of International Conference on Machine Learning (ICML), 2024. 2, 8, A10, A11 A We prove the two theorems of Section 3 and collect the propositions it cites. Throughout, F denotes the ","arxiv_match":false}],"id":"2401.09417","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":996},{"title":"Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking","arxiv_id":"2505.24857","year":2025,"url":"https://arxiv.org/abs/2505.24857","citing_submissions":100,"explicit_arxiv_matches":31,"examples":[{"id":"oE5oVgTl3z","title":"PRICE: Learning What to Recompute for Reasoning in Diffusion Language Models","forum_url":"https://openreview.net/forum?id=oE5oVgTl3z","excerpt":"Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021b. URL https://arxiv. org/abs/2108.07732. Heli Ben-Hamu, Itai Gat, Daniel Severo, Niklas S. Nolte, and Brian Karrer. Accelerated sampling from masked diffusion models via entropy bounded unmasking. In Advances in Neural Information Processing Systems, volume 38, 2025. URL https://proceedings.neurips.cc/paper_files/paper/2025/hash/ 510e22f4a2da5212a64f1591736e2eaf-Abstract-Conference.html. Akas","arxiv_match":false},{"id":"6zSuqyB4a9","title":"What Makes a Good Decoding Path? Selecting Parallel-Decodable Trajectories for Diffusion Large Language Models","forum_url":"https://openreview.net/forum?id=6zSuqyB4a9","excerpt":"n The Fourteenth International Conference on Learning Representations, 2026. URL https: //openreview.net/forum?id=XjcHRIu0iF. Heli Ben-Hamu, Itai Gat, Daniel Severo, Niklas S Nolte, and Brian Karrer. Accelerated sampling from masked diffusion models via entropy bounded unmasking. Advances in Neural Information Processing Systems, 38:55981–56007, 2026. Tiwei Bie, Maosong Cao, Kun Chen, Lun Du, Mingliang Gong, Zhuochen Gong, Yanmei Gu, Jiaqi Hu, Zenan Huang, Zhenzhong Lan, et ","arxiv_match":false},{"id":"9GDStxR3kP","title":"Inter-Trajectory Dependence Limits Answer Coverage in Local dLLM Repair","forum_url":"https://openreview.net/forum?id=9GDStxR3kP","excerpt":" via hierarchical search and self-verification for discrete diffusion language models. arXiv preprint arXiv:2602.01842, 2026. Heli Ben-Hamu, Itai Gat, Daniel Severo, Niklas S Nolte, and Brian Karrer. Accelerated sampling from masked diffusion models via entropy bounded unmasking. In D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, and N. Chen (eds.), Advances in Neural Information Processing Systems, volume 38, pp. 55981–56007. Curran Associates, Inc., 202","arxiv_match":false}],"id":"2505.24857","metadata_source":"Cached arXiv API metadata","rank":997},{"title":"Detecting pretraining data from large language models","arxiv_id":"2310.16789","year":2023,"url":"https://arxiv.org/abs/2310.16789","citing_submissions":100,"explicit_arxiv_matches":30,"examples":[{"id":"OllKu9vgU9","title":"Router-Free Modular Storage for Knowledge Unlearning in Large Language Models","forum_url":"https://openreview.net/forum?id=OllKu9vgU9","excerpt":" adapters and deletable user proxies, 2026. URL https://arxiv.org/abs/2604.21571. Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. Detecting pretraining data from large language models. In The Twelfth International Conference on Learning Representations, 2024a. URL https://openreview. net/forum?id=zWqr3MQuNs. Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari","arxiv_match":false},{"id":"M82NayqkbE","title":"Dataset Inference via Cumulative Evidence","forum_url":"https://openreview.net/forum?id=M82NayqkbE","excerpt":"ety, 184(2):407–431, 2021. Shubhanshu Shekhar and Aaditya Ramdas. Nonparametric two-sample testing by betting. IEEE Transactions on Information Theory, 70(2):1178–1203, 2023. Weijia Shi. Bookmia dataset. huggingface, 2023. datasets/swj0419/BookMIA. URL https://huggingface.co/ Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. Detecting pretraining data from large language models. arXiv preprint arXiv:2310.16789, 2023. Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference atta","arxiv_match":true},{"id":"tifLEoFKw2","title":"Black-Box Membership Inference Attack for Vision-Language Models Using Self-Memory Probing","forum_url":"https://openreview.net/forum?id=tifLEoFKw2","excerpt":"f the Association for Computational Linguistics: NAACL 2025, pp. 4165–4182, 2025. Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. Detecting pretraining data from large language models. In The Twelfth International Conference on Learning Representations, 2024. Keisuke Shirai, Cristian C Beltran-Hernandez, Masashi Hamaya, Atsushi Hashimoto, Shohei Tanaka, Kento Kawaharazuka, Kazutos","arxiv_match":false}],"id":"2310.16789","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":998},{"title":"Gr-2: A generative video-language-action model with webscale knowledge for robot manipulation","arxiv_id":"2410.06158","year":2024,"url":"https://arxiv.org/abs/2410.06158","citing_submissions":100,"explicit_arxiv_matches":93,"examples":[{"id":"0FBmlQimVO","title":"What Should a World-Action Model Imagine? 4D Geometry as an Intermediate Representation for Control","forum_url":"https://openreview.net/forum?id=0FBmlQimVO","excerpt":"24. doi: 10.52202/079017-4411. URL https://proceedings.neurips.cc/paper_files/paper/2024/hash/ fad8962279154544ed69bb63eb14d677-Abstract-Conference.html. Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, Hanbo Zhang, and Minzhao Zhu. GR-2: A generative video-language-action model with web-scale knowledge for robot manipulation. arXiv preprint arXiv:2410.06158, 2024. URL https://arxiv.org/abs/2410.06158. Sili Chen, Hengkai Guo, Shengnan Zhu, Feihu Zhang, Zilong Huang, Jiashi Feng, and Bingyi Kang. Vid","arxiv_match":true},{"id":"ubiZuUXpr6","title":"Copper-Policy: Focus on the Representation for Robust Robot Manipulation","forum_url":"https://openreview.net/forum?id=ubiZuUXpr6","excerpt":"Jun Guo, Xinze He, Piaopiao Jin, Jie Li, Bingxuan Lin, Futeng Liu, Wei Liu, Fei Ma, Kun Ma, et al. Xiaomi-robotics-0: An open-sourced vision-language-action model with real-time execution. arXiv preprint arXiv:2602.12684, 2026b. Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, et al. Gr-2: A generative video-language-action model with webscale knowledge for robot manipulation. arXiv preprint arXiv:2410.06158, 2024. Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Zixuan Li, Qiwei Liang, Xian","arxiv_match":true},{"id":"REE0TB9Fde","title":"PINT: A Unified Model with Shared Predictive Intent for Robot Manipulation","forum_url":"https://openreview.net/forum?id=REE0TB9Fde","excerpt":"ngjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, et al. WorldVLA: Towards autoregressive action world model. arXiv preprint arXiv:2506.21539, 2025. URL https://arxiv.org/abs/2506.21539. Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, et al. GR-2: A generative video-language-action model with webscale knowledge for robot manipulation. arXiv preprint arXiv:2410.06158, 2024. Jialei Chen, Kai Wang, Kang Chen, Shuaihang Chen, Feng Gao, Wenhao Tang, Zhiyuan Li, Weilin ","arxiv_match":true}],"id":"2410.06158","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":999},{"title":"Hopfield networks is all you need","arxiv_id":"2008.02217","year":2020,"url":"https://arxiv.org/abs/2008.02217","citing_submissions":100,"explicit_arxiv_matches":43,"examples":[{"id":"npqZc5b0ap","title":"A Local Associative Memory that Knows Where It Holds","forum_url":"https://openreview.net/forum?id=npqZc5b0ap","excerpt":"ard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Thomas Adler, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter. Hopfield networks is all you need. In International Conference on Learning Representations (ICLR), 2021. URL https://openreview.net/forum? id=tL89RnzIiCd. Douglas L. Reilly, Leon N. Cooper, and Charles Elbaum. A neural model for cate","arxiv_match":false},{"id":"fBc4oexOI4","title":"Taming the Edge of Chaos: The Dynamical Origin of Prompt Robustness in LLMs","forum_url":"https://openreview.net/forum?id=fBc4oexOI4","excerpt":" Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024. URL https://arxiv.org/abs/2412.15115. Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geofrey K Sandner, et al. Hopfield networks is all you need. arXiv preprint arXiv:2008.02217, 2020. Michael E Sander, Pierre Ablin, Mathieu Blondel, and Gabriel Peyré. Sinkformers: Transformer","arxiv_match":true},{"id":"92SEw5FtLg","title":"PREM: Prefix-Steered Recurrent Memory for Long-Video Understanding","forum_url":"https://openreview.net/forum?id=92SEw5FtLg","excerpt":"nhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber, Markus Holzleitner, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter. Hopfield networks is all you need. In International Conference on Learning Representations, 2021. Imanol Schlag, Kazuki Irie, and Jürgen Schmidhuber. Linear transformers are secretly fast weight programmers. In International Conferen","arxiv_match":false}],"id":"2008.02217","metadata_source":"cached citation catalog or recurring bibliography title; release year inferred from arXiv identifier","rank":1000}]}