Agentic Reasoning for Large Language Models
♢ Foundations · Evolution · Collaboration ♢
大規模言語モデルのためのエージェント推論
♢ 基礎・進化・コラボレーション ♢
References 参考文献
-
[1] 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.
- [2] Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625, 2022.
- [3] Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models. In International Conference on Machine Learning, pages 10764–10799. PMLR, 2023.
- [4] Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Grifiths, 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.
- [5] 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.
- [6] Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, 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.
- [7] Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face. Advances in Neural Information Processing Systems, 36:38154–38180, 2023.
- [8] Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al. A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6):186345, 2024.
- [9] Aditi Singh, Abul Ehtesham, Saket Kumar, and Tala Talaei Khoei. Agentic retrieval-augmented generation: A survey on agentic rag. arXiv preprint arXiv:2501.09136, 2025.
- [10] Yizheng Huang and Jimmy Huang. A survey on retrieval-augmented text generation for large language models. arXiv preprint arXiv:2404.10981, 2024.
- [11] Xingyao Wang, Boxuan Li, Yufan Song, Frank F Xu, Xiangru Tang, Mingchen Zhuge, Jiayi Pan, Yueqi Song, Bowen Li, Jaskirat Singh, et al. Openhands: An open platform for ai software developers as generalist agents. arXiv preprint arXiv:2407.16741, 2024.
74
Agentic Reasoning for Large Language Models
- [12] 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.
- [13] Zhiyu Li, 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.
- [14] 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.
- [15] Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Kristian Kersting, Jeff Z. Pan, Hinrich Schütze, Volker Tresp, and Yunpu Ma. Memory-r1: Enhancing large language model agents to manage and utilize memories via reinforcement learning. arXiv preprint arXiv:2508.19828, 2025.
- [16] Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F Karlsson, Jie Fu, and Yemin Shi. Autoagents: A framework for automatic agent generation. arXiv preprint arXiv:2309.17288, 2023.
- [17] Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, 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. In The Twelfth International Conference on Learning Representations, 2024. URL https: //openreview.net/forum?id=VtmBAGCN7o.
- [18] Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. Unleashing cogni-tive synergy in large language models: A task-solving agent through multi-persona self-collaboration. In Proc. 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL2024), 2024.
- [19] Wei Wang, Dan Zhang, Tao Feng, Boyan Wang, and Jie Tang. Battleagentbench: A benchmark for evaluating cooperation and competition capabilities of language models in multi-agent systems. arXiv preprint arXiv:2408.15971, 2024.
- [20] Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, 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. arXiv preprint arXiv:2308.03688, 2023. URL https://www.arxiv.org/ abs/2308.03688.
- [21] Kunlun Zhu, Hongyi Du, Zhaochen Hong, Xiaocheng Yang, Shuyi Guo, Zhe Wang, Zhenhailong Wang, Cheng Qian, Xiangru Tang, Heng Ji, et al. Multiagentbench: Evaluating the collaboration and competition of llm agents. arXiv preprint arXiv:2503.01935, 2025.
- [22] Ziyi Ni, Yifan Li, Ning Yang, Dou Shen, Pin Lyu, and Daxiang Dong. Tree-of-code: A self-growing tree framework for end-to-end code generation and execution in complex tasks. In Findings of the Association for Computational Linguistics: ACL 2025, pages 9804–9819, 2025.
- [23] 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, 2025.
75
Agentic Reasoning for Large Language Models
- [24] Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, and Yongfeng Zhang. A-mem: Agentic memory for llm agents. arXiv preprint arXiv:2502.12110, 2025.
- [25] Tianxin Wei, Noveen Sachdeva, Benjamin Coleman, Zhankui He, Yuanchen Bei, Xuying Ning, Mengting Ai, Yunzhe Li, Jingrui He, Ed H Chi, et al. Evo-memory: Benchmarking llm agent test-time learning with self-evolving memory. arXiv preprint arXiv:2511.20857, 2025.
- [26] Hao Ma, Tianyi Hu, Zhiqiang Pu, Liu Boyin, Xiaolin Ai, Yanyan Liang, and Min Chen. Coevolving with the other you: Fine-tuning llm with sequential cooperative multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 37:15497–15525, 2024.
- [27] 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.
- [28] Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, et al. Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning. arXiv preprint arXiv:2505.16421, 2025.
- [29] Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. Solving olympiad geometry without human demonstrations. Nature, 625(7995):476–482, 2024.
- [30] Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al. Mathematical discoveries from program search with large language models. Nature, 625(7995): 468–475, 2024.
- [31] Ranjan Sapkota, Konstantinos I Roumeliotis, and Manoj Karkee. Vibe coding vs. agentic coding: Fundamentals and practical implications of agentic AI, 2025.
- [32] Andrej Karpathy. Vibe coding — wikipedia. https://en.wikipedia.org/wiki/Vibe_coding, 2025.
- [33] Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. Chemcrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376, 2023.
- [34] Fouad Bousetouane. Physical ai agents: Integrating cognitive intelligence with real-world action. arXiv preprint arXiv:2501.08944, 2025.
- [35] Qianggang Ding, Santiago Miret, and Bang Liu. Matexpert: Decomposing materials discovery by mimicking human experts. arXiv preprint arXiv:2410.21317, 2024.
- [36] 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.
- [37] Booker Meghan, Byrd Grayson, Kemp Bethany, Schmidt Aurora, and Rivera Corban. Embodiedrag: Dynamic 3d scene graph retrieval for eficient and scalable robot task planning. arXiv preprint arXiv:2410.23968, 2024. URL https://www.arxiv.org/abs/2410.23968.
76
Agentic Reasoning for Large Language Models
- [38] Baining Zhao, Ziyou Wang, Jianjie Fang, Chen Gao, Fanhang Man, Jinqiang Cui, Xin Wang, Xinlei Chen, Yong Li, and Wenwu Zhu. Embodied-r: Collaborative framework for activating embodied spatial reasoning in foundation models via reinforcement learning. arXiv preprint arXiv:2504.12680, 2025.
- [39] Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, et al. Mmedagent: Learning to use medical tools with multi-modal agent. arXiv preprint arXiv:2407.02483, 2024.
- [40] Kexin Huang, Serena Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Yusuf Roohani, Ryan Li, Lin Qiu, Gavin Li, Junze Zhang, et al. Biomni: A general-purpose biomedical ai agent. biorxiv, 2025.
- [41] Kuan Li, Zhongwang Zhang, Huifeng Yin, Liwen Zhang, Litu Ou, Jialong Wu, Wenbiao Yin, Baixuan Li, Zhengwei Tao, Xinyu Wang, et al. Websailor: Navigating super-human reasoning for web agent. arXiv preprint arXiv:2507.02592, 2025.
- [42] 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.
- [43] Ranjan Sapkota, Konstantinos I Roumeliotis, and Manoj Karkee. Ai agents vs. agentic ai: A conceptual taxonomy, applications and challenges. arXiv preprint arXiv:2505.10468, 2025.
- [44] 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, 2024.
- [45] Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig. Webarena: A realistic web environment for building autonomous agents. arXiv preprint arXiv:2307.13854, 2023. URL https://www.arxiv. org/abs/2307.13854.
- [46] Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Gra-ham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, and Daniel Fried. Visualwebarena: Evaluat-ing multimodal agents on realistic visual web tasks. arXiv preprint arXiv:2401.13649, 2024. URL https://www.arxiv.org/abs/2401.13649.
- [47] Lawrence Jang, Yinheng Li, Dan Zhao, Charles Ding, Justin Lin, Paul Pu Liang, Rogerio Bonatti, and Kazuhito Koishida. Videowebarena: Evaluating long context multimodal agents with video understanding web tasks. arXiv preprint arXiv:2410.19100, 2024.
- [48] 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.
- [49] 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.
- [50] Boyu Gou, Zanming Huang, Yuting Ning, Yu Gu, Michael Lin, Weijian Qi, Andrei Kopanev, Botao Yu, Bernal Jiménez Gutiérrez, Yiheng Shu, et al. Mind2web 2: Evaluating agentic search with agent-as-a-judge. arXiv preprint arXiv:2506.21506, 2025.
77
Agentic Reasoning for Large Language Models
- [51] Jie Huang and Kevin Chen-Chuan Chang. Towards reasoning in large language models: A survey. arXiv preprint arXiv:2212.10403, 2022.
- [52] Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wanxiang Che. Towards reasoning era: A survey of long chain-of-thought for reasoning large language models. arXiv preprint arXiv:2503.09567, 2025.
- [53] Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, et al. Towards large reasoning models: A survey of reinforced reasoning with large language models. arXiv preprint arXiv:2501.09686, 2025.
- [54] Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, et al. A survey of frontiers in llm reasoning: Inference scaling, learning to reason, and agentic systems. arXiv preprint arXiv:2504.09037, 2025.
- [55] Kaiyan Zhang, Yuxin Zuo, Bingxiang He, Youbang 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, 2025.
- [56] Guibin Zhang, Hejia Geng, Xiaohang Yu, Zhenfei Yin, Zaibin Zhang, Zelin Tan, Heng Zhou, Zhongzhi Li, Xiangyuan Xue, Yijiang Li, et al. The landscape of agentic reinforcement learning for llms: A survey. arXiv preprint arXiv:2509.02547, 2025.
- [57] Minhua Lin, Zongyu Wu, Zhichao Xu, Hui Liu, Xianfeng Tang, Qi He, Charu Aggarwal, Xiang Zhang, and Suhang Wang. A comprehensive survey on reinforcement learning-based agentic search: Foundations, roles, optimizations, evaluations, and applications. arXiv preprint arXiv:2510.16724, 2025.
- [58] Jinyuan Fang, Yanwen Peng, Xi Zhang, Yingxu Wang, Xinhao Yi, Guibin Zhang, Yi Xu, Bin Wu, Siwei Liu, Zihao Li, et al. A comprehensive survey of self-evolving ai agents: A new paradigm bridging foundation models and lifelong agentic systems. arXiv preprint arXiv:2508.07407, 2025.
- [59] 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, 2025.
- [60] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025.
- [61] Pengcheng Jiang, Jiacheng Lin, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, and Jiawei Han. Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning. arXiv preprint arXiv:2503.00223, 2025.
- [62] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
- [63] 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.
78
Agentic Reasoning for Large Language Models
- [64] Fanbin Lu, Zhisheng Zhong, Shu Liu, Chi-Wing Fu, and Jiaya Jia. Arpo: End-to-end policy optimization for gui agents with experience replay. arXiv preprint arXiv:2505.16282, 2025.
- [65] 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. arXiv preprint arXiv:2503.14476, 2025.
- [66] 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 conversations. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=BAakY1hNKS.
- [67] Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Commu-nicative agents for" mind" exploration of large language model society. Advances in Neural Information Processing Systems, 36:51991–52008, 2023.
- [68] Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmid-huber. Gptswarm: Language agents as optimizable graphs. In Forty-first International Conference on Machine Learning, 2024.
- [69] Haoyang Hong, Jiajun Yin, Yuan Wang, Jingnan Liu, Zhe Chen, Ailing Yu, Ji Li, Zhiling Ye, Hansong Xiao, Yefei Chen, et al. Multi-agent deep research: Training multi-agent systems with m-grpo. arXiv preprint arXiv:2511.13288, 2025.
- [70] Alexander Novikov, Ngân Vu, 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.
˜
- [71] Binfeng Xu, Zhiyuan Peng, Bowen Lei, Subhabrata Mukherjee, Yuchen Liu, and Dongkuan Xu. REWOO: Decoupling reasoning from observations for eficient augmented language models. arXiv preprint arXiv:2305.18323, 2023.
- [72] Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. LLM+P: Empowering large language models with optimal planning proficiency. arXiv preprint arXiv:2304.11477, 2023.
- [73] Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati. On the planning abilities of large language models: A critical investigation. Advances in Neural Information Processing Systems, 36:75993–76005, 2023.
- [74] Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pages 17682–17690, 2024.
- [75] Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, and Ming Jin. Algorithm of thoughts: Enhancing exploration of ideas in large language models. arXiv preprint arXiv:2308.10379, 2023.
- [76] Runquan Gui, Zhihai Wang, Jie Wang, Chi Ma, Huiling Zhen, Mingxuan Yuan, Jianye Hao, Defu Lian, Enhong Chen, and Feng Wu. Hypertree planning: Enhancing llm reasoning via hierarchical thinking. arXiv preprint arXiv:2505.02322, 2025.
79
Agentic Reasoning for Large Language Models
- [77] Jihwan Jeong, Xiaoyu Wang, Jingmin Wang, Scott Sanner, and Pascal Poupart. Reflect-then-plan: Ofline model-based planning through a doubly bayesian lens. arXiv preprint arXiv:2506.06261, 2025.
- [78] Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. Gorilla: Large language model connected with massive apis. Advances in Neural Information Processing Systems, 37:126544–126565, 2024.
- [79] Tanmay Gupta, Luca Weihs, and Aniruddha Kembhavi. Codenav: Beyond tool-use to using real-world codebases with llm agents. arXiv preprint arXiv:2406.12276, 2024.
- [80] Liyi Chen, Panrong Tong, Zhongming Jin, Ying Sun, Jieping Ye, and Hui Xiong. Plan-on-graph: Self-correcting adaptive planning of large language model on knowledge graphs. Advances in Neural Information Processing Systems, 37:37665–37691, 2024.
- [81] Yanming Liu, Xinyue Peng, Jiannan Cao, Yuwei Zhang, Xuhong Zhang, Sheng Cheng, Xun Wang, Jianwei Yin, and Tianyu Du. Tool-planner: Task planning with clusters across multiple tools. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview. net/forum?id=dRz3cizftU.
- [82] Yichao Liang, Nishanth Kumar, Hao Tang, Adrian Weller, Joshua B Tenenbaum, Tom Silver, João F Henriques, and Kevin Ellis. Visualpredicator: Learning abstract world models with neuro-symbolic predicates for robot planning. arXiv preprint arXiv:2410.23156, 2024.
- [83] Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su. Llm-planner: Few-shot grounded planning for embodied agents with large language models. In Proceedings of the IEEE/CVF international conference on computer vision, pages 2998–3009, 2023.
- [84] Tamer Abuelsaad, Deepak Akkil, Prasenjit Dey, Ashish Jagmohan, Aditya Vempaty, and Ravi Kokku. Agent-e: From autonomous web navigation to foundational design principles in agentic systems. arXiv preprint arXiv:2407.13032, 2024.
- [85] Saaket Agashe, Jiuzhou Han, Shuyu Gan, Jiachen Yang, Ang Li, and Xin Eric Wang. Agent s: An open agentic framework that uses computers like a human. arXiv preprint arXiv:2410.08164, 2024.
- [86] Minjong Yoo, Jinwoo Jang, Wei-Jin Park, and Honguk Woo. Exploratory retrieval-augmented planning for continual embodied instruction following. Advances in Neural Information Processing Systems, 37: 67034–67060, 2024.
- [87] Rohan Sinha, Amine Elhafsi, Christopher Agia, Matthew Foutter, Edward Schmerling, and Marco Pavone. Real-time anomaly detection and reactive planning with large language models. arXiv preprint arXiv:2407.08735, 2024.
- [88] Cristina Cornelio, Flavio Petruzzellis, and Pietro Lio. Hierarchical planning for complex tasks with knowledge graph-rag and symbolic verification. arXiv preprint arXiv:2504.04578, 2025.
- [89] Zikang Zhou, HU Haibo, Xinhong Chen, Jianping Wang, Nan Guan, Kui Wu, Yung-Hui Li, Yu-Kai Huang, and Chun Jason Xue. Behaviorgpt: Smart agent simulation for autonomous driving with next-patch prediction. Advances in Neural Information Processing Systems, 37:79597–79617, 2024.
- [90] Gaoyue Zhou, Hengkai Pan, Yann LeCun, and Lerrel Pinto. Dino-wm: World models on pre-trained visual features enable zero-shot planning. arXiv preprint arXiv:2411.04983, 2024.
80
Agentic Reasoning for Large Language Models
- [91] Chongkai Gao, Haozhuo Zhang, Zhixuan Xu, Zhehao Cai, and Lin Shao. Flip: Flow-centric generative planning as general-purpose manipulation world model. arXiv preprint arXiv:2412.08261, 2024.
- [92] Shibo Hao, Yi Gu, Haotian Luo, Tianyang Liu, Xiyan Shao, Xinyuan Wang, Shuhua Xie, Haodi Ma, Adithya Samavedhi, Qiyue Gao, et al. LLM reasoners: New evaluation, library, and analysis of step-by-step reasoning with large language models. arXiv preprint arXiv:2404.05221, 2024.
- [93] Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2609–2634, 2023.
- [94] Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, and Zheng Zhang. Plan, verify and switch: Integrated reasoning with diverse x-of-thoughts. arXiv preprint arXiv:2310.14628, 2023.
- [95] Fei Ni, Jianye Hao, Shiguang Wu, Longxin Kou, Yifu Yuan, Zibin Dong, Jinyi Liu, MingZhi Li, Yuzheng Zhuang, and Yan Zheng. Peria: Perceive, reason, imagine, act via holistic language and vision planning for manipulation. Advances in Neural Information Processing Systems, 37:17541–17571, 2024.
- [96] Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Anumanchipalli, Kurt Keutzer, and Amir Gholami. Plan-and-act: Improving planning of agents for long-horizon tasks. arXiv preprint arXiv:2503.09572, 2025.
- [97] Jiaxin Wen, Jian Guan, Hongning Wang, Wei Wu, and Minlie Huang. Codeplan: Unlocking reasoning potential in large language models by scaling code-form planning. In The Thirteenth International Conference on Learning Representations, 2024.
- [98] Michael Lutz, Arth Bohra, Manvel Saroyan, Artem Harutyunyan, and Giovanni Campagna. Wilbur: Adaptive in-context learning for robust and accurate web agents. arXiv preprint arXiv:2404.05902, 2024.
- [99] Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. Executable code actions elicit better llm agents. In Forty-first International Conference on Machine Learning, 2024.
- [100] Asif Rahman, Veljko Cvetkovic, Kathleen Reece, Aidan Walters, Yasir Hassan, Aneesh Tummeti, Bryan Torres, Denise Cooney, Margaret Ellis, and Dimitrios S Nikolopoulos. Marco: Multi-agent code optimization with real-time knowledge integration for high-performance computing. arXiv preprint arXiv:2505.03906, 2025.
- [101] Chengbo He, Bochao Zou, Xin Li, Jiansheng Chen, Junliang Xing, and Huimin Ma. Enhancing llm reasoning with multi-path collaborative reactive and reflection agents. arXiv preprint arXiv:2501.00430, 2024.
- [102] Mrinal Rawat, Ambuje Gupta, Rushil Goomer, Alessandro Di Bari, Neha Gupta, and Roberto Pier-accini. Pre-act: Multi-step planning and reasoning improves acting in llm agents. arXiv preprint arXiv:2505.09970, 2025.
- [103] Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, et al. Rest meets react: Self-improvement for multi-step reasoning llm agent. arXiv preprint arXiv:2312.10003, 2023.
81
Agentic Reasoning for Large Language Models
- [104] Xue Jiang, Yihong Dong, Lecheng Wang, Zheng Fang, Qiwei Shang, Ge Li, Zhi Jin, and Wenpin Jiao. Self-planning code generation with large language models. ACM Transactions on Software Engineering and Methodology, 33(7):1–30, 2024.
- [105] Dhruv Shah, Błażej Osiński, Sergey Levine, et al. Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. In Conference on robot learning, pages 492–504. PMLR, 2023.
- [106] Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, and Aram Galstyan. Tree-of-traversals: A zero-shot reasoning algorithm for augmenting black-box language models with knowledge graphs. arXiv preprint arXiv:2407.21358, 2024.
- [107] Jieyi Long. Large language model guided tree-of-thought. arXiv preprint arXiv:2305.08291, 2023.
- [108] Jing Yu Koh, Stephen McAleer, Daniel Fried, and Ruslan Salakhutdinov. Tree search for language model agents. arXiv preprint arXiv:2407.01476, 2024.
- [109] Chaojie Wang, Yanchen Deng, Zhiyi Lyu, Liang Zeng, Jujie He, Shuicheng Yan, and Bo An. Q*: Improving multi-step reasoning for llms with deliberative planning. arXiv preprint arXiv:2406.14283, 2024.
- [110] Silin Meng, Yiwei Wang, Cheng-Fu Yang, Nanyun Peng, and Kai-Wei Chang. Llm-a*: Large language model enhanced incremental heuristic search on path planning. arXiv preprint arXiv:2407.02511, 2024.
- [111] Gang Liu, Michael Sun, Wojciech Matusik, Meng Jiang, and Jie Chen. Multimodal large language models for inverse molecular design with retrosynthetic planning. arXiv preprint arXiv:2410.04223, 2024.
- [112] Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. Reasoning with language model is planning with world model. arXiv preprint arXiv:2305.14992, 2023.
- [113] Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, and Rafael Rafailov. Agent q: Advanced reasoning and learning for autonomous ai agents. arXiv preprint arXiv:2408.07199, 2024.
- [114] Henry W Sprueill, Carl Edwards, Mariefel V Olarte, Udishnu Sanyal, Heng Ji, and Sutanay Choudhury. Monte carlo thought search: Large language model querying for complex scientific reasoning in catalyst design. arXiv preprint arXiv:2310.14420, 2023.
- [115] Xiao Yu, Maximillian Chen, and Zhou Yu. Prompt-based monte-carlo tree search for goal-oriented dialogue policy planning. arXiv preprint arXiv:2305.13660, 2023.
- [116] Zirui Zhao, Wee Sun Lee, and David Hsu. Large language models as commonsense knowledge for large-scale task planning. Advances in neural information processing systems, 36:31967–31987, 2023.
- [117] Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang. Everything of thoughts: Defying the law of penrose triangle for thought generation. arXiv preprint arXiv:2311.04254, 2023.
- [118] Ziru Chen, Michael White, Raymond Mooney, Ali Payani, Yu Su, and Huan Sun. When is tree search useful for llm planning? it depends on the discriminator. arXiv preprint arXiv:2402.10890, 2024.
82
Agentic Reasoning for Large Language Models
- [119] Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, and Ying Nian Wu. Latent plan transformer for trajectory abstraction: Planning as latent space inference. Advances in Neural Information Processing Systems, 37:123379–123401, 2024.
- [120] Xidong Feng, Ziyu Wan, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, and Jun Wang. Alphazero-like tree-search can guide large language model decoding and training. arXiv preprint arXiv:2309.17179, 2023.
- [121] Jaesik Yoon, Hyeonseo Cho, Doojin Baek, Yoshua Bengio, and Sungjin Ahn. Monte carlo tree diffusion for system 2 planning. arXiv preprint arXiv:2502.07202, 2025.
- [122] John Schultz, Jakub Adamek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada Lewis, Anian Ruoss, et al. Mastering board games by external and internal planning with language models. arXiv preprint arXiv:2412.12119, 2024.
- [123] Zhiliang Chen, Xinyuan Niu, Chuan-Sheng Foo, and Bryan Kian Hsiang Low. Broaden your scope! efi-cient multi-turn conversation planning for llms with semantic space. arXiv preprint arXiv:2503.11586, 2025.
- [124] Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, James Xu Zhao, Min-Yen Kan, Junxian He, and Michael Xie. Self-evaluation guided beam search for reasoning. Advances in Neural Information Processing Systems, 36:41618–41650, 2023.
- [125] Olga Golovneva, Sean O’Brien, Ramakanth Pasunuru, Tianlu Wang, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz. Pathfinder: Guided search over multi-step reasoning paths. arXiv preprint arXiv:2312.05180, 2023.
- [126] Haofu Qian, Chenjia Bai, Jiatao Zhang, Fei Wu, Wei Song, and Xuelong Li. Discriminator-guided em-bodied planning for llm agent. In The Thirteenth International Conference on Learning Representations, 2025.
- [127] Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, and Noah D Goodman. Stream of search (sos): Learning to search in language. arXiv preprint arXiv:2404.03683, 2024.
- [128] Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen, Peter Hase, Elias Stengel-Eskin, and Mohit Bansal. System-1. x: Learning to balance fast and slow planning with language models. arXiv preprint arXiv:2407.14414, 2024.
- [129] Yanchu Guan, Dong Wang, Zhixuan Chu, Shiyu Wang, Feiyue Ni, Ruihua Song, Longfei Li, Jinjie Gu, and Chenyi Zhuang. Intelligent virtual assistants with llm-based process automation. arXiv preprint arXiv:2312.06677, 2023.
- [130] Junjie Chen, Haitao Li, Jingli Yang, Yiqun Liu, and Qingyao Ai. Enhancing llm-based agents via global planning and hierarchical execution. arXiv preprint arXiv:2504.16563, 2025.
- [131] Zican Hu, Wei Liu, Xiaoye Qu, Xiangyu Yue, Chunlin Chen, Zhi Wang, and Yu Cheng. Divide and conquer: Grounding llms as eficient decision-making agents via ofline hierarchical reinforcement learning. arXiv preprint arXiv:2505.19761, 2025.
83
Agentic Reasoning for Large Language Models
- [132] Antonis Antoniades, Albert Örwall, Kexun Zhang, Yuxi Xie, Anirudh Goyal, and William Wang. Swe-search: Enhancing software agents with monte carlo tree search and iterative refinement. arXiv preprint arXiv:2410.20285, 2024.
- [133] Artem Lykov and Dzmitry Tsetserukou. Llm-brain: Ai-driven fast generation of robot behaviour tree based on large language model. In 2024 2nd International Conference on Foundation and Large Language Models (FLLM), pages 392–397. IEEE, 2024.
- [134] Yue Cao and CS Lee. Robot behavior-tree-based task generation with large language models. arXiv preprint arXiv:2302.12927, 2023.
- [135] Riccardo Andrea Izzo, Gianluca Bardaro, and Matteo Matteucci. Btgenbot: Behavior tree generation for robotic tasks with lightweight llms. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 9684–9690. IEEE, 2024.
- [136] 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. arXiv preprint arXiv:2204.01691, 2022.
- [137] 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.
- [138] Lin Guan, Karthik Valmeekam, Sarath Sreedharan, and Subbarao Kambhampati. Leveraging pre-trained large language models to construct and utilize world models for model-based task planning. Advances in Neural Information Processing Systems, 36:79081–79094, 2023.
- [139] Sadegh Mahdavi, Raquel Aoki, Keyi Tang, and Yanshuai Cao. Leveraging environment interaction for automated pddl translation and planning with large language models. Advances in Neural Information Processing Systems, 37:38960–39008, 2024.
- [140] Michael Katz, Harsha Kokel, Kavitha Srinivas, and Shirin Sohrabi Araghi. Thought of search: Planning with language models through the lens of eficiency. Advances in Neural Information Processing Systems, 37:138491–138568, 2024.
- [141] Yilun Hao, Yang Zhang, and Chuchu Fan. Planning anything with rigor: General-purpose zero-shot planning with llm-based formalized programming. arXiv preprint arXiv:2410.12112, 2024.
- [142] Kaustubh Vyas, Damien Graux, Yijun Yang, Sébastien Montella, Chenxin Diao, Wendi Zhou, Pavlos Vougiouklis, Ruofei Lai, Yang Ren, Keshuang Li, et al. From an llm swarm to a pddl-empowered hive: Planning self-executed instructions in a multi-modal jungle. arXiv preprint arXiv:2412.12839, 2024.
- [143] Yuji Zhang, Qingyun Wang, Cheng Qian, Jiateng Liu, Chenkai Sun, Denghui Zhang, Tarek Abdelzaher, Chengxiang Zhai, Preslav Nakov, and Heng Ji. Atomic reasoning for scientific table claim verification. arXiv preprint arXiv:2506.06972, 2025.
- [144] Zibin Dong, Jianye Hao, Yifu Yuan, Fei Ni, Yitian Wang, Pengyi Li, and Yan Zheng. Diffuserlite: Towards real-time diffusion planning. Advances in Neural Information Processing Systems, 37:122556– 122583, 2024.
84
Agentic Reasoning for Large Language Models
- [145] Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott M Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, and Martha White. Goal-space planning with subgoal models. Journal of Machine Learning Research, 25(330):1–57, 2024.
- [146] Ao Li, Yuexiang Xie, Songze Li, Fugee Tsung, Bolin Ding, and Yaliang Li. Agent-oriented planning in multi-agent systems. arXiv preprint arXiv:2410.02189, 2024.
- [147] Mianchu Wang, Rui Yang, Xi Chen, Hao Sun, Meng Fang, and Giovanni Montana. Goplan: Goal-conditioned ofline reinforcement learning by planning with learned models. arXiv preprint arXiv:2310.20025, 2023.
- [148] Chenglong Kang, Xiaoyi Liu, and Fei Guo. Retrointext: A multimodal large language model enhanced framework for retrosynthetic planning via in-context representation learning. In The Thirteenth International Conference on Learning Representations, 2025.
- [149] Jiacheng Ye, Jiahui Gao, Shansan Gong, Lin Zheng, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Beyond autoregression: Discrete diffusion for complex reasoning and planning. arXiv preprint arXiv:2410.14157, 2024.
- [150] Yupeng Zheng, Zebin Xing, Qichao Zhang, Bu Jin, Pengfei Li, Yuhang Zheng, Zhongpu Xia, Kun Zhan, Xianpeng Lang, Yaran Chen, et al. Planagent: A multi-modal large language agent for closed-loop vehicle motion planning. arXiv preprint arXiv:2406.01587, 2024.
- [151] Sid Nayak, Adelmo Morrison Orozco, Marina Have, Jackson Zhang, Vittal Thirumalai, Darren Chen, Aditya Kapoor, Eric Robinson, Karthik Gopalakrishnan, James Harrison, et al. Long-horizon planning for multi-agent robots in partially observable environments. Advances in Neural Information Processing Systems, 37:67929–67967, 2024.
- [152] Tianxin Wei, Ruizhong Qiu, Yifan Chen, Yunzhe Qi, Jiacheng Lin, Wenju Xu, Sreyashi Nag, Ruirui Li, Hanqing Lu, Zhengyang Wang, Chen Luo, Hui Liu, Suhang Wang, Jingrui He, Qi He, and Xianfeng Tang. Robust watermarking for diffusion models: A unified multi-dimensional recipe, 2024.
- [153] Wenxuan Bao, Ruxi Deng, Ruizhong Qiu, Tianxin Wei, Hanghang Tong, and Jingrui He. Latte: Collaborative test-time adaptation of vision-language models in federated learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025.
- [154] Lingjie Chen, Ruizhong Qiu, Siyu Yuan, Zhining Liu, Tianxin Wei, Hyunsik Yoo, Zhichen Zeng, Deqing Yang, and Hanghang Tong. WAPITI: A watermark for finetuned open-source LLMs, 2024.
- [155] Zhining Liu, Ze Yang, Xiao Lin, Ruizhong Qiu, Tianxin Wei, Yada Zhu, Hendrik Hamann, Jingrui He, and Hanghang Tong. Breaking silos: Adaptive model fusion unlocks better time series forecasting. In Proceedings of the 42nd International Conference on Machine Learning, 2025.
- [156] Lihui Liu, Zihao Wang, Ruizhong Qiu, Yikun Ban, Eunice Chan, Yangqiu Song, Jingrui He, and Hanghang Tong. Logic query of thoughts: Guiding large language models to answer complex logic queries with knowledge graphs, 2024.
- [157] Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, and Hanghang Tong. Class-imbalanced graph learning without class rebalancing. In Proceedings of the 41st International Conference on Machine Learning, 2024.
85
Agentic Reasoning for Large Language Models
- [158] Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Yada Zhu, Hendrik Hamann, and Hanghang Tong. AIM: Attributing, interpreting, mitigating data unfairness. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2014–2025, 2024.
- [159] Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, and Hanghang Tong. Topological augmentation for class-imbalanced node classification, 2023.
- [160] Zhichen Zeng, Ruizhong Qiu, Wenxuan Bao, Tianxin Wei, Xiao Lin, Yuchen Yan, Tarek F. Abdelzaher, Jiawei Han, and Hanghang Tong. Pave your own path: Graph gradual domain adaptation on fused Gromov–Wasserstein geodesics, 2025.
- [161] Zhichen Zeng, Ruizhong Qiu, Zhe Xu, Zhining Liu, Yuchen Yan, Tianxin Wei, Lei Ying, Jingrui He, and Hanghang Tong. Graph mixup on approximate Gromov–Wasserstein geodesics. In Proceedings of the 41st International Conference on Machine Learning, 2024.
- [162] Xiao Lin, Zhining Liu, Ze Yang, Gaotang Li, Ruizhong Qiu, Shuke Wang, Hui Liu, Haotian Li, Sumit Keswani, Vishwa Pardeshi, et al. Moralise: A structured benchmark for moral alignment in visual language models, 2025.
- [163] Xiao Lin, Zhining Liu, Dongqi Fu, Ruizhong Qiu, and Hanghang Tong. BackTime: Backdoor attacks on multivariate time series forecasting. In Advances in Neural Information Processing Systems, volume 37, 2024.
- [164] Ruizhong Qiu, Gaotang Li, Tianxin Wei, Jingrui He, and Hanghang Tong. Saffron-1: Safety inference scaling, 2025.
- [165] Ruizhong Qiu, Zhe Xu, Wenxuan Bao, and Hanghang Tong. Ask, and it shall be given: On the Turing completeness of prompting. In 13th International Conference on Learning Representations, 2025.
- [166] Ruizhong Qiu, Weiliang Will Zeng, Hanghang Tong, James Ezick, and Christopher Lott. How eficient is LLM-generated code? A rigorous & high-standard benchmark. In 13th International Conference on Learning Representations, 2025.
- [167] Ruizhong Qiu, Jun-Gi Jang, Xiao Lin, Lihui Liu, and Hanghang Tong. TUCKET: A tensor time series data structure for eficient and accurate factor analysis over time ranges. Proceedings of the VLDB Endowment, 17(13), 2024.
- [168] Ruizhong Qiu, Dingsu Wang, Lei Ying, H Vincent Poor, Yifang Zhang, and Hanghang Tong. Recon-structing graph diffusion history from a single snapshot. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1978–1988, 2023.
- [169] Ruizhong Qiu, Zhiqing Sun, and Yiming Yang. DIMES: A differentiable meta solver for combinatorial optimization problems. In Advances in Neural Information Processing Systems, volume 35, pages 25531–25546, 2022.
- [170] Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Menghai Pan, Zhichen Zeng, Mahashweta Das, and Hanghang Tong. Discrete-state continuous-time diffusion for graph generation. In Advances in Neural Information Processing Systems, volume 37, 2024.
- [171] Ting-Wei Li, Ruizhong Qiu, and Hanghang Tong. Model-free graph data selection under distribution shift, 2025.
86
Agentic Reasoning for Large Language Models
- [172] Jiaru Zou, Yikun Ban, Zihao Li, Yunzhe Qi, Ruizhong Qiu, Ling Yang, and Jingrui He. Transformer copilot: Learning from the mistake log in llm fine-tuning, 2025. URL https://arxiv.org/abs/ 2505.16270.
- [173] Ruizhong Qiu and Hanghang Tong. Gradient compressed sensing: A query-eficient gradient estimator for high-dimensional zeroth-order optimization. In Proceedings of the 41st International Conference on Machine Learning, 2024.
- [174] Hyunsik Yoo, SeongKu Kang, Ruizhong Qiu, Charlie Xu, Fei Wang, and Hanghang Tong. Embracing plasticity: Balancing stability and plasticity in continual recommender systems. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025.
- [175] Hyunsik Yoo, Ruizhong Qiu, Charlie Xu, Fei Wang, and Hanghang Tong. Generalizable recommender system during temporal popularity distribution shifts. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025.
- [176] Hyunsik Yoo, Zhichen Zeng, Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, and Hanghang Tong. Ensuring user-side fairness in dynamic recommender systems. In Proceedings of the ACM on Web Conference 2024, pages 3667–3678, 2024.
- [177] Eunice Chan, Zhining Liu, Ruizhong Qiu, Yuheng Zhang, Ross Maciejewski, and Hanghang Tong. Group fairness via group consensus. In The 2024 ACM Conference on Fairness, Accountability, and Transparency, pages 1788–1808, 2024.
- [178] Ziwei Wu, Lecheng Zheng, Yuancheng Yu, Ruizhong Qiu, John Birge, and Jingrui He. Fair anomaly detection for imbalanced groups, 2024.
- [179] Xinyu He, Jian Kang, Ruizhong Qiu, Fei Wang, Jose Sepulveda, and Hanghang Tong. On the sensitivity of individual fairness: Measures and robust algorithms. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pages 829–838, 2024.
- [180] Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew Margenot, and Hanghang Tong. Networked time series imputation via position-aware graph enhanced variational autoencoders. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2256–2268, 2023.
- [181] Yue Meng and Chuchu Fan. Telograf: Temporal logic planning via graph-encoded flow matching. arXiv preprint arXiv:2505.00562, 2025.
- [182] Ruizhe Zhong, Xingbo Du, Shixiong Kai, Zhentao Tang, Siyuan Xu, Jianye Hao, Mingxuan Yuan, and Junchi Yan. Flexplanner: Flexible 3d floorplanning via deep reinforcement learning in hybrid action space with multi-modality representation. Advances in Neural Information Processing Systems, 37: 49252–49278, 2024.
- [183] Yangning Li, Yinghui Li, Xinyu Wang, Yong Jiang, Zhen Zhang, Xinran Zheng, Hui Wang, Hai-Tao Zheng, Philip S Yu, Fei Huang, et al. Benchmarking multimodal retrieval augmented generation with dynamic vqa dataset and self-adaptive planning agent. arXiv preprint arXiv:2411.02937, 2024.
- [184] Jiaru Zou, Dongqi Fu, Sirui Chen, Xinrui He, Zihao Li, Yada Zhu, Jiawei Han, and Jingrui He. Rag over tables: Hierarchical memory index, multi-stage retrieval, and benchmarking, 2025. URL https://arxiv.org/abs/2504.01346.
87
Agentic Reasoning for Large Language Models
- [185] Shuofei Qiao, Runnan Fang, Ningyu Zhang, Yuqi Zhu, Xiang Chen, Shumin Deng, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. Agent planning with world knowledge model. Advances in Neural Information Processing Systems, 37:114843–114871, 2024.
- [186] Zichen Liu, Guoji Fu, Chao Du, Wee Sun Lee, and Min Lin. Continual reinforcement learning by planning with online world models. arXiv preprint arXiv:2507.09177, 2025.
- [187] Hang Wang, Xin Ye, Feng Tao, Chenbin Pan, Abhirup Mallik, Burhaneddin Yaman, Liu Ren, and Junshan Zhang. Adawm: Adaptive world model based planning for autonomous driving. arXiv preprint arXiv:2501.13072, 2025.
- [188] Yining Ye, Xin Cong, Shizuo Tian, Yujia Qin, Chong Liu, Yankai Lin, Zhiyuan Liu, and Maosong Sun. Rational decision-making agent with internalized utility judgment. arXiv preprint arXiv:2308.12519, 2023.
- [189] Zhenfang Chen, Delin Chen, Rui Sun, Wenjun Liu, and Chuang Gan. Scaling autonomous agents via automatic reward modeling and planning. arXiv preprint arXiv:2502.12130, 2025.
- [190] Max Ruiz Luyten, Antonin Berthon, and Mihaela van der Schaar. Strategic planning: A top-down approach to option generation. In Forty-second International Conference on Machine Learning, 2025.
- [191] Chang Ma, Haiteng Zhao, Junlei Zhang, Junxian He, and Lingpeng Kong. Non-myopic generation of language models for reasoning and planning. arXiv preprint arXiv:2410.17195, 2024.
- [192] Ruiqi Ni, Zherong Pan, and Ahmed H Qureshi. Physics-informed temporal difference metric learning for robot motion planning. arXiv preprint arXiv:2505.05691, 2025.
- [193] Sharath Matada, Luke Bhan, Yuanyuan Shi, and Nikolay Atanasov. Generalizable motion planning via operator learning. arXiv preprint arXiv:2410.17547, 2024.
- [194] Hongjin Su, Shizhe Diao, Ximing Lu, Mingjie Liu, Jiacheng Xu, Xin Dong, Yonggan Fu, Peter Belcak, Hanrong Ye, Hongxu Yin, Yi Dong, Evelina Bakhturina, Tao Yu, Yejin Choi, Jan Kautz, and Pavlo Molchanov. Toolorchestra: Elevating intelligence via eficient model and tool orchestration, 2025. URL https://arxiv.org/abs/2511.21689.
- [195] Amber Xie, Oleh Rybkin, Dorsa Sadigh, and Chelsea Finn. Latent diffusion planning for imitation learning. arXiv preprint arXiv:2504.16925, 2025.
- [196] Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, Ramin Hasani, Mathias Lechner, and Daniela Rus. Safedif-fuser: Safe planning with diffusion probabilistic models. In The Thirteenth International Conference on Learning Representations, 2023.
- [197] Yixiang Shan, Zhengbang Zhu, Ting Long, Liang Qifan, Yi Chang, Weinan Zhang, and Liang Yin. Contradiff: Planning towards high return states via contrastive learning. In The Thirteenth International Conference on Learning Representations, 2025.
- [198] Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li K Wenliang, Elliot Catt, John Reid, Cannada A Lewis, Joel Veness, and Tim Genewein. Amortized planning with large-scale transformers: A case study on chess. Advances in Neural Information Processing Systems, 37: 65765–65790, 2024.
88
Agentic Reasoning for Large Language Models
- [199] Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro. Art: Automatic multi-step reasoning and tool-use for large language models, 2023. URL https://arxiv.org/abs/2303.09014.
- [200] Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong, Xin Zhao, and Ji-Rong Wen. ChatCoT: Tool-augmented chain-of-thought reasoning on chat-based large language models. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14777–14790, Singapore, December 2023. Association for Computational Linguis-tics. doi: 10.18653/v1/2023.findings-emnlp.985. URL https://aclanthology.org/2023. findings-emnlp.985/.
- [201] Yining Lu, Haoping Yu, and Daniel Khashabi. GEAR: Augmenting language models with generalizable and eficient tool resolution. In Yvette Graham and Matthew Purver, editors, Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 112–138, St. Julian’s, Malta, March 2024. Association for Computational Linguistics. doi: 10.18653/v1/2024.eacl-long.7. URL https://aclanthology.org/2024.eacl-long.7/.
- [202] Shirley Wu, Shiyu Zhao, Qian Huang, Kexin Huang, Michihiro Yasunaga, Kaidi Cao, Vassilis N. Ioannidis, Karthik Subbian, Jure Leskovec, and James Zou. Avatar: Optimizing llm agents for tool usage via contrastive reasoning. In Advances in Neural Information Processing Systems, volume 37, pages 25981–26010. Curran Associates, Inc., 2024.
- [203] Yujia Qin, Shihao Liang, Yining 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, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. URL https://openreview.net/ forum?id=dHng2O0Jjr.
- [204] Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, and Le Sun. Toolalpaca: Generalized tool learning for language models with 3000 simulated cases. CoRR, abs/2306.05301, 2023. doi: 10.48550/ARXIV.2306.05301. URL https://doi.org/10.48550/arXiv.2306.05301.
- [205] Mingyang Chen, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang, Jeff Z Pan, Wen Zhang, Huajun Chen, Fan Yang, et al. Learning to reason with search for llms via reinforcement learning. arXiv preprint arXiv:2503.19470, 2025.
- [206] Qingxiu Dong, Li Dong, Yao Tang, Tianzhu Ye, Yutao Sun, Zhifang Sui, and Furu Wei. Reinforcement pre-training. arXiv preprint arXiv:2506.08007, 2025.
- [207] 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.
- [208] Yaobo Liang, Chenfei Wu, Ting Song, Wenshan Wu, Yan Xia, Yu Liu, Yang Ou, Shuai Lu, Lei Ji, Shaoguang Mao, Yun Wang, Linjun Shou, Ming Gong, and Nan Duan. Taskmatrix.ai: Completing tasks by connecting foundation models with millions of apis. CoRR, abs/2303.16434, 2023. doi: 10.48550/ARXIV.2303.16434. URL https://doi.org/10.48550/arXiv.2303.16434.
- [209] Pan Lu, Bowen Chen, Sheng Liu, Rahul Thapa, Joseph Boen, and James Zou. Octotools: An agentic framework with extensible tools for complex reasoning, 2025. URL https://arxiv.org/abs/ 2502.11271.
89
Agentic Reasoning for Large Language Models
- [210] Zijing Zhang, Zhanpeng Chen, He Zhu, Ziyang Chen, Nan Du, and Xiaolong Li. Toolexpnet: Optimizing multi-tool selection in llms with similarity and dependency-aware experience networks. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar, editors, Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025, pages 15706–15722. Association for Computational Linguistics, 2025. URL https://aclanthology. org/2025.findings-acl.811/.
- [211] Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor S. Bursztyn, Ryan A. Rossi, Somdeb Sarkhel, and Chao Zhang. Toolchain*: Eficient action space navigation in large language models with a* search. 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= B6pQxqUcT8.
- [212] Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, and Sadao Kurohashi. MultiTool-CoT: GPT-3 can use multiple external tools with chain of thought prompting. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1522–1532, Toronto, Canada, July 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.acl-short.130. URL https://aclanthology. org/2023.acl-short.130/.
- [213] Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving re-trieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509, 2022.
- [214] Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, and Tomas Pfister. Tool documentation enables zero-shot tool-usage with large language models, 2023. URL https://arxiv.org/abs/2308.00675.
- [215] Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Ren Kan, Dongsheng Li, and Deqing Yang. Easytool: Enhancing llm-based agents with concise tool instruction. arXiv preprint arXiv:2401.06201, 2024.
- [216] Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, and Ji-Rong Wen. Tool learning with large language models: A survey. Frontiers of Computer Science, 19(8): 198343, 2025.
- [217] Zhengliang Shi, Shen Gao, Lingyong Yan, Yue Feng, Xiuyi Chen, Zhumin Chen, Dawei Yin, Suzan Verberne, and Zhaochun Ren. Tool learning in the wild: Empowering language models as automatic tool agents. In Proceedings of the ACM on Web Conference 2025, pages 2222–2237, 2025.
- [218] Hongru Wang, Yujia Qin, Yankai Lin, Jeff Z Pan, and Kam-Fai Wong. Empowering large language models: Tool learning for real-world interaction. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2983–2986, 2024.
- [219] Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E Gonzalez, and Bin Cui. Buffer of thoughts: Thought-augmented reasoning with large language models. Advances in Neural Information Processing Systems, 37:113519–113544, 2024.
- [220] Kanzhi Cheng, Qiushi Sun, Yougang Chu, Fangzhi Xu, Yantao Li, Jianbing Zhang, and Zhiyong Wu. Seeclick: Harnessing gui grounding for advanced visual gui agents. arXiv preprint arXiv:2401.10935, 2024.
90
Agentic Reasoning for Large Language Models
- [221] Jiaru Zou, Ling Yang, Jingwen Gu, Jiahao Qiu, Ke Shen, Jingrui He, and Mengdi Wang. Reasonflux-prm: Trajectory-aware prms for long chain-of-thought reasoning in llms, 2025. URL https://arxiv. org/abs/2506.18896.
- [222] Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers. Using an llm to help with code understanding. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, pages 1–13, 2024.
- [223] Junde Wu, Jiayuan Zhu, Yuyuan Liu, Min Xu, and Yueming Jin. Agentic reasoning: A streamlined framework for enhancing llm reasoning with agentic tools. 2025. URL https://arxiv.org/abs/ 2502.04644.
- [224] Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. Chameleon: Plug-and-play compositional reasoning with large language models. Advances in Neural Information Processing Systems, 36:43447–43478, 2023.
- [225] Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, et al. Restgpt: Connecting large language models with real-world restful apis. arXiv preprint arXiv:2306.06624, 2023.
- [226] Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, and Tushar Khot. Adapt: As-needed decomposition and planning with language models. arXiv preprint arXiv:2311.05772, 2023.
- [227] Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, and Bill Yuchen Lin. Agent lumos: Unified and modular training for open-source language agents. arXiv preprint arXiv:2311.05657, 2023.
- [228] Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Pengjie Ren, Suzan Verberne, and Zhaochun Ren. Learning to use tools via cooperative and interactive agents. arXiv preprint arXiv:2403.03031, 2024.
- [229] Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. Understanding the effects of rlhf on llm generalisation and diversity. arXiv preprint arXiv:2310.06452, 2023.
- [230] Ziniu Li, Congliang Chen, Tian Xu, Zeyu Qin, Jiancong Xiao, Zhi-Quan Luo, and Ruoyu Sun. Preserving diversity in supervised fine-tuning of large language models. arXiv preprint arXiv:2408.16673, 2024.
- [231] Laura O’Mahony, Leo Grinsztajn, Hailey Schoelkopf, and Stella Biderman. Attributing mode collapse in the fine-tuning of large language models. In ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models, volume 2, 2024.
- [232] Yirong Zeng, Xiao Ding, Yuxian Wang, Weiwen Liu, Yutai Hou, Wu Ning, Xu Huang, Duyu Tang, Dandan Tu, Bing Qin, et al. itool: Reinforced fine-tuning with dynamic deficiency calibration for advanced tool use. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13901–13916, 2025.
- [233] Zhaochen Yu, Ling Yang, Jiaru Zou, Shuicheng Yan, and Mengdi Wang. Demystifying reinforcement learning in agentic reasoning. arXiv preprint arXiv:2510.11701, 2025.
91
Agentic Reasoning for Large Language Models
- [234] Yifei Zhou, Song Jiang, Yuandong Tian, Jason Weston, Sergey Levine, Sainbayar Sukhbaatar, and Xian Li. Sweet-rl: Training multi-turn llm agents on collaborative reasoning tasks. arXiv preprint arXiv:2503.15478, 2025.
- [235] 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. arXiv preprint arXiv:2502.18449, 2025.
- [236] Zijing Zhang, Ziyang Chen, Mingxiao Li, Zhaopeng Tu, and Xiaolong Li. Rlvmr: Reinforcement learning with verifiable meta-reasoning rewards for robust long-horizon agents. arXiv preprint arXiv:2507.22844, 2025.
- [237] Jiaru Zou, Ling Yang, Yunzhe Qi, Sirui Chen, Mengting Ai, Ke Shen, Jingrui He, and Mengdi Wang. Autotool: Dynamic tool selection and integration for agentic reasoning, 2025. URL https://arxiv. org/abs/2512.13278.
- [238] 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.
- [239] Hao Sun, Zile Qiao, Jiayan Guo, Xuanbo Fan, Yingyan Hou, Yong Jiang, Pengjun Xie, Yan Zhang, Fei Huang, and Jingren Zhou. Zerosearch: Incentivize the search capability of llms without searching. arXiv preprint arXiv:2505.04588, 2025.
- [240] 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.
- [241] 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.
- [242] Kimi Team, Yifan Bai, Yiping Bao, Guanduo Chen, Jiahao Chen, Ningxin Chen, Ruijue Chen, Yanru Chen, Yuankun Chen, Yutian Chen, et al. Kimi k2: Open agentic intelligence. arXiv preprint arXiv:2507.20534, 2025.
- [243] Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, et al. Glm-4.5: Agentic, reasoning, and coding (arc) foundation models. arXiv preprint arXiv:2508.06471, 2025.
- [244] Jiaru Zou, Soumya Roy, Vinay Kumar Verma, Ziyi Wang, David Wipf, Pan Lu, Sumit Negi, James Zou, and Jingrui He. Tattoo: Tool-grounded thinking prm for test-time scaling in tabular reasoning. arXiv preprint arXiv:2510.06217, 2025.
- [245] Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine, editors, Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023. URL http://papers.nips.cc/paper_files/paper/ 2023/hash/8fd1a81c882cd45f64958da6284f4a3f-Abstract-Conference.html.
92
Agentic Reasoning for Large Language Models
- [246] Zhiyuan Ma, Jiayu Liu, Xianzhen Luo, Zhenya Huang, Qingfu Zhu, and Wanxiang Che. Advanc-ing tool-augmented large language models via meta-verification and reflection learning. CoRR, abs/2506.04625, 2025. doi: 10.48550/ARXIV.2506.04625. URL https://doi.org/10.48550/ arXiv.2506.04625.
- [247] Mengsong Wu, Tong Zhu, Han Han, Xiang Zhang, Wenbiao Shao, and Wenliang Chen. Chain-of-tools: Utilizing massive unseen tools in the cot reasoning of frozen language models. CoRR, abs/2503.16779, 2025. doi: 10.48550/ARXIV.2503.16779. URL https://doi.org/10.48550/ arXiv.2503.16779.
- [248] Shitian Zhao, Haoquan Zhang, Shaoheng Lin, Ming Li, Qilong Wu, Kaipeng Zhang, and Chen Wei. Pyvision: Agentic vision with dynamic tooling. CoRR, abs/2507.07998, 2025. doi: 10.48550/ARXIV. 2507.07998. URL https://doi.org/10.48550/arXiv.2507.07998.
- [249] Yunheng Zou, Austin H. Cheng, Abdulrahman Aldossary, Jiaru Bai, Shi Xuan Leong, Jorge A. Campos Gonzalez Angulo, Changhyeok Choi, Cher Tian Ser, Gary Tom, Andrew Wang, Zijian Zhang, Ilya Yakavets, Han Hao, Chris Crebolder, Varinia Bernales, and Alán Aspuru-Guzik. El agente: An autonomous agent for quantum chemistry. CoRR, abs/2505.02484, 2025. doi: 10.48550/ARXIV.2505.02484. URL https://doi.org/10.48550/arXiv.2505.02484.
- [250] Xing Cui, Yueying Zou, Zekun Li, Pei-Pei Li, Xinyuan Xu, Xuannan Liu, Huaibo Huang, and Ran He. Tˆ2agent A tool-augmented multimodal misinformation detection agent with monte carlo tree search. CoRR, abs/2505.19768, 2025. doi: 10.48550/ARXIV.2505.19768. URL https://doi.org/ 10.48550/arXiv.2505.19768.
- [251] Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu, Jian Luan, and Bin Wang. Toolrerank: Adaptive and hierarchy-aware reranking for tool retrieval. In Nicoletta Calzolari, Min-Yen Kan, Véronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue, editors, Proceedings of the 2024 Joint Interna-tional Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy, pages 16263–16273. ELRA and ICCL, 2024. URL https: //aclanthology.org/2024.lrec-main.1413.
- [252] 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.
- [253] Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Gui, Ziran Jiang, Ziyu Jiang, et al. Crag-comprehensive rag benchmark. Advances in Neural Information Processing Systems, 37:10470–10490, 2024.
- [254] Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. Measuring and narrowing the compositionality gap in language models. arXiv preprint arXiv:2210.03350, 2022.
- [255] Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-rag: Self-reflective retrieval augmented generation. In NeurIPS 2023 workshop on instruction tuning and instruction following, 2023.
- [256] Xinyan Guan, Jiali Zeng, Fandong Meng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, and Jie Zhou. Deeprag: Thinking to retrieve step by step for large language models. arXiv preprint arXiv:2502.01142, 2025.
93
Agentic Reasoning for Large Language Models
- [257] Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, and Zhicheng Dou. Inters: Unlocking the power of large language models in search with instruction tuning. arXiv preprint arXiv:2401.06532, 2024.
- [258] 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.
- [259] Jerry Huang, Siddarth Madala, Risham Sidhu, Cheng Niu, Hao Peng, Julia Hockenmaier, and Tong Zhang. Rag-rl: Advancing retrieval-augmented generation via rl and curriculum learning. arXiv preprint arXiv:2503.12759, 2025.
- [260] 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.
- [261] Zhongxiang Sun, Qipeng Wang, Weijie Yu, Xiaoxue Zang, Kai Zheng, Jun Xu, Xiao Zhang, Yang Song, and Han Li. Rearter: Retrieval-augmented reasoning with trustworthy process rewarding. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1251–1261, 2025.
- [262] Meng-Chieh Lee, Qi Zhu, Costas Mavromatis, Zhen Han, Soji Adeshina, Vassilis N Ioannidis, Huzefa Rangwala, and Christos Faloutsos. Agent-g: An agentic framework for graph retrieval augmented generation.
- [263] Xuying Ning, Dongqi Fu, Tianxin Wei, Mengting Ai, Jiaru Zou, Ting-Wei Li, and Jingrui He. Mc-search: Benchmarking multimodal agentic rag with structured reasoning chains. In NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle: Benchmarks, Emergent Abilities, and Scaling, 2025.
- [264] Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Enting Chen, Damien Graux, Andre Melo, Ruofei Lai, Zeren Jiang, et al. Gear: Graph-enhanced agent for retrieval-augmented generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12049–12072, 2025.
- [265] Han Zhang, Langshi Zhou, and Hanfang Yang. Learning to retrieve and reason on knowledge graph through active self-reflection. arXiv preprint arXiv:2502.14932, 2025.
- [266] Kelong Mao, Zheng Liu, Hongjin Qian, Fengran Mo, Chenlong Deng, and Zhicheng Dou. Rag-studio: Towards in-domain adaptation of retrieval augmented generation through self-alignment. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 725–735, 2024.
- [267] Tianjun Zhang, Shishir G Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E Gonzalez. Raft: Adapting language model to domain specific rag. arXiv preprint arXiv:2403.10131, 2024.
- [268] Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Richard James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, et al. Ra-dit: Retrieval-augmented dual instruction tuning. In The Twelfth International Conference on Learning Representations, 2023.
- [269] Xuan-Phi Nguyen, Shrey Pandit, Senthil Purushwalkam, Austin Xu, Hailin Chen, Yifei Ming, Zixuan Ke, Silvio Savarese, Caiming Xong, and Shafiq Joty. Sfr-rag: Towards contextually faithful llms. arXiv preprint arXiv:2409.09916, 2024.
94
Agentic Reasoning for Large Language Models
- [270] 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.
- [271] Ziqi Wang, Le Hou, Tianjian Lu, Yuexin Wu, Yunxuan Li, Hongkun Yu, and Heng Ji. Enable language models to implicitly learn self-improvement from data. In Proc. The Twelfth International Conference on Learning Representations (ICLR2024), 2024.
- [272] 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. URL https: //openreview.net/forum?id=1PL1NIMMrw.
- [273] 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. URL https://openreview.net/forum?id=YfZ4ZPt8zd.
- [274] Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, and Jie Tang. Agenttuning: Enabling generalized agent abilities for llms. In Findings of the Association for Computational Linguistics: ACL 2024, pages 3053–3077, 2024.
- [275] 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, pages 8003–8017, 2023.
- [276] 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.
- [277] Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. In Advances in Neural Information Processing Systems (NeurIPS), 2023.
- [278] 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. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
- [279] Jiaqi Li, Xinyi Dong, Yang Liu, Zhizhuo Yang, Quansen Wang, Xiaobo Wang, Song-Chun Zhu, Zixia Jia, and Zilong Zheng. Reflectevo: Improving meta introspection of small llms by learning self-reflection. In Findings of the Association for Computational Linguistics (ACL), 2025. URL https: //aclanthology.org/2025.findings-acl.871/.
- [280] Zhi Zheng and Wee Sun Lee. Reasoning-cv: Fine-tuning powerful reasoning llms for knowledge-assisted claim verification. arXiv preprint arXiv:2505.12348, 2025.
- [281] Alan Dao and Thinh Le. Rezero: Enhancing llm search ability by trying one-more-time. arXiv preprint arXiv:2504.11001, 2025.
- [282] Nearchos Potamitis and Akhil Arora. Are retrials all you need? enhancing large language model reasoning without verbalized feedback. arXiv preprint arXiv:2504.12951, 2025.
95
Agentic Reasoning for Large Language Models
- [283] Hung Le, Yue Wang, Akhilesh Deepak Yu, Thanh-Tung Nguyen, Zhiwei Sun, Nan Jiang, Quoc Viet Le, and Steven C. H. Hoi. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
- [284] Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin. Lever: Learning to verify language-to-code generation with execution. In International Conference on Machine Learning, pages 26106–26128. PMLR, 2023.
- [285] 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 International Conference on Learning Representations (ICLR), 2024.
- [286] Danny Driess, Fei Xia, Mehdi SM 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, pages 8469–8488. PMLR, 2023.
- [287] Shelly Bensal, Umar Jamil, Christopher Bryant, Melisa Russak, Kiran Kamble, Dmytro Mozolevskyi, Muayad Ali, and Waseem AlShikh. Reflect, retry, reward: Self-improving llms via reinforcement learning. arXiv preprint arXiv:2505.24726, 2025.
- [288] Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback. 2024.
- [289] Potsawee Manakul, Adian Liusie, and Mark Gales. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. In Proceedings of the 2023 conference on empirical methods in natural language processing, pages 9004–9017, 2023.
- [290] Jishnu Ray Chowdhury and Cornelia Caragea. Zero-shot verification-guided chain of thoughts. arXiv preprint arXiv:2501.13122, 2025.
- [291] Dongxu Zhang, Ning Yang, Jihua Zhu, Jinnan Yang, Miao Xin, and Baoliang Tian. Ascot: An adaptive self-correction chain-of-thought method for late-stage fragility in llms. arXiv preprint arXiv:2508.05282, 2025.
- [292] Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, and Wentao Zhang. Mm-verify: Enhancing multimodal reasoning with chain-of-thought verification. In ACL, 2025. URL https://aclanthology.org/2025.acl-long.689/.
- [293] Charles Packer, Vivian Fang, Shishir G. Patil, Kevin Lin, Sarah Wooders, and Joseph Gonza-lez. Memgpt: Towards llms as operating systems. ArXiv, abs/2310.08560, 2023. URL https: //api.semanticscholar.org/CorpusID:263909014.
- [294] Zi-Yi Dou, Cheng-Fu Yang, Xueqing Wu, Kai-Wei Chang, and Nanyun Peng. Re-rest: Reflection-reinforced self-training for language agents. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15394–15411, 2024.
- [295] LangChain AI. Langchain library. 2023. URL https://www.langchain.com/.
- [296] Jerry Liu. LlamaIndex, 11 2022. URL https://github.com/jerryjliu/llama_index.
96
Agentic Reasoning for Large Language Models
- [297] Wanjun Zhong, Lianghong Guo, Qi-Fei Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. ArXiv, abs/2305.10250, 2023. URL https://api. semanticscholar.org/CorpusID:258741194.
- [298] Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, and Graham Neubig. Agent workflow memory. ArXiv, abs/2409.07429, 2024. URL https://api.semanticscholar.org/CorpusID:272592995.
- [299] Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi Yao, Mengru Wang, Shuofei Qiao, et al. Lightmem: Lightweight and eficient memory-augmented generation. arXiv preprint arXiv:2510.18866, 2025.
- [300] Jiayan Nan, Wenquan Ma, Wenlong Wu, and Yize Chen. Nemori: Self-organizing agent memory inspired by cognitive science. arXiv preprint arXiv:2508.03341, 2025.
- [301] 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. arXiv preprint arXiv:2510.04618, 2025.
- [302] Siru Ouyang, Jun Yan, I 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. arXiv preprint arXiv:2509.25140, 2025.
- [303] Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. Dynamic cheatsheet: Test-time learning with adaptive memory. arXiv preprint arXiv:2504.07952, 2025.
- [304] Kevin Lin, Charlie Snell, Yu Wang, Charles Packer, Sarah Wooders, Ion Stoica, and Joseph Gonzalez. Sleep-time compute: Beyond inference scaling at test-time. ArXiv, abs/2504.13171, 2025. URL https://api.semanticscholar.org/CorpusID:277857467.
- [305] 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, abs/2404.16130, 2024. URL https://api.semanticscholar.org/CorpusID:269363075.
- [306] 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.
- [307] Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen, Dongmei Jiang, and Liqiang Nie. Optimus-1: Hybrid multimodal memory empowered agents excel in long-horizon tasks. Advances in neural information processing systems, 37:49881–49913, 2024.
- [308] Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar, Sugiri Pranata, Akira Kinose, Koki Oguri, Felix Wick, and Yang You. Rap: Retrieval-augmented planning with contextual memory for multimodal llm agents. arXiv preprint arXiv:2402.03610, 2024.
- [309] Lin Long, Yichen He, Wentao Ye, Yiyuan Pan, Yuan Lin, Hang Li, Junbo Zhao, and Wei Li. Seeing, listening, remembering, and reasoning: A multimodal agent with long-term memory. arXiv preprint arXiv:2508.09736, 2025.
- [310] Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, and Hanghang Tong. Mem-gallery: Benchmarking multimodal long-term conversational memory for mllm agents. arXiv preprint arXiv:2601.03515, 2026.
97
Agentic Reasoning for Large Language Models
- [311] Pengzhou Cheng, Lingzhong Dong, Zeng Wu, Zongru Wu, Xiangru Tang, Chengwei Qin, Zhuosheng Zhang, and Gongshen Liu. Agent-scankit: Unraveling memory and reasoning of multimodal agents via sensitivity perturbations. arXiv preprint arXiv:2510.00496, 2025.
- [312] 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 eficient long-horizon agents. arXiv preprint arXiv:2506.15841, 2025.
- [313] Yuqiang Zhang, Jiangming Shu, Ye Ma, Xueyuan Lin, Shangxi Wu, and Jitao Sang. Memory as action: Autonomous context curation for long-horizon agentic tasks. arXiv preprint arXiv:2510.12635, 2025. URL https://arxiv.org/abs/2510.12635.
- [314] Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, et al. Memagent: Reshaping long-context llm with multi-conv rl-based memory agent. arXiv preprint arXiv:2507.02259, 2025.
- [315] Yu Wang, Ryuichi Takanobu, Zhiqi Liang, Yuzhen Mao, Yuanzhe Hu, Julian McAuley, and Xiaojian Wu. Mem-{\alpha}: Learning memory construction via reinforcement learning. arXiv preprint arXiv:2509.25911, 2025.
- [316] Kai Zhang, Xiangchao Chen, Bo Liu, Tianci Xue, Zeyi Liao, Zhihan Liu, Xiyao Wang, Yuting Ning, Zhaorun Chen, Xiaohan Fu, et al. Agent learning via early experience. arXiv preprint arXiv:2510.08558, 2025.
- [317] 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.
- [318] Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou, Junwei Liao, Yuchen Feng, Weinan Zhang, Ying Wen, Zhiyu Li, Feiyu Xiong, Yutao Qi, et al. Memrl: Self-evolving agents via runtime reinforcement learning on episodic memory. arXiv preprint arXiv:2601.03192, 2026.
- [319] 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. URL https://openreview.net/forum?id=hSyW5go0v8.
- [320] Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, and Hinrich Schütze. Ret-llm: Towards a general read-write memory for large language models. ArXiv, abs/2305.14322, 2023. URL https://api. semanticscholar.org/CorpusID:258841042.
- [321] Bing Wang, Xinnian Liang, Jian Yang, Hui Huang, Zhenhe Wu, ShuangZhi Wu, Zejun Ma, and Zhoujun Li. Scm: Enhancing large language model with self-controlled memory framework. In International Conference on Database Systems for Advanced Applications, pages 188–203. Springer, 2025.
- [322] Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. Evaluating very long-term conversational memory of llm agents. arXiv preprint arXiv:2402.17753, 2024.
- [323] 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.
98
Agentic Reasoning for Large Language Models
- [324] Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, and Deqing Yang. SELFGOAL: Your language agents already know how to achieve high-level goals. In Luis Chiruzzo, Alan Ritter, and Lu Wang, editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 799–819, Albuquerque, New Mexico, April 2025. Association for Computational Linguistics. ISBN 979-8-89176-189-6. doi: 10.18653/v1/2025.naacl-long.36. URL https://aclanthology.org/2025.naacl-long.36/.
- [325] Ajay Patel, Markus Hofmarcher, Claudiu Leoveanu-Condrei, Marius-Constantin Dinu, Chris Callison-Burch, and Sepp Hochreiter. Large language models can self-improve at web agent tasks. ArXiv, abs/2405.20309, 2024. URL https://api.semanticscholar.org/CorpusID:270122967.
- [326] Xiaohe Bo, Zeyu Zhang, Quanyu Dai, Xueyang Feng, Lei Wang, Rui Li, Xu Chen, and Ji-Rong Wen. Reflective multi-agent collaboration based on large language models. Advances in Neural Information Processing Systems, 37:138595–138631, 2024.
- [327] Yangyang Yu, Haohang Li, Zhi Chen, Yuechen Jiang, Yang Li, Denghui Zhang, Rong Liu, Jordan W. Suchow, and Khaldoun Khashanah. Finmem: A performance-enhanced llm trading agent with layered memory and character design. arXiv preprint arXiv:2311.13743, 2023. URL https://www.arxiv. org/abs/2311.13743.
- [328] Yu Wang and Xi Chen. Mirix: Multi-agent memory system for llm-based agents. arXiv preprint arXiv:2507.07957, 2025.
- [329] Siru Ouyang, Wenhao Yu, Kaixin Ma, Zi-Qiang Xiao, Zhihan Zhang, Mengzhao Jia, Jiawei Han, Hongming Zhang, and Dong Yu. Repograph: Enhancing ai software engineering with repository-level code graph. ArXiv, abs/2410.14684, 2024. URL https://api.semanticscholar.org/ CorpusID:273502041.
- [330] Alireza Rezazadeh, Zichao Li, Wei Wei, and Yujia Bao. From isolated conversations to hierarchical schemas: Dynamic tree memory representation for llms. arXiv preprint arXiv:2410.14052, 2024.
- [331] Zelong Li, Shuyuan Xu, Kai Mei, Wenyue Hua, Balaji Rama, Om Raheja, Hao Wang, He Zhu, and Yongfeng Zhang. Autoflow: Automated workflow generation for large language model agents. ArXiv, abs/2407.12821, 2024. URL https://api.semanticscholar.org/CorpusID:271270428.
- [332] 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.
- [333] Zhen Zeng, William Watson, Nicole Cho, Saba Rahimi, Shayleen Reynolds, Tucker Hybinette Balch, and Manuela Veloso. Flowmind: Automatic workflow generation with llms. Proceedings of the Fourth ACM International Conference on AI in Finance, 2023. URL https://api.semanticscholar.org/ CorpusID:265452485.
- [334] Yifei Zhou, Sergey Levine, Jason Weston, Xian Li, and Sainbayar Sukhbaatar. Self-challenging language model agents. arXiv preprint arXiv:2506.01716, 2025.
- [335] 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.
99
Agentic Reasoning for Large Language Models
- [336] Toby Simonds, Kevin Lopez, Akira Yoshiyama, and Dominique Garmier. Self rewarding self improving. arXiv preprint arXiv:2505.08827, 2025.
- [337] Jianqiao Lu, Wanjun Zhong, Wenyong Huang, Yufei Wang, Qi Zhu, Fei Mi, Baojun Wang, Weichao Wang, Xingshan Zeng, Lifeng Shang, et al. Self: Self-evolution with language feedback. arXiv preprint arXiv:2310.00533, 2023.
- [338] 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. arXiv preprint arXiv:2409.12917, 2024.
- [339] 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.
- [340] Tevin Wang and Chenyan Xiong. Autorule: Reasoning chain-of-thought extracted rule-based rewards improve preference learning. arXiv preprint arXiv:2506.15651, 2025.
- [341] Mengkang Hu, Pu Zhao, Can Xu, Qingfeng Sun, Jian-Guang Lou, Qingwei Lin, Ping Luo, and Saravan Rajmohan. Agentgen: Enhancing planning abilities for large language model based agent via environment and task generation. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1, pages 496–507, 2025.
- [342] Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang. Adaplanner: Adaptive planning from feedback with language models. Advances in neural information processing systems, 36: 58202–58245, 2023.
- [343] Maxime Robeyns, Martin Szummer, and Laurence Aitchison. A self-improving coding agent. arXiv preprint arXiv:2504.15228, 2025.
- [344] 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.
- [345] Borui Wang, Kathleen McKeown, and Rex Ying. Dystil: Dynamic strategy induction with large language models for reinforcement learning. arXiv preprint arXiv:2505.03209, 2025.
- [346] Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou. Large language models as tool makers. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=qV83K9d5WB.
- [347] Lifan Yuan, Yangyi Chen, Xingyao Wang, Yi Fung, Hao Peng, and Heng Ji. CRAFT: Customizing LLMs by creating and retrieving from specialized toolsets. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=G0vdDSt9XM.
- [348] Cheng Qian, Chi Han, Yi Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji. CREATOR: Tool creation for disentangling abstract and concrete reasoning of large language models. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6922–6939, Singapore, December 2023. Association for Computational Linguistics. doi: 10.18653/v1/ 2023.findings-emnlp.462. URL https://aclanthology.org/2023.findings-emnlp.462/.
- [349] Georg Wölflein, Dyke Ferber, Daniel Truhn, Ognjen Arandjelović, and Jakob Nikolas Kather. Llm agents making agent tools, 2025. URL https://arxiv.org/abs/2502.11705.
100
Agentic Reasoning for Large Language Models
- [350] Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. Chatdev: Communicative agents for software development. In ACL 2024, pages 15174–15186. Association for Computational Linguistics, 2024.
- [351] Yue Hu, Yuzhu Cai, Yaxin Du, Xinyu Zhu, Xiangrui Liu, Zijie Yu, Yuchen Hou, Shuo Tang, and Siheng Chen. Self-evolving multi-agent collaboration networks for software development. arXiv preprint arXiv:2410.16946, 2024.
- [352] J Gregory Pauloski, Yadu Babuji, Ryan Chard, Mansi Sakarvadia, Kyle Chard, and Ian Foster. Empow-ering scientific workflows with federated agents. arXiv preprint arXiv:2505.05428, 2025.
- [353] Shu-Heng Chen. Agent-based computational finance. In Leigh Tesfatsion and Kenneth L. Judd, editors, Handbook of Computational Economics, volume 3, pages 1245–1293. Elsevier, 2012.
- [354] John C. Hull. Risk Management and Financial Institutions. Wiley, 5th edition, 2018.
- [355] Yuante Li, Xu Yang, Xiao Yang, Minrui Xu, Xisen Wang, Weiqing Liu, and Jiang Bian. R&d-agent-quant: A multi-agent framework for data-centric factors and model joint optimization. CoRR, abs/2505.15155, 2025. doi: 10.48550/ARXIV.2505.15155. URL https://doi.org/10.48550/ arXiv.2505.15155.
- [356] Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, et al. Finrobot: An open-source ai agent platform for financial applications using large language models. arXiv preprint arXiv:2405.14767, 2024.
- [357] Yiying Wang, Xiaojing Li, Binzhu Wang, Yueyang Zhou, Yingru Lin, Han Ji, Hong Chen, Jinshi Zhang, Fei Yu, Zewei Zhao, et al. Peer: Expertizing domain-specific tasks with a multi-agent framework and tuning methods. arXiv preprint arXiv:2407.06985, 2024.
- [358] Yangyang Yu, Zhiyuan Yao, Haohang Li, Zhiyang Deng, Yuechen Jiang, Yupeng Cao, Zhi Chen, Jordan W. Suchow, Zhenyu Cui, Rong Liu, Zhaozhuo Xu, Denghui Zhang, Koduvayur Subbalakshmi, Guojun Xiong, Yueru He, Jimin Huang, Dong Li, and Qianqian Xie. Fincon: A synthesized LLM multi-agent system with conceptual verbal reinforcement for enhanced financial decision making. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tom-czak, and Cheng Zhang, editors, Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, De-cember 10 - 15, 2024, 2024. URL http://papers.nips.cc/paper_files/paper/2024/hash/ f7ae4fe91d96f50abc2211f09b6a7e49-Abstract-Conference.html.
- [359] Jingyun Sun, Chengxiao Dai, Zhongze Luo, Yangbo Chang, and Yang Li. Lawluo: A multi-agent collaborative framework for multi-round chinese legal consultation. arXiv preprint arXiv:2407.16252, 2024.
- [360] Albert Sadowski, JarosĹ Chudziak, et al. On verifiable legal reasoning: A multi-agent framework with formalized knowledge representations. arXiv preprint arXiv:2509.00710, 2025.
- [361] Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Hamid Alinejad-Rokny, Shiwen Ni, et al. Agentcourt: Simulating court with adversarial evolvable lawyer agents. arXiv preprint arXiv:2408.08089, 2024.
101
Agentic Reasoning for Large Language Models
- [362] Jarosław A Chudziak and Adam Kostka. Ai-powered math tutoring: Platform for personalized and adaptive education. In International Conference on Artificial Intelligence in Education, pages 462–469. Springer, 2025.
- [363] Xueqiao Zhang, Chao Zhang, Jianwen Sun, Jun Xiao, Yi Yang, and Yawei Luo. Eduplanner: Llm-based multi-agent systems for customized and intelligent instructional design. IEEE Transactions on Learning Technologies, 2025.
- [364] Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik S Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, and Hae W Park. Mdagents: An adaptive collaboration of llms for medical decision-making. Advances in Neural Information Processing Systems, 37:79410–79452, 2024.
- [365] Fatemeh Ghezloo, Mehmet Saygin Seyfioglu, Rustin Soraki, Wisdom O Ikezogwo, Beibin Li, Tejoram Vivekanandan, Joann G Elmore, Ranjay Krishna, and Linda Shapiro. Pathfinder: A multi-modal multi-agent system for medical diagnostic decision-making applied to histopathology. arXiv preprint arXiv:2502.08916, 2025.
- [366] Yinghao Zhu, Yifan Qi, Zixiang Wang, Lei Gu, Dehao Sui, Haoran Hu, Xichen Zhang, Ziyi He, Liantao Ma, and Lequan Yu. Healthflow: A self-evolving ai agent with meta planning for autonomous healthcare research. arXiv preprint arXiv:2508.02621, 2025.
- [367] Mingxuan Cui, Yilan Jiang, Duo Zhou, Cheng Qian, Yuji Zhang, and Qiong Wang. Shortagesim: Simulating drug shortages under information asymmetry. arXiv preprint arXiv:2509.01813, 2025.
- [368] Ziyue Wang, Junde Wu, Linghan Cai, Chang Han Low, Xihong Yang, Qiaxuan Li, and Yueming Jin. Medagent-pro: Towards evidence-based multi-modal medical diagnosis via reasoning agentic workflow. arXiv preprint arXiv:2503.18968, 2025.
- [369] Reza Averly, Frazier N Baker, Ian A Watson, and Xia Ning. Liddia: Language-based intelligent drug discovery agent. arXiv preprint arXiv:2502.13959, 2025.
- [370] Zhaolin Hu, Yixiao Zhou, Zhongan Wang, Xin Li, Weimin Yang, Hehe Fan, and Yi Yang. OSDA agent: Leveraging large language models for de novo design of organic structure directing agents. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. URL https://openreview.net/forum?id=9YNyiCJE3k.
- [371] Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu, Jieyu Zhao, Yingzhou Lu, and Yue Zhao. Drugagent: Automating ai-aided drug discovery programming through llm multi-agent collaboration. arXiv preprint arXiv:2411.15692, 2024.
- [372] Haoyang Liu, Yijiang Li, and Haohan Wang. Genomas: A multi-agent framework for scientific discovery via code-driven gene expression analysis. arXiv preprint arXiv:2507.21035, 2025.
- [373] Qixin Deng, Qikai Yang, Ruibin Yuan, Yipeng Huang, Yi Wang, Xubo Liu, Zeyue Tian, Jiahao Pan, Ge Zhang, Hanfeng Lin, et al. Composerx: Multi-agent symbolic music composition with llms. In The 25th International Society for Music Information Retrieval Conference, 2024.
- [374] Wentao Zhang, Liang Zeng, Yuzhen Xiao, Yongcong Li, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, Yahui Zhou, and Bo An. Agentorchestra: Orchestrating multi-agent intelligence with the tool-environment-agent(tea) protocol, 2026. URL https://arxiv.org/abs/2506.12508.
102
Agentic Reasoning for Large Language Models
- [375] Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhitao Zeng, Zhu Zhuo, Evangelos B Mazomenos, and Yueming Jin. Surgraw: Multi-agent workflow with chain-of-thought reasoning for surgical intelligence. arXiv preprint arXiv:2503.10265, 2025.
- [376] Ran Xu, Wenqi Shi, Yuchen Zhuang, Yue Yu, Joyce C Ho, Haoyu Wang, and Carl Yang. Collab-rag: Boosting retrieval-augmented generation for complex question answering via white-box and black-box llm collaboration. arXiv preprint arXiv:2504.04915, 2025.
- [377] Thang Nguyen, Peter Chin, and Yu-Wing Tai. Ma-rag: Multi-agent retrieval-augmented generation via collaborative chain-of-thought reasoning, 2025. URL https://arxiv.org/abs/2505.20096.
- [378] Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang, and Sercan Arik. Chain of agents: Large language models collaborating on long-context tasks. Advances in Neural Information Processing Systems, 37:132208–132237, 2024.
- [379] Hong Qing Yu and Frank McQuade. Rag-kg-il: A multi-agent hybrid framework for reducing hal-lucinations and enhancing llm reasoning through rag and incremental knowledge graph learning integration, 2025. URL https://arxiv.org/abs/2503.13514.
- [380] Dawei Li, Zhen Tan, Peijia Qian, Yifan Li, Kumar Satvik Chaudhary, Lijie Hu, and Jiayi Shen. Smoa: Improving multi-agent large language models with sparse mixture-of-agents, 2024. URL https: //arxiv.org/abs/2411.03284.
- [381] Siwei Han, Peng Xia, Ruiyi Zhang, Tong Sun, Yun Li, Hongtu Zhu, and Huaxiu Yao. Mdocagent: A multi-modal multi-agent framework for document understanding, 2025. URL https://arxiv. org/abs/2503.13964.
- [382] Patara Trirat, Wonyong Jeong, and Sung Ju Hwang. Automl-agent: A multi-agent llm framework for full-pipeline automl. arXiv preprint arXiv:2410.02958, 2024.
- [383] Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Friederike Niedtner, Grace Proebsting, Grifin Bassman, Jack Gerrits, Jacob Alber, et al. Magentic-one: A generalist multi-agent system for solving complex tasks. arXiv preprint arXiv:2411.04468, 2024.
- [384] Rui Ye, Shuo Tang, Rui Ge, Yaxin Du, Zhenfei Yin, Siheng Chen, and Jing Shao. Mas-gpt: Training llms to build llm-based multi-agent systems. arXiv preprint arXiv:2503.03686, 2025.
- [385] Yaolun Zhang, Xiaogeng Liu, and Chaowei Xiao. Metaagent: Automatically constructing multi-agent systems based on finite state machines. arXiv preprint arXiv:2507.22606, 2025.
- [386] Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang, Tianyi Ma, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, and Yanfang Ye. Agentrouter: A knowledge-graph-guided llm router for collaborative multi-agent question answering. arXiv preprint arXiv:2510.05445, 2025.
- [387] Feijie Wu, Zitao Li, Fei Wei, Yaliang Li, Bolin Ding, and Jing Gao. Talk to right specialists: Routing and planning in multi-agent system for question answering. arXiv preprint arXiv:2501.07813, 2025.
- [388] Huao Li, Yu Quan Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Michael Lewis, and Katia Sycara. Theory of mind for multi-agent collaboration via large language models. arXiv preprint arXiv:2310.10701, 2023.
103
Agentic Reasoning for Large Language Models
- [389] Logan Cross, Violet Xiang, Agam Bhatia, Daniel LK Yamins, and Nick Haber. Hypothetical minds: Scaffolding theory of mind for multi-agent tasks with large language models. arXiv preprint arXiv:2407.07086, 2024.
- [390] Mircea Lică, Ojas Shirekar, Baptiste Colle, and Chirag Raman. Mindforge: Empowering embodied agents with theory of mind for lifelong cultural learning. arXiv preprint arXiv:2411.12977, 2024.
- [391] Yuheng Wu, Wentao Guo, Zirui Liu, Heng Ji, Zhaozhuo Xu, and Denghui Zhang. How large language models encode theory-of-mind: a study on sparse parameter patterns. npj Artificial Intelligence, 1(1): 20, 2025.
- [392] Bo Yang, Jiaxian Guo, Yusuke Iwasawa, and Yutaka Matsuo. Large language models as theory of mind aware generative agents with counterfactual reflection. arXiv preprint arXiv:2501.15355, 2025.
- [393] Rikunari Sagara, Koichiro Terao, and Naoto Iwahashi. Beliefnest: A joint action simulator for embodied agents with theory of mind. arXiv preprint arXiv:2505.12321, 2025.
- [394] Arnav Singhvi, Manish Shetty, Shangyin Tan, Christopher Potts, Koushik Sen, Matei Zaharia, and Omar Khattab. Dspy assertions: Computational constraints for self-refining language model pipelines. arXiv preprint arXiv:2312.13382, 2023.
- [395] Han Zhou, Xingchen Wan, Ruoxi Sun, Hamid Palangi, Shariq Iqbal, Ivan Vulić, Anna Korhonen, and Sercan Ö Arık. Multi-agent design: Optimizing agents with better prompts and topologies. arXiv preprint arXiv:2502.02533, 2025.
- [396] Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng. Automatic prompt optimization with" gradient descent" and beam search. arXiv preprint arXiv:2305.03495, 2023.
- [397] Shengchao Hu, Li Shen, Ya Zhang, and Dacheng Tao. Learning multi-agent communication from graph modeling perspective. arXiv preprint arXiv:2405.08550, 2024.
- [398] 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. arXiv preprint arXiv:2410.11782, 2024.
- [399] Xianghua Zeng, Hang Su, Zhengyi Wang, and Zhiyuan Lin. Graph diffusion for robust multi-agent coordination. In Forty-second International Conference on Machine Learning.
- [400] Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeffrey Xu Yu, and Tianlong Chen. Cut the crap: An economical communication pipeline for llm-based multi-agent systems. arXiv preprint arXiv:2410.02506, 2024.
- [401] Boyi Li, Zhonghan Zhao, Der-Horng Lee, and Gaoang Wang. Adaptive graph pruning for multi-agent communication. arXiv preprint arXiv:2506.02951, 2025.
- [402] Shilong Wang, Guibin Zhang, Miao Yu, Guancheng Wan, Fanci Meng, Chongye Guo, Kun Wang, and Yang Wang. G-safeguard: A topology-guided security lens and treatment on llm-based multi-agent systems. arXiv preprint arXiv:2502.11127, 2025.
- [403] Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. Multi-agent architec-ture search via agentic supernet. arXiv preprint arXiv:2502.04180, 2025.
104
Agentic Reasoning for Large Language Models
- [404] Hui Yi Leong and Yuqing Wu. Dynaswarm: Dynamically graph structure selection for llm-based multi-agent system. arXiv preprint arXiv:2507.23261, 2025.
- [405] Yanwei Yue, Guibin Zhang, Boyang Liu, Guancheng Wan, Kun Wang, Dawei Cheng, and Yiyan Qi. Masrouter: Learning to route llms for multi-agent systems. arXiv preprint arXiv:2502.11133, 2025.
- [406] Jun Liu, Zhenglun Kong, Changdi Yang, Fan Yang, Tianqi Li, Peiyan Dong, Joannah Nanjekye, Hao Tang, Geng Yuan, Wei Niu, et al. Rcr-router: Eficient role-aware context routing for multi-agent llm systems with structured memory. arXiv preprint arXiv:2508.04903, 2025.
- [407] Cheng Qian, Zuxin Liu, Shirley Kokane, Akshara Prabhakar, Jielin Qiu, Haolin Chen, Zhiwei Liu, Heng Ji, Weiran Yao, Shelby Heinecke, et al. xrouter: Training cost-aware llms orchestration system via reinforcement learning. arXiv preprint arXiv:2510.08439, 2025.
- [408] Jingbo Wang, Sendong Zhao, Haochun Wang, Yuzheng Fan, Lizhe Zhang, Yan Liu, and Ting Liu. Optimal-agent-selection: State-aware routing framework for eficient multi-agent collaboration. arXiv preprint arXiv:2511.02200, 2025.
- [409] Shuo Liu, Zeyu Liang, Xueguang Lyu, and Christopher Amato. Llm collaboration with multi-agent reinforcement learning. arXiv preprint arXiv:2508.04652, 2025.
- [410] Guanzhong Chen, Shaoxiong Yang, Chao Li, Wei Liu, Jian Luan, and Zenglin Xu. Heterogeneous group-based reinforcement learning for llm-based multi-agent systems. arXiv preprint arXiv:2506.02718, 2025.
- [411] Ziqi Jia, Junjie Li, Xiaoyang Qu, and Jianzong Wang. Enhancing multi-agent systems via reinforcement learning with llm-based planner and graph-based policy. arXiv preprint arXiv:2503.10049, 2025.
- [412] Guobin Zhu, Rui Zhou, Wenkang Ji, and Shiyu Zhao. Lamarl: Llm-aided multi-agent reinforcement learning for cooperative policy generation. IEEE Robotics and Automation Letters, 2025.
- [413] Chanwoo Park, Seungju Han, Xingzhi Guo, Asuman Ozdaglar, Kaiqing Zhang, and Joo-Kyung Kim. Maporl: Multi-agent post-co-training for collaborative large language models with reinforcement learning. arXiv preprint arXiv:2502.18439, 2025.
- [414] Wanjia Zhao, Mert Yuksekgonul, Shirley Wu, and James Zou. Sirius: Self-improving multi-agent systems via bootstrapped reasoning. arXiv preprint arXiv:2502.04780, 2025.
- [415] Vighnesh Subramaniam, Yilun Du, Joshua B Tenenbaum, Antonio Torralba, Shuang Li, and Igor Mordatch. Multiagent finetuning: Self improvement with diverse reasoning chains. arXiv preprint arXiv:2501.05707, 2025.
- [416] Ziyan Wang, Zhicheng Zhang, Fei Fang, and Yali Du. M3hf: Multi-agent reinforcement learning from multi-phase human feedback of mixed quality. arXiv preprint arXiv:2503.02077, 2025.
- [417] The Viet Bui, Tien Mai, and Hong Thanh Nguyen. O-mapl: Ofline multi-agent preference learning. arXiv preprint arXiv:2501.18944, 2025.
- [418] Raja Ben Abdessalem, Shiva Nejati, Lionel C. Briand, and Thomas Stifter. Testing advanced driver assistance systems using multi-objective search and neural networks. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, ASE ’16, page 63–74, New York, NY, USA, 2016. Association for Computing Machinery. ISBN 9781450338455. doi: 10.1145/ 2970276.2970311. URL https://doi.org/10.1145/2970276.2970311.
105
Agentic Reasoning for Large Language Models
- [419] Ziyu Wan, Yunxiang Li, Xiaoyu Wen, Yan Song, Hanjing Wang, Linyi Yang, Mark Schmidt, Jun Wang, Weinan Zhang, Shuyue Hu, et al. Rema: Learning to meta-think for llms with multi-agent reinforcement learning. arXiv preprint arXiv:2503.09501, 2025.
- [420] Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An. Group-in-group policy optimization for llm agent training. arXiv preprint arXiv:2505.10978, 2025.
- [421] Zixuan Ke, Austin Xu, Yifei Ming, Xuan-Phi Nguyen, Caiming Xiong, and Shafiq Joty. Mas-zero: Designing multi-agent systems with zero supervision. arXiv preprint arXiv:2505.14996, 2025.
- [422] 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, pages 19632–19642, 2024.
- [423] Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, Heather Miller, et al. Dspy: Compiling declarative language model calls into state-of-the-art pipelines. In The Twelfth International Conference on Learning Representations, 2024.
- [424] Jiahao Qiu, Xuan Qi, Tongcheng Zhang, Xinzhe Juan, Jiacheng Guo, Yifu Lu, Yimin Wang, Zixin Yao, Qihan Ren, Xun Jiang, et al. Alita: Generalist agent enabling scalable agentic reasoning with minimal predefinition and maximal self-evolution. arXiv preprint arXiv:2505.20286, 2025.
- [425] Guibin Zhang, Muxin Fu, Guancheng Wan, Miao Yu, Kun Wang, and Shuicheng Yan. G-memory: Tracing hierarchical memory for multi-agent systems. arXiv preprint arXiv:2506.07398, 2025.
- [426] Haoran Xu, Jiacong Hu, Ke Zhang, Lei Yu, Yuxin Tang, Xinyuan Song, Yiqun Duan, Lynn Ai, and Bill Shi. Sedm: Scalable self-evolving distributed memory for agents. arXiv preprint arXiv:2509.09498, 2025.
- [427] Alireza Rezazadeh, Zichao Li, Ange Lou, Yuying Zhao, Wei Wei, and Yujia Bao. Collaborative memory: Multi-user memory sharing in llm agents with dynamic access control. arXiv preprint arXiv:2505.18279, 2025.
- [428] Dongge Han, Camille Couturier, Daniel Madrigal Diaz, Xuchao Zhang, Victor Rühle, and Saravan Raj-mohan. Legomem: Modular procedural memory for multi-agent llm systems for workflow automation. arXiv preprint arXiv:2510.04851, 2025.
- [429] Zhao Kaiya, Michelangelo Naim, Jovana Kondic, Manuel Cortes, Jiaxin Ge, Shuying Luo, Guangyu Robert Yang, and Andrew Ahn. Lyfe agents: Generative agents for low-cost real-time social interactions. arXiv preprint arXiv:2310.02172, 2023.
- [430] 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.
- [431] Wenyue Hua, Xianjun Yang, Mingyu Jin, Zelong Li, Wei Cheng, Ruixiang Tang, and Yongfeng Zhang. Trustagent: Towards safe and trustworthy llm-based agents. In Findings of the Association for Compu-tational Linguistics: EMNLP 2024, pages 10000–10016, 2024.
- [432] Adam Zweiger, Jyothish Pari, Han Guo, Ekin Akyürek, Yoon Kim, and Pulkit Agrawal. Self-adapting language models. arXiv preprint arXiv:2506.10943, 2025.
106
Agentic Reasoning for Large Language Models
- [433] Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, et al. Ttrl: Test-time reinforcement learning. arXiv preprint arXiv:2504.16084, 2025.
- [434] Toby Simonds and Akira Yoshiyama. Ladder: Self-improving llms through recursive problem decom-position. arXiv preprint arXiv:2503.00735, 2025.
- [435] Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. Star: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems, 35:15476–15488, 2022.
- [436] Mohamed Amine Ferrag, Norbert Tihanyi, and Merouane Debbah. Reasoning beyond limits: Advances and open problems for llms. arXiv preprint arXiv:2503.22732, 2025.
- [437] Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai, Xueqiao Sun, Wenyi Zhao, Yu Yang, Xinyue Yang, Jiadai Sun, Shuntian Yao, et al. Webrl: Training llm web agents via self-evolving online curriculum reinforcement learning. arXiv preprint arXiv:2411.02337, 2024.
- [438] Jin Hwa Lee, Stefano Sarao Mannelli, and Andrew Saxe. Why do animals need shaping? a theory of task composition and curriculum learning. arXiv preprint arXiv:2402.18361, 2024.
- [439] Xuechen Liang, Meiling Tao, Yinghui Xia, Jianhui Wang, Kun Li, Yijin Wang, Yangfan He, Jingsong Yang, Tianyu Shi, Yuantao Wang, et al. Sage: Self-evolving agents with reflective and memory-augmented abilities. Neurocomputing, page 130470, 2025.
- [440] Rana Salama, Jason Cai, Michelle Yuan, Anna Currey, Monica Sunkara, Yi Zhang, and Yassine Benajiba. Meminsight: Autonomous memory augmentation for llm agents. arXiv preprint arXiv:2503.21760, 2025.
- [441] Jiaru Zou, Xiyuan Yang, Ruizhong Qiu, Gaotang Li, Katherine Tieu, Pan Lu, Ke Shen, Hanghang Tong, Yejin Choi, Jingrui He, James Zou, Mengdi Wang, and Ling Yang. Latent collaboration in multi-agent systems, 2025. URL https://arxiv.org/abs/2511.20639.
- [442] Sizhe Yuen, Francisco Gomez Medina, Ting Su, Yali Du, and Adam J. Sobey. Intrinsic memory agents: Heterogeneous multi-agent llm systems through structured contextual memory. arXiv preprint arXiv:2508.08997, 2025.
- [443] Hanqing Yang, Jingdi Chen, Marie Siew, Tania Lorido-Botran, and Carlee Joe-Wong. Llm-powered decentralized generative agents with adaptive hierarchical knowledge graph for cooperative planning. arXiv preprint arXiv:2502.05453, 2025.
- [444] Hang Gao and Yongfeng Zhang. Memory sharing for large language model based agents. arXiv preprint arXiv:2404.09982, 2024.
- [445] Ye Bai, Minghan Wang, and Thuy-Trang Vu. Maple: Multi-agent adaptive planning with long-term memory for table reasoning. arXiv preprint arXiv:2506.05813, 2025.
- [446] Yixing Chen, Yiding Wang, Siqi Zhu, Haofei Yu, Tao Feng, Muhan Zhang, Mostofa Patwary, and Jiaxuan You. Multi-agent evolve: Llm self-improve through co-evolution, 2025. URL https:// arxiv.org/abs/2510.23595.
- [447] Junwei Liao, Muning Wen, Jun Wang, and Weinan Zhang. Marft: Multi-agent reinforcement fine-tuning, 2025. URL https://arxiv.org/abs/2504.16129.
107
Agentic Reasoning for Large Language Models
- [448] Yujie Zhao, Lanxiang Hu, Yang Wang, Minmin Hou, Hao Zhang, Ke Ding, and Jishen Zhao. Stronger-mas: Multi-agent reinforcement learning for collaborative llms, 2025. URL https://arxiv.org/ abs/2510.11062.
- [449] Natalia Zhang, Xinqi Wang, Qiwen Cui, Runlong Zhou, Sham M Kakade, and Simon S Du. Preference-based multi-agent reinforcement learning: Data coverage and algorithmic techniques. arXiv preprint arXiv:2409.00717, 2024.
- [450] Xufeng Zhao, Mengdi Li, Cornelius Weber, Muhammad Burhan Hafez, and Stefan Wermter. Chat with the environment: Interactive multimodal perception using large language models. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3590–3596. IEEE, 2023.
- [451] Xiangyuan Xue, Yifan Zhou, Guibin Zhang, Zaibin Zhang, Yijiang Li, Chen Zhang, Zhenfei Yin, Philip Torr, Wanli Ouyang, and Lei Bai. Comas: Co-evolving multi-agent systems via interaction rewards, 2025. URL https://arxiv.org/abs/2510.08529.
- [452] Sumeet Ramesh Motwani, Chandler Smith, Rocktim Jyoti Das, Rafael Rafailov, Ivan Laptev, Philip H. S. Torr, Fabio Pizzati, Ronald Clark, and Christian Schroeder de Witt. Malt: Improving reasoning with multi-agent llm training, 2025. URL https://arxiv.org/abs/2412.01928.
- [453] Guoxin Chen, Zile Qiao, Wenqing Wang, Donglei Yu, Xuanzhong Chen, Hao Sun, Minpeng Liao, Kai Fan, Yong Jiang, Penguin Xie, Wayne Xin Zhao, Ruihua Song, and Fei Huang. Mars: Optimizing dual-system deep research via multi-agent reinforcement learning, 2025. URL https://arxiv. org/abs/2510.04935.
- [454] Jingyu Zhang, Haozhu Wang, Eric Michael Smith, Sid Wang, Amr Sharaf, Mahesh Pasupuleti, Ben-jamin Van Durme, Daniel Khashabi, Jason Weston, and Hongyuan Zhan. The alignment waltz: Jointly training agents to collaborate for safety, 2025. URL https://arxiv.org/abs/2510.08240.
- [455] 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.
- [456] 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.
- [457] Mathematical Association of America. American invitational mathematics examination. https: //www.maa.org/math-competitions/aime.
- [458] Elliot Glazer, Ege Erdil, Tamay Besiroglu, Diego Chicharro, Evan Chen, Alex Gunning, Caroline Falkman Olsson, Jean-Stanislas Denain, Anson Ho, Emily de Oliveira Santos, et al. Frontiermath: A benchmark for evaluating advanced mathematical reasoning in ai. arXiv preprint arXiv:2411.04872, 2024.
- [459] Minh-Thang Luong, Dawsen Hwang, Hoang H Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, et al. Towards robust mathematical reasoning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 35406–35430, 2025.
- [460] Grzegorz Swirszcz, Adam Zsolt Wagner, Geordie Williamson, Sam Blackwell, Bogdan Georgiev, Alex Davies, Ali Eslami, Sebastien Racaniere, Theophane Weber, and Pushmeet Kohli. Advancing geometry with ai: Multi-agent generation of polytopes. arXiv preprint arXiv:2502.05199, 2025.
108
Agentic Reasoning for Large Language Models
- [461] Bogdan Georgiev, Javier Gómez-Serrano, Terence Tao, and Adam Zsolt Wagner. Mathematical exploration and discovery at scale. arXiv preprint arXiv:2511.02864, 2025.
- [462] Simon Willison. Not all ai-assisted programming is vibe coding (but vibe coding rocks). https: //simonwillison.net/2025/Mar/19/vibe-coding/, 2025. Blog post.
- [463] Alex Davies, Petar Veličković, Lars Buesing, Sam Blackwell, Daniel Zheng, Nenad Tomašev, Richard Tanburn, Peter Battaglia, Charles Blundell, András Juhász, Marc Lackenby, Geordie Williamson, Demis Hassabis, and Pushmeet Kohli. Advancing mathematics by guiding human intuition with AI. Nature, (7887):70–74, 2021. doi: 10.1038/s41586-021-04086-x.
- [464] Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo, and Shafiq Joty. CodeChain: Towards modular code generation through chain of self-revisions with representative sub-modules. In International Conference on Learning Representations (ICLR), 2023.
- [465] Tao Huang, Zhihong Sun, Zhi Jin, Ge Li, and Chen Lyu. Knowledge-aware code generation with large language models. In IEEE/ACM International Conference on Program Comprehension (ICPC), pages 52–63, 2024.
- [466] Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Vageesh D C, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, Balasubramanyan Ashok, and Shashank Shet. Codeplan: Repository-level coding using llms and planning. Proceedings of the ACM on Software Engineering, 1(FSE):675–698, 2024.
- [467] Yewei Han and Chen Lyu. Multi-stage guided code generation for large language models. Engineering Applications of Artificial Intelligence, 139(PA):109491, 2025.
- [468] Jierui Li, Hung Le, Yingbo Zhou, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. Codetree: Agent-guided tree search for code generation with large language models. arXiv preprint arXiv:2411.04329, 2024.
- [469] Vaibhav Aggarwal, Ojasv Kamal, Abhinav Japesh, Zhijing Jin, and Bernhard Schölkopf. DARS: Dynamic action re-sampling to enhance coding agent performance by adaptive tree traversal, 2025.
- [470] Nicola Dainese, Matteo Merler, Minttu Alakuijala, and Pekka Marttinen. Generating code world models with large language models guided by monte carlo tree search. In Conference on Neural Information Processing Systems (NeurIPS), pages 60429–60474, 2024.
- [471] Chia-Tung Ho, Haoxing Ren, and Brucek Khailany. Verilogcoder: Autonomous Verilog coding agents with graph-based planning and abstract syntax tree (ast)-based waveform tracing tool. In AAAI Conference on Artificial Intelligence (AAAI), volume 39, pages 300–307, 2025.
- [472] Karina Zainullina, Alexander Golubev, Maria Trofimova, Sergei Polezhaev, Ibragim Badertdinov, Daria Litvintseva, Simon Karasik, Filipp Fisin, Sergei Skvortsov, Maksim Nekrashevich, Anton Shevtsov, and Boris Yangel. Guided search strategies in non-serializable environments with applications to software engineering agents. In International Conference on Machine Learning (ICML), 2025.
- [473] Amitayush Thakur, George Tsoukalas, Yeming Wen, Jimmy Xin, and Swarat Chaudhuri. An in-context learning agent for formal theorem-proving. In Conference on Language Models, 2024.
- [474] Kaiyu Yang, Gabriel Poesia, Jingxuan He, Wenda Li, Kristin Lauter, Swarat Chaudhuri, and Dawn Song. Formal mathematical reasoning: A new frontier in AI. arXiv preprint arXiv:2412.16075, 2024.
109
Agentic Reasoning for Large Language Models
- [475] Jordan S Ellenberg, Cristofero S Fraser-Taliente, Thomas R Harvey, Karan Srivastava, and Andrew V Sutherland. Generative modeling for mathematical discovery. arXiv preprint arXiv:2503.11061, 2025.
- [476] AlphaProof and AlphaGeometry teams. AI achieves silver-medal standard solving International Mathematical Olympiad problems, 2024. URL https://deepmind.google/discover/blog/ ai-solves-imo-problems-at-silver-medal-level.
- [477] Kechi Zhang, Huangzhao Zhang, Ge Li, Jia Li, Zhuo Li, and Zhi Jin. ToolCoder: Teach code generation models to use API search tools, 2023.
- [478] Renxi Wang, Xudong Han, Lei Ji, Shu Wang, Timothy Baldwin, and Haonan Li. Toolgen: Unified tool retrieval and calling via generation. In International Conference on Learning Representations (ICLR), 2025.
- [479] Kechi Zhang, Jia Li, Ge Li, Xianjie Shi, and Zhi Jin. Codeagent: Enhancing code generation with tool-integrated agent systems for real-world repo-level coding challenges. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13643–13658, 2024.
- [480] Xue Jiang, Yihong Dong, Yongding Tao, Huanyu Liu, Zhi Jin, Wenpin Jiao, and Ge Li. ROCODE: Inte-grating backtracking mechanism and program analysis in large language models for code generation. In IEEE/ACM International Conference on Software Engineering (ICSE), pages 670–670, 2025.
- [481] Yifei Lu, Fanghua Ye, Jian Li, Qiang Gao, Cheng Liu, Haibo Luo, Nan Du, Xiaolong Li, and Feiliang Ren. CodeTool: Enhancing programmatic tool invocation of LLMs via process supervision, 2025.
- [482] Huy Nhat Phan, Hoang Nhat Phan, Tien N Nguyen, and Nghi DQ Bui. Repohyper: Search-expand-refine on semantic graphs for repository-level code completion, 2024.
- [483] Manish Acharya, Yifan Zhang, Kevin Leach, and Yu Huang. Optimizing code runtime performance through context-aware retrieval-augmented generation. In 2025 IEEE/ACM 33rd International Confer-ence on Program Comprehension (ICPC), pages 1–5. IEEE Computer Society, 2025.
- [484] Mihir Athale and Vishal Vaddina. Knowledge graph based repository-level code generation. In IEEE/ACM International Workshop on Large Language Models for Code (LLM4Code), pages 169–176, 2025.
- [485] Yilin Zhang, Xinran Zhao, Zora Zhiruo Wang, Chenyang Yang, Jiayi Wei, and Tongshuang Wu. cAST: Enhancing code retrieval-augmented generation with structural chunking via abstract syntax tree, 2025.
- [486] Katherine M Collins, Albert Q Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B Tenenbaum, William Hart, et al. Evaluating language models for mathematics through interactions. Proceedings of the National Academy of Sciences, 121(24): e2318124121, 2024.
- [487] Kechi Zhang, Zhuo Li, Jia Li, Ge Li, and Zhi Jin. Self-edit: Fault-aware code editor for code generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 769–787, 2023.
- [488] Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. Is self-repair a silver bullet for code generation?, 2024.
110
Agentic Reasoning for Large Language Models
- [489] Nan Jiang, Xiaopeng Li, Shiqi Wang, Qiang Zhou, Soneya B Hossain, Baishakhi Ray, Varun Kumar, Xiaofei Ma, and Anoop Deoras. Ledex: Training LLMs to better self-debug and explain code. In Neural Information Processing Systems (NeurIPS), pages 35517–35543, 2024.
- [490] Tianyou Chang, Shizhan Chen, Guodong Fan, and Zhiyong Feng. A self-iteration code generation method based on large language models. In International Conference on Parallel and Distributed Systems (ICPADS), pages 275–281, 2023.
- [491] Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug. arXiv preprint arXiv:2304.05128, 2023.
- [492] Yihong Dong, Xue Jiang, Zhi Jin, and Ge Li. Self-collaboration code generation via chatgpt. ACM Transactions on Software Engineering and Methodology, 33(7):1–38, 2024.
- [493] Samuel Holt, Max Ruiz Luyten, and Mihaela van der Schaar. L2MAC: Large language model automatic computer for extensive code generation, 2023.
- [494] Yanlong Li, Jindong Li, Qi Wang, Menglin Yang, He Kong, and Shengsheng Wang. Cogito, ergo sum: A neurobiologically-inspired cognition-memory-growth system for code generation, 2025.
- [495] Dong Huang, Jie M Zhang, Michael Luck, Qingwen Bu, Yuhao Qing, and Heming Cui. AgentCoder: Multi-agent-based code generation with iterative testing and optimisation, 2023.
- [496] Huan Zhang, Wei Cheng, Yuhan Wu, and Wei Hu. A pair programming framework for code generation via multi-plan exploration and feedback-driven refinement. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, pages 1319–1331, 2024.
- [497] Feng Lin, Dong Jae Kim, et al. Soen-101: Code generation by emulating software process models using large language model agents. In International Conference on Software Engineering (ICSE), pages 1527–1539, 2025.
- [498] Yoichi Ishibashi and Yoshimasa Nishimura. Self-organized agents: A LLM multi-agent framework toward ultra large-scale code generation and optimization, 2024.
- [499] Md Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez. Mapcoder: Multi-agent code generation for competitive problem solving. arXiv preprint arXiv:2405.11403, 2024.
- [500] Ana Nunez, Nafis Tanveer Islam, Sumit Kumar Jha, and Peyman Najafirad. Autosafecoder: A multi-agent framework for securing llm code generation through static analysis and fuzz testing, 2024.
- [501] Yaojie Hu, Qiang Zhou, Qihong Chen, Xiaopeng Li, Linbo Liu, Dejiao Zhang, Amit Kachroo, Talha Oz, and Omer Tripp. Qualityflow: An agentic workflow for program synthesis controlled by llm quality checks, 2025.
- [502] Siwei Liu, Jinyuan Fang, Han Zhou, Yingxu Wang, and Zaiqiao Meng. SEW: Self-evolving agentic workflows for automated code generation, 2025.
- [503] Yingwei Ma, Rongyu Cao, Yongchang Cao, Yue Zhang, Jue Chen, Yibo Liu, Yuchen Liu, Binhua Li, Fei Huang, and Yongbin Li. Lingma SWE-GPT: An open development-process-centric language model for automated software improvement, 2024.
- [504] Ruwei Pan, Hongyu Zhang, and Chao Liu. CodeCoR: An llm-based self-reflective multi-agent frame-work for code generation, 2025.
111
Agentic Reasoning for Large Language Models
- [505] Xuehang Guo, Xingyao Wang, Yangyi Chen, Sha Li, Chi Han, Manling Li, and Heng Ji. Syncmind: Measuring agent out-of-sync recovery in collaborative software engineering. In International Conference on Machine Learning (ICML), 2025.
- [506] Qinghua Xu, Guancheng Wang, Lionel Briand, and Kui Liu. Hallucination to consensus: Multi-agent llms for end-to-end test generation, 2025.
- [507] Alireza Ghafarollahi and Markus J Buehler. Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digital Discovery, 3(7):1389– 1409, 2024.
- [508] Mehrad Ansari and Seyed Mohamad Moosavi. Agent-based learning of materials datasets from the scientific literature. Digital Discovery, 3(12):2607–2617, 2024.
- [509] Patrick Tser Jern Kon, Jiachen Liu, Qiuyi Ding, Yiming Qiu, Zhenning Yang, Yibo Huang, Jayanth Srinivasa, Myungjin Lee, Mosharaf Chowdhury, and Ang Chen. Curie: Toward rigorous and automated scientific experimentation with ai agents. arXiv preprint arXiv:2502.16069, 2025.
- [510] Yubo Ma, Zhibin Gou, Junheng Hao, Ruochen Xu, Shuohang Wang, Liangming Pan, Yujiu Yang, Yixin Cao, Aixin Sun, Hany Awadalla, et al. Sciagent: Tool-augmented language models for scientific reasoning. arXiv preprint arXiv:2402.11451, 2024.
- [511] Andrew D McNaughton, Gautham Krishna Sankar Ramalaxmi, Agustin Kruel, Carter R Knutson, Rohith A Varikoti, and Neeraj Kumar. Cactus: Chemistry agent connecting tool usage to science. ACS omega, 9(46):46563–46573, 2024.
- [512] Botao Yu, Frazier N Baker, Ziru Chen, Garrett Herb, Boyu Gou, Daniel Adu-Ampratwum, Xia Ning, and Huan Sun. Chemtoolagent: The impact of tools on language agents for chemistry problem solving. arXiv preprint arXiv:2411.07228, 2024.
- [513] Mengsong Wu, YaFei Wang, Yidong Ming, Yuqi An, Yuwei Wan, Wenliang Chen, Binbin Lin, Yuqiang Li, Tong Xie, and Dongzhan Zhou. Chemagent: Enhancing llms for chemistry and materials science through tree-search based tool learning. arXiv preprint arXiv:2506.07551, 2025.
- [514] Shanghua Gao, Richard Zhu, Zhenglun Kong, Ayush Noori, Xiaorui Su, Curtis Ginder, Theodoros Tsiligkaridis, and Marinka Zitnik. Txagent: An ai agent for therapeutic reasoning across a universe of tools. arXiv preprint arXiv:2503.10970, 2025.
- [515] Qiao Jin, Zhizheng Wang, Yifan Yang, Qingqing Zhu, Donald Wright, Thomas Huang, Nikhil Khandekar, Nicholas Wan, Xuguang Ai, W John Wilbur, et al. Agentmd: Empowering language agents for risk prediction with large-scale clinical tool learning. Nature Communications, 16(1):9377, 2025.
- [516] Jakub Lála, Odhran O’Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G Rodriques, and An-drew D White. Paperqa: Retrieval-augmented generative agent for scientific research. arXiv preprint arXiv:2312.07559, 2023.
- [517] Michael D Skarlinski, Sam Cox, Jon M Laurent, James D Braza, Michaela Hinks, Michael J Hammerling, Manvitha Ponnapati, Samuel G Rodriques, and Andrew D White. Language agents achieve superhuman synthesis of scientific knowledge. arXiv preprint arXiv:2409.13740, 2024.
112
Agentic Reasoning for Large Language Models
- [518] Yuan Chiang, Elvis Hsieh, Chia-Hong Chou, and Janosh Riebesell. Llamp: Large language model made powerful for high-fidelity materials knowledge retrieval and distillation. arXiv preprint arXiv:2401.17244, 2024.
- [519] Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, and Bang Liu. Honeycomb: A flexible llm-based agent system for materials science. arXiv preprint arXiv:2409.00135, 2024.
- [520] Yuanhao Qu, Kaixuan Huang, Ming Yin, Kanghong Zhan, Dyllan Liu, Di Yin, Henry C. Cousins, William A. Johnson, Xiaotong Wang, Mihir Shah, Russ B. Altman, Denny Zhou, Mengdi Wang, and Le Cong. Crispr-gpt for agentic automation of gene-editing experiments, 2025. URL https: //arxiv.org/abs/2404.18021.
- [521] Bowen Gao, Yanwen Huang, Yiqiao Liu, Wenxuan Xie, Wei-Ying Ma, Ya-Qin Zhang, and Yanyan Lan. Pharmagents: Building a virtual pharma with large language model agents. arXiv preprint arXiv:2503.22164, 2025.
- [522] Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, et al. Organa: A robotic assistant for automated chemistry experimentation and characterization. Matter, 8(2), 2025.
- [523] Alireza Ghafarollahi and Markus J Buehler. Atomagents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence. arXiv preprint arXiv:2407.10022, 2024.
- [524] Kexin Chen, Junyou Li, Kunyi Wang, Yuyang Du, Jiahui Yu, Jiamin Lu, Lanqing Li, Jiezhong Qiu, Jianzhang Pan, Yi Huang, et al. Chemist-x: Large language model-empowered agent for reaction condition recommendation in chemical synthesis. arXiv preprint arXiv:2311.10776, 2023.
- [525] Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun, Joshua B Tenenbaum, Daniela Rus, Chuang Gan, and Wojciech Matusik. Llm and simulation as bilevel optimizers: A new paradigm to advance physical scientific discovery. arXiv preprint arXiv:2405.09783, 2024.
- [526] Yihang Xiao, Jinyi Liu, Yan Zheng, Xiaohan Xie, Jianye Hao, Mingzhi Li, Ruitao Wang, Fei Ni, Yuxiao Li, Jintian Luo, et al. Cellagent: An llm-driven multi-agent framework for automated single-cell data analysis. arXiv preprint arXiv:2407.09811, 2024.
- [527] Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora, Zachary Steinhart, Kexin Huang, Alexander Marson, Percy Liang, and Jure Leskovec. Biodiscoveryagent: An ai agent for designing genetic perturbation experiments. arXiv preprint arXiv:2405.17631, 2024.
- [528] Yoshitaka Inoue, Tianci Song, Xinling Wang, Augustin Luna, and Tianfan Fu. Drugagent: Multi-agent large language model-based reasoning for drug-target interaction prediction. ArXiv, pages arXiv–2408, 2025.
- [529] Joaquin Ramirez-Medina, Mohammadmehdi Ataei, and Alidad Amirfazli. Accelerating scientific research through a multi-llm framework. arXiv preprint arXiv:2502.07960, 2025.
- [530] 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.
- [531] Biqing Qi, Kaiyan Zhang, Haoxiang Li, Kai Tian, Sihang Zeng, Zhang-Ren Chen, and Bowen Zhou. Large language models are zero shot hypothesis proposers. arXiv preprint arXiv:2311.05965, 2023.
113
Agentic Reasoning for Large Language Models
- [532] Xiangru Tang, Tianyu Hu, Muyang Ye, Yanjun Shao, Xunjian Yin, Siru Ouyang, Wangchunshu Zhou, Pan Lu, Zhuosheng Zhang, Yilun Zhao, et al. Chemagent: Self-updating library in large language models improves chemical reasoning. arXiv preprint arXiv:2501.06590, 2025.
- [533] Izumi Takahara, Teruyasu Mizoguchi, and Bang Liu. Accelerated inorganic materials design with generative ai agents. arXiv preprint arXiv:2504.00741, 2025.
- [534] Henry W Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V Olarte, Udishnu Sanyal, Conrad John-ston, Hongbin Liu, Heng Ji, and Sutanay Choudhury. Chemreasoner: Heuristic search over a large lan-guage model’s knowledge space using quantum-chemical feedback. arXiv preprint arXiv:2402.10980, 2024.
- [535] Shuyi Jia, Chao Zhang, and Victor Fung. Llmatdesign: Autonomous materials discovery with large language models. arXiv preprint arXiv:2406.13163, 2024.
- [536] NovelSeek Team, Bo Zhang, Shiyang Feng, Xiangchao Yan, Jiakang Yuan, Zhiyin Yu, Xiaohan He, Songtao Huang, Shaowei Hou, Zheng Nie, et al. Novelseek: When agent becomes the scientist–building closed-loop system from hypothesis to verification. arXiv preprint arXiv:2505.16938, 2025.
- [537] Shrinidhi Kumbhar, Venkatesh Mishra, Kevin Coutinho, Divij Handa, Ashif Iquebal, and Chitta Baral. Hypothesis generation for materials discovery and design using goal-driven and constraint-guided llm agents. arXiv preprint arXiv:2501.13299, 2025.
- [538] Yingming Pu, Tao Lin, and Hongyu Chen. Piflow: Principle-aware scientific discovery with multi-agent collaboration. arXiv preprint arXiv:2505.15047, 2025.
- [539] Haoyang Liu, Yijiang Li, Jinglin Jian, Yuxuan Cheng, Jianrong Lu, Shuyi Guo, Jinglei Zhu, Mianchen Zhang, Miantong Zhang, and Haohan Wang. Toward a team of ai-made scientists for scientific discovery from gene expression data. arXiv preprint arXiv:2402.12391, 2024.
- [540] Kyle Swanson, Wesley Wu, Nash L Bulaong, John E Pak, and James Zou. The virtual lab: Ai agents design new sars-cov-2 nanobodies with experimental validation. bioRxiv, pages 2024–11, 2024.
- [541] Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian D. Reid, and Niko Sünderhauf. Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning. In Jie Tan, Marc Toussaint, and Kourosh Darvish, editors, Conference on Robot Learning, CoRL 2023, 6-9 November 2023, Atlanta, GA, USA, volume 229 of Proceedings of Machine Learning Research, pages 23–72. PMLR, 2023. URL https://proceedings.mlr.press/v229/rana23a.html.
- [542] Yao Mu, Qinglong Zhang, Mengkang Hu, Wenhai Wang, Mingyu Ding, Jun Jin, Bin Wang, Jifeng Dai, Yu Qiao, and Ping Luo. Embodiedgpt: Vision-language pre-training via embodied chain of thought. Advances in Neural Information Processing Systems, 36:25081–25094, 2023.
- [543] Byeonghwi Kim, Jinyeon Kim, Yuyeong Kim, Cheolhong Min, and Jonghyun Choi. Context-aware planning and environment-aware memory for instruction following embodied agents. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10936–10946, 2023.
- [544] Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. CoRR, abs/2302.01560, 2023. doi: 10.48550/ARXIV.2302.01560. URL https://doi.org/10.48550/ arXiv.2302.01560.
114
Agentic Reasoning for Large Language Models
- [545] 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.
- [546] Zhekai Duan, Yuan Zhang, Shikai Geng, Gaowen Liu, Joschka Boedecker, and Chris Xiaoxuan Lu. Fast ecot: Eficient embodied chain-of-thought via thoughts reuse. arXiv preprint arXiv:2506.07639, 2025.
- [547] Alisson Azzolini, Junjie Bai, Hannah Brandon, Jiaxin Cao, Prithvijit Chattopadhyay, Huayu Chen, Jinju Chu, Yin Cui, Jenna Diamond, Yifan Ding, et al. Cosmos-reason1: From physical common sense to embodied reasoning. arXiv preprint arXiv:2503.15558, 2025.
- [548] 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, pages 1702–1713, 2025.
- [549] Qi Sun, Pengfei Hong, Tej Deep Pala, Vernon Toh, U Tan, Deepanway Ghosal, Soujanya Poria, et al. Emma-x: An embodied multimodal action model with grounded chain of thought and look-ahead spatial reasoning. arXiv preprint arXiv:2412.11974, 2024.
- [550] Dongyoung Kim, Sumin Park, Huiwon Jang, Jinwoo Shin, Jaehyung Kim, and Younggyo Seo. Robot-r1: Reinforcement learning for enhanced embodied reasoning in robotics. arXiv preprint arXiv:2506.00070, 2025.
- [551] Zirui Song, Guangxian Ouyang, Mingzhe Li, Yuheng Ji, Chenxi Wang, Zixiang Xu, Zeyu Zhang, Xiaoqing Zhang, Qian Jiang, Zhenhao Chen, et al. Maniplvm-r1: Reinforcement learning for reasoning in embodied manipulation with large vision-language models. arXiv preprint arXiv:2505.16517, 2025.
- [552] Li Kang, Xiufeng Song, Heng Zhou, Yiran Qin, Jie Yang, Xiaohong Liu, Philip Torr, Lei Bai, and Zhenfei Yin. Viki-r: Coordinating embodied multi-agent cooperation via reinforcement learning. arXiv preprint arXiv:2506.09049, 2025.
- [553] Wenhao Wang, Yanyan Li, Long Jiao, and Jiawei Yuan. Gsce: A prompt framework with enhanced reasoning for reliable llm-driven drone control. In 2025 International Conference on Unmanned Aircraft Systems (ICUAS), pages 441–448. IEEE, 2025.
- [554] Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar. Minedojo: Building open-ended embodied agents with internet-scale knowledge. Advances in Neural Information Processing Systems, 35:18343–18362, 2022.
- [555] Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, and Siyuan Huang. An embodied generalist agent in 3d world. arXiv preprint arXiv:2311.12871, 2023.
- [556] Lucy Xiaoyang Shi, Brian Ichter, Michael Equi, Liyiming Ke, Karl Pertsch, Quan Vuong, 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.
- [557] Gemini Robotics Team, Saminda Abeyruwan, Joshua Ainslie, Jean-Baptiste Alayrac, Montserrat Gon-zalez Arenas, Travis Armstrong, Ashwin Balakrishna, Robert Baruch, Maria Bauza, Michiel Blokzijl, et al. Gemini robotics: Bringing ai into the physical world. arXiv preprint arXiv:2503.20020, 2025.
115
Agentic Reasoning for Large Language Models
- [558] Jingkang Yang, Yuhao Dong, Shuai Liu, Bo Li, Ziyue Wang, Haoran Tan, Chencheng Jiang, Jiamu Kang, Yuanhan Zhang, Kaiyang Zhou, et al. Octopus: Embodied vision-language programmer from environmental feedback. In European conference on computer vision, pages 20–38. Springer, 2024.
- [559] Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees GM Snoek, Jan-Jakob Sonke, and Efstratios Gavves. Capo: Cooperative plan optimization for eficient embodied multi-agent cooperation. arXiv preprint arXiv:2411.04679, 2024.
- [560] Kehui Liu, Zixin Tang, Dong Wang, Zhigang Wang, Xuelong Li, and Bin Zhao. Coherent: Collaboration of heterogeneous multi-robot system with large language models. arXiv preprint arXiv:2409.15146, 2024.
- [561] Yiran Qin, Enshen Zhou, Qichang Liu, Zhenfei Yin, Lu Sheng, Ruimao Zhang, Yu Qiao, and Jing Shao. Mp5: A multi-modal open-ended embodied system in minecraft via active perception. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16307–16316. IEEE, 2024.
- [562] Bangguo Yu, Hamidreza Kasaei, and Ming Cao. L3mvn: Leveraging large language models for visual target navigation. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3554–3560. IEEE, 2023.
- [563] Abhinav Rajvanshi, Karan Sikka, Xiao Lin, Bhoram Lee, Han-Pang Chiu, and Alvaro Velasquez. Saynav: Grounding large language models for dynamic planning to navigation in new environments. In Proceedings of the International Conference on Automated Planning and Scheduling, volume 34, pages 464–474, 2024.
- [564] Abrar Anwar, John Welsh, Joydeep Biswas, Soha Pouya, and Yan Chang. Remembr: Building and reasoning over long-horizon spatio-temporal memory for robot navigation. In 2025 IEEE International Conference on Robotics and Automation (ICRA), pages 2838–2845. IEEE, 2025.
- [565] Quanting Xie, So Yeon Min, Pengliang Ji, Yue Yang, Tianyi Zhang, Kedi Xu, Aarav Bajaj, Ruslan Salakhutdinov, Matthew Johnson-Roberson, and Yonatan Bisk. Embodied-rag: General non-parametric embodied memory for retrieval and generation. arXiv preprint arXiv:2409.18313, 2024. URL https: //www.arxiv.org/abs/2409.18313.
- [566] Yichen Zhu, Zhicai Ou, Xiaofeng Mou, and Jian Tang. Retrieval-augmented embodied agents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17985– 17995, 2024.
- [567] Junpeng Yue, Xinrun Xu, Börje F. Karlsson, and Zongqing Lu. Mllm as retriever: Interactively learning multimodal retrieval for embodied agents. arXiv preprint arXiv:2410.03450, 2024. URL https://www.arxiv.org/abs/2410.03450.
- [568] Marc Glocker, Peter Hönig, Matthias Hirschmanner, and Markus Vincze. Llm-empowered embodied agent for memory-augmented task planning in household robotics. arXiv preprint arXiv:2504.21716, 2025.
- [569] Gabriel Sarch, Yue Wu, Michael J Tarr, and Katerina Fragkiadaki. Open-ended instructable embodied agents with memory-augmented large language models. arXiv preprint arXiv:2310.15127, 2023.
- [570] Luo Ling and Bai Qianqian. Endowing embodied agents with spatial reasoning capabilities for vision-and-language navigation. arXiv preprint arXiv:2504.08806, 2025.
116
Agentic Reasoning for Large Language Models
- [571] Hongxin Zhang, Zheyuan Zhang, Zeyuan Wang, Zunzhe Zhang, Lixing Fang, Qinhong Zhou, and Chuang Gan. Ella: Embodied social agents with lifelong memory. arXiv preprint arXiv:2506.24019, 2025.
- [572] Yuanfei Wang, Xinju Huang, Fangwei Zhong, Yaodong Yang, Yizhou Wang, Yuanpei Chen, and Hao Dong. Communication-eficient desire alignment for embodied agent-human adaptation, 2025. URL https://arxiv.org/abs/2505.22503.
- [573] Allen Z Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, et al. Robots that ask for help: Uncertainty alignment for large language model planners. arXiv preprint arXiv:2307.01928, 2023.
- [574] Yang Zhang, Shixin Yang, Chenjia Bai, Fei Wu, Xiu Li, Zhen Wang, and Xuelong Li. Towards eficient llm grounding for embodied multi-agent collaboration. arXiv preprint arXiv:2405.14314, 2024.
- [575] Jesus Moncada-Ramirez, Jose-Luis Matez-Bandera, Javier Gonzalez-Jimenez, and Jose-Raul Ruiz-Sarmiento. Agentic workflows for improving large language model reasoning in robotic object-centered planning. Robotics, 14(3):24, 2025.
- [576] Shuang Ao, Flora D Salim, and Simon Khan. Emac+: Embodied multimodal agent for collaborative planning with vlm+ llm. arXiv preprint arXiv:2505.19905, 2025.
- [577] Shyam Sundar Kannan, Vishnunandan LN Venkatesh, and Byung-Cheol Min. Smart-llm: Smart multi-agent robot task planning using large language models. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 12140–12147. IEEE, 2024.
- [578] Hongxin Zhang, Zeyuan Wang, Qiushi Lyu, Zheyuan Zhang, Sunli Chen, Tianmin Shu, Behzad Dariush, Kwonjoon Lee, Yilun Du, and Chuang Gan. Combo: compositional world models for embodied multi-agent cooperation. arXiv preprint arXiv:2404.10775, 2024.
- [579] Mandi Zhao, Shreeya Jain, and Shuran Song. Roco: Dialectic multi-robot collaboration with large language models. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 286–299. IEEE, 2024.
- [580] Dyke Ferber, Omar SM El Nahhas, Georg Wölflein, Isabella C Wiest, Jan Clusmann, Marie-Elisabeth Leß-man, Sebastian Foersch, Jacqueline Lammert, Maximilian Tschochohei, Dirk Jäger, et al. Autonomous artificial intelligence agents for clinical decision making in oncology. arXiv preprint arXiv:2404.04667, 2024.
- [581] Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, and May D. Wang. Ehragent: Code empowers large language models for few-shot complex tabular reasoning on electronic health records. arXiv preprint arXiv:2401.07128, 2024. URL https: //www.arxiv.org/abs/2401.07128.
- [582] Yexiao He, Ang Li, Boyi Liu, Zhewei Yao, and Yuxiong He. Medorch: Medical diagnosis with tool-augmented reasoning agents for flexible extensibility. arXiv preprint arXiv:2506.00235, 2025.
- [583] Ling Yue, Sixue Xing, Jintai Chen, and Tianfan Fu. Clinicalagent: Clinical trial multi-agent system with large language model-based reasoning. In Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 1–10, 2024.
117
Agentic Reasoning for Large Language Models
- [584] Yichun Feng, Jiawei Wang, Lu Zhou, Zhen Lei, and Yixue Li. Doctoragent-rl: A multi-agent collaborative reinforcement learning system for multi-turn clinical dialogue. arXiv preprint arXiv:2505.19630, 2025.
- [585] Tianqi Shang, Weiqing He, Charles Zheng, Lingyao Li, Li Shen, and Bingxin Zhao. Dynamicare: A dynamic multi-agent framework for interactive and open-ended medical decision-making. arXiv preprint arXiv:2507.02616, 2025.
- [586] Alex J Goodell, Simon N Chu, Dara Rouholiman, and Larry F Chu. Large language model agents can use tools to perform clinical calculations. npj Digital Medicine, 8(1):163, 2025.
- [587] Yakun Zhu, Shaohang Wei, Xu Wang, Kui Xue, Xiaofan Zhang, and Shaoting Zhang. Menti: Bridging medical calculator and llm agent with nested tool calling. arXiv preprint arXiv:2410.13610, 2024.
- [588] Andrew Hoopes. Voxelprompt: A vision-language agent for grounded medical image analysis. PhD thesis, Massachusetts Institute of Technology, 2025.
- [589] Huan Xu, Jinlin Wu, Guanglin Cao, Zhen Lei, Zhen Chen, and Hongbin Liu. Enhancing surgical robots with embodied intelligence for autonomous ultrasound scanning. arXiv preprint arXiv:2405.00461, 2024.
- [590] Abhishek Dutta and Yen-Che Hsiao. Adaptive reasoning and acting in medical language agents. arXiv preprint arXiv:2410.10020, 2024.
- [591] Adibvafa Fallahpour, Jun Ma, Alif Munim, Hongwei Lyu, and Bo Wang. Medrax: Medical reasoning agent for chest x-ray. arXiv preprint arXiv:2502.02673, 2025.
- [592] Mahyar Abbasian, Iman Azimi, Amir M Rahmani, and Ramesh Jain. Conversational health agents: A personalized llm-powered agent framework. arXiv preprint arXiv:2310.02374, 2023.
- [593] Ran Xu, Yuchen Zhuang, Yishan Zhong, Yue Yu, Zifeng Wang, Xiangru Tang, Hang Wu, May D. Wang, Peifeng Ruan, Donghan Yang, Tao Wang, Guanghua Xiao, Xin Liu, Carl Yang, Yang Xie, and Wenqi Shi. Medagentgym: A scalable agentic training environment for code-centric reasoning in biomedical data science, 2025. URL https://arxiv.org/abs/2506.04405.
- [594] Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, Jack Gallifant, Anye Shi, Jie Sun, Xiang Li, Jingxian He, Wenyue Hua, et al. Simulated patient systems powered by large language model-based ai agents offer potential for transforming medical education. Communications Medicine, 2025.
- [595] Mohammad Almansoori, Komal Kumar, and Hisham Cholakkal. Self-evolving multi-agent simulations for realistic clinical interactions. arXiv preprint arXiv:2503.22678, 2025.
- [596] Namkyeong Lee, Edward De Brouwer, Ehsan Hajiramezanali, Tommaso Biancalani, Chanyoung Park, and Gabriele Scalia. Rag-enhanced collaborative llm agents for drug discovery. arXiv preprint arXiv:2502.17506, 2025.
- [597] Juncheng Wu, Wenlong Deng, Xingxuan Li, Sheng Liu, Taomian Mi, Yifan Peng, Ziyang Xu, Yi Liu, Hyunjin Cho, Chang-In Choi, et al. Medreason: Eliciting factual medical reasoning steps in llms via knowledge graphs. arXiv preprint arXiv:2504.00993, 2025.
- [598] Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, and Navid Ghaffarzadegan. Epidemic modeling with generative agents. arXiv preprint arXiv:2307.04986, 2023.
118
Agentic Reasoning for Large Language Models
- [599] Zhuoyun Du, Lujie Zheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, and Haohao Ying. Llms can simulate standardized patients via agent coevolution. arXiv preprint arXiv:2412.11716, 2024.
- [600] Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin, and Ting Liu. Beyond direct diagnosis: Llm-based multi-specialist agent consultation for automatic diagnosis. arXiv preprint arXiv:2401.16107, 2024.
- [601] Xiangru Tang, Anni Zou, Zhuosheng Zhang, Ziming Li, Yilun Zhao, Xingyao Zhang, Arman Cohan, and Mark Gerstein. Medagents: Large language models as collaborators for zero-shot medical reasoning. arXiv preprint arXiv:2311.10537, 2023. URL https://www.arxiv.org/abs/2311.10537.
- [602] Yanzhou Su, Tianbin Li, Jiyao Liu, Chenglong Ma, Junzhi Ning, Cheng Tang, Sibo Ju, Jin Ye, Pengcheng Chen, Ming Hu, et al. Gmai-vl-r1: Harnessing reinforcement learning for multimodal medical reasoning. arXiv preprint arXiv:2504.01886, 2025.
- [603] 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.
- [604] Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, and Yu Su. Gpt-4v (ision) is a generalist web agent, if grounded. arXiv preprint arXiv:2401.01614, 2024.
- [605] Hanyu Lai, Xiao Liu, Iat Long Iong, Shuntian Yao, Yuxuan Chen, Pengbo Shen, Hao Yu, Hanchen Zhang, Xiaohan Zhang, Yuxiao Dong, et al. Autowebglm: A large language model-based web navigating agent. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5295–5306, 2024.
- [606] Junteng Liu, Yunji Li, Chi Zhang, Jingyang Li, Aili Chen, Ke Ji, Weiyu Cheng, Zijia Wu, Chengyu Du, Qidi Xu, et al. Webexplorer: Explore and evolve for training long-horizon web agents. arXiv preprint arXiv:2509.06501, 2025.
- [607] Lucas-Andrei Thil, Mirela Popa, and Gerasimos Spanakis. Navigating webai: Training agents to complete web tasks with large language models and reinforcement learning. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, pages 866–874, 2024.
- [608] Wenxuan Shi, Haochen Tan, Chuqiao Kuang, Xiaoguang Li, Xiaozhe Ren, Chen Zhang, Hanting Chen, Yasheng Wang, Lifeng Shang, Fisher Yu, et al. Pangu deepdiver: Adaptive search intensity scaling via open-web reinforcement learning. arXiv preprint arXiv:2505.24332, 2025.
- [609] Ding-Chu Zhang, Yida Zhao, Jialong Wu, Liwen Zhang, Baixuan Li, Wenbiao Yin, Yong Jiang, Yu-Feng Li, Kewei Tu, Pengjun Xie, et al. Evolvesearch: An iterative self-evolving search agent. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13134–13147, 2025.
- [610] Tianqing Fang, Hongming Zhang, Zhisong Zhang, Kaixin Ma, Wenhao Yu, Haitao Mi, and Dong Yu. Webevolver: Enhancing web agent self-improvement with coevolving world model. arXiv preprint arXiv:2504.21024, 2025.
- [611] 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.
119
Agentic Reasoning for Large Language Models
- [612] Yifei Zhou, Qianlan Yang, Kaixiang Lin, Min Bai, Xiong Zhou, Yu-Xiong Wang, Sergey Levine, and Li Erran Li. Proposer-agent-evaluator (pae): Autonomous skill discovery for foundation model internet agents. In Forty-second International Conference on Machine Learning, 2025.
- [613] Zhiyong Wu, Chengcheng Han, Zichen Ding, Zhenmin Weng, Zhoumianze Liu, Shunyu Yao, Tao Yu, and Lingpeng Kong. Os-copilot: Towards generalist computer agents with self-improvement. arXiv preprint arXiv:2402.07456, 2024.
- [614] Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, and Fei Wu. Infiguiagent: A multimodal generalist gui agent with native reasoning and reflection. arXiv preprint arXiv:2501.04575, 2025.
- [615] Zichen Zhu, Hao Tang, Yansi Li, Dingye Liu, Hongshen Xu, Kunyao Lan, Danyang Zhang, Yixuan Jiang, Hao Zhou, Chenrun Wang, et al. Moba: multifaceted memory-enhanced adaptive planning for eficient mobile task automation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations), pages 535–549, 2025.
- [616] Haowei Liu, Xi Zhang, Haiyang Xu, Yuyang Wanyan, Junyang Wang, Ming Yan, Ji Zhang, Chun-feng Yuan, Changsheng Xu, Weiming Hu, et al. Pc-agent: A hierarchical multi-agent collaboration framework for complex task automation on pc. arXiv preprint arXiv:2502.14282, 2025.
- [617] Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang, Qiushi Sun, Chengyou Jia, Kanzhi Cheng, Zichen Ding, Liheng Chen, Paul Pu Liang, et al. Os-atlas: A foundation action model for generalist gui agents. arXiv preprint arXiv:2410.23218, 2024.
- [618] Xiaoqiang Wang and Bang Liu. Oscar: Operating system control via state-aware reasoning and re-planning. arXiv preprint arXiv:2410.18963, 2024.
- [619] Zhixiong Zeng, Jing Huang, Liming Zheng, Wenkang Han, Yufeng Zhong, Lei Chen, Longrong Yang, Yingjie Chu, Yuzhi He, and Lin Ma. Uitron: Foundational gui agent with advanced perception and planning. arXiv preprint arXiv:2508.21767, 2025.
- [620] Fanbin Lu, Zhisheng Zhong, Shu Liu, Chi-Wing Fu, and Jiaya Jia. Arpo:end-to-end policy optimization for gui agents with experience replay. arXiv preprint arXiv:2505.16282, 2025. URL https://www. arxiv.org/abs/2505.16282.
- [621] Hanyu Lai, Xiao Liu, Yanxiao Zhao, Han Xu, Hanchen Zhang, Bohao Jing, Yanyu Ren, Shuntian Yao, Yuxiao Dong, and Jie Tang. Computerrl: Scaling end-to-end online reinforcement learning for computer use agents. arXiv preprint arXiv:2508.14040, 2025. URL https://www.arxiv.org/abs/ 2508.14040.
- [622] Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin, Liang Liu, Hao Wang, Han Xiao, Shuai Ren, Guanjing Xiong, and Hongsheng Li. Ui-r1: Enhancing action prediction of gui agents by reinforcement learning. arXiv preprint arXiv:2503.21620, 2025. URL https://www.arxiv.org/abs/2503.21620.
- [623] Run Luo, Lu Wang, Wanwei He, Longze Chen, Jiaming Li, and Xiaobo Xia. Gui-r1: A generalist r1-style vision-language action model for gui agents. arXiv preprint arXiv:2504.10458, 2025.
- [624] Yuhang Liu, Pengxiang Li, Congkai Xie, Xavier Hu, Xiaotian Han, Shengyu Zhang, Hongxia Yang, and Fei Wu. Infigui-r1: Advancing multimodal gui agents from reactive actors to deliberative reasoners. arXiv preprint arXiv:2504.14239, 2025. URL https://www.arxiv.org/abs/2504.14239.
120
Agentic Reasoning for Large Language Models
- [625] Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, and Yueting Zhuang. Ui-s1: Advancing gui automation via semi-online reinforcement learning. arXiv preprint arXiv:2509.11543, 2025. URL https://www.arxiv.org/ abs/2509.11543.
- [626] Yue Fan, Handong Zhao, Ruiyi Zhang, Yu Shen, Xin Eric Wang, and Gang Wu. Gui-bee: Align gui action grounding to novel environments via autonomous exploration. arXiv preprint arXiv:2501.13896, 2025. URL https://www.arxiv.org/abs/2501.13896.
- [627] Xinbin Yuan, Jian Zhang, Kaixin Li, Zhuoxuan Cai, Lujian Yao, Jie Chen, Enguang Wang, Qibin Hou, Jinwei Chen, Peng-Tao Jiang, and Bo Li. Enhancing visual grounding for gui agents via self-evolutionary reinforcement learning. arXiv preprint arXiv:2505.12370, 2025. URL https: //www.arxiv.org/abs/2505.12370.
- [628] Longxi Gao, Li Zhang, Pengzhi Gao, Wei Liu, Jian Luan, and Mengwei Xu. Gui-shift: Enhancing vlm-based gui agents through self-supervised reinforcement learning, 2025. URL https://arxiv. org/abs/2505.12493.
- [629] Shuquan Lian, Yuhang Wu, Jia Ma, Yifan Ding, Zihan Song, Bingqi Chen, Xiawu Zheng, and Hui Li. Ui-agile: Advancing gui agents with effective reinforcement learning and precise inference-time grounding. arXiv preprint arXiv:2507.22025, 2025. URL https://www.arxiv.org/abs/2507. 22025.
- [630] Chenyu Yang, Shiqian Su, Shi Liu, Xuan Dong, Yue Yu, Weijie Su, Xuehui Wang, Zhaoyang Liu, Jinguo Zhu, Hao Li, Wenhai Wang, Yu Qiao, Xizhou Zhu, and Jifeng Dai. Zerogui: Automating online gui learning at zero human cost. arXiv preprint arXiv:2505.23762v1, 2025. URL https: //www.arxiv.org/abs/2505.23762v1.
- [631] Zhang Zhong, Lu Yaxi, Fu Yikun, Huo Yupeng, Yang Shenzhi, Wu Yesai, Si Han, Cong Xin, Chen Haotian, Lin Yankai, Xie Jie, Zhou Wei, Xu Wang, Zhang Yuanheng, Su Zhou, Zhai Zhongwu, Liu Xiaoming, Mei Yudong, Xu Jianming, Tian Hongyan, Wang Chongyi, Chen Chi, Yao Yuan, Liu Zhiyuan, and Sun Maosong. Agentcpm-gui: Building mobile-use agents with reinforcement fine-tuning. arXiv preprint arXiv:2506.01391v2, 2025. URL https://www.arxiv.org/abs/2506.01391v2.
- [632] Xiao Liu, Bo Qin, Dongzhu Liang, Guang Dong, Hanyu Lai, Hanchen Zhang, Hanlin Zhao, Iat Long Iong, Jiadai Sun, Jiaqi Wang, Junjie Gao, Junjun Shan, Kangning Liu, Shudan Zhang, Shuntian Yao, Siyi Cheng, Wentao Yao, Wenyi Zhao, Xinghan Liu, Xinyi Liu, Xinying Chen, Xinyue Yang, Yang Yang, Yifan Xu, Yu Yang, Yujia Wang, Yulin Xu, Zehan Qi, Yuxiao Dong, and Jie Tang. Autoglm: Autonomous foundation agents for guis. arXiv preprint arXiv:2411.00820, 2024. URL https: //www.arxiv.org/abs/2411.00820.
- [633] Jiabo Ye, Xi Zhang, Haiyang Xu, Haowei Liu, Junyang Wang, Zhaoqing Zhu, Ziwei Zheng, Feiyu Gao, Junjie Cao, Zhengxi Lu, Jitong Liao, Qi Zheng, Fei Huang, Jingren Zhou, and Ming Yan. Mobile-agent-v3: Fundamental agents for gui automation. arXiv preprint arXiv:2508.15144, 2025. URL https://www.arxiv.org/abs/2508.15144.
- [634] 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.
121
Agentic Reasoning for Large Language Models
- [635] Assaf Elovic. gpt-researcher, July 2023. URL https://github.com/assafelovic/ gpt-researcher.
- [636] Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, et al. Chain of ideas: Revolutionizing research via novel idea development with llm agents. arXiv preprint arXiv:2410.13185, 2024.
- [637] Aniketh Garikaparthi, Manasi Patwardhan, Lovekesh Vig, and Arman Cohan. Iris: Interactive research ideation system for accelerating scientific discovery. arXiv preprint arXiv:2504.16728, 2025.
- [638] 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. arXiv preprint arXiv:2401.13919, 2024.
- [639] Zhengbo Zhang, Zhiheng Lyu, Junhao Gong, Hongzhu Yi, Xinming Wang, Yuxuan Zhou, Jiabing Yang, Ping Nie, Yan Huang, and Wenhu Chen. Browseragent: Building web agents with human-inspired web browsing actions. arXiv preprint arXiv:2510.10666, 2025.
- [640] Viraj Prabhu, Yutong Dai, Matthew Fernandez, Jing Gu, Krithika Ramakrishnan, Yanqi Luo, Silvio Savarese, Caiming Xiong, Junnan Li, Zeyuan Chen, et al. Walt: Web agents that learn tools. arXiv preprint arXiv:2510.01524, 2025.
- [641] Jialong Wu, Baixuan Li, Runnan Fang, Wenbiao Yin, Liwen Zhang, Zhengwei Tao, Dingchu Zhang, Zekun Xi, Gang Fu, Yong Jiang, et al. Webdancer: Towards autonomous information seeking agency. arXiv preprint arXiv:2505.22648, 2025.
- [642] Zhengwei Tao, Jialong Wu, Wenbiao Yin, Junkai Zhang, Baixuan Li, Haiyang Shen, Kuan Li, Liwen Zhang, Xinyu Wang, Yong Jiang, et al. Webshaper: Agentically data synthesizing via information-seeking formalization. arXiv preprint arXiv:2507.15061, 2025.
- [643] Hao Wen, Yuanchun Li, Guohong Liu, Shanhui Zhao, Tao Yu, Toby Jia-Jun Li, Shiqi Jiang, Yunhao Liu, Yaqin Zhang, and Yunxin Liu. Autodroid: Llm-powered task automation in android. In Proceedings of the 30th Annual International Conference on Mobile Computing and Networking, pages 543–557, 2024.
- [644] Hao Wen, Shizuo Tian, Borislav Pavlov, Wenjie Du, Yixuan Li, Ge Chang, Shanhui Zhao, Jiacheng Liu, Yunxin Liu, Ya-Qin Zhang, et al. Autodroid-v2: Boosting slm-based gui agents via code generation. In Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services, pages 223–235, 2025.
- [645] Jiayi Zhang, Chuang Zhao, Yihan Zhao, Zhaoyang Yu, Ming He, and Jianping Fan. Mobileexperts: A dynamic tool-enabled agent team in mobile devices. arXiv preprint arXiv:2407.03913, 2024.
- [646] Chengyou Jia, Minnan Luo, Zhuohang Dang, Qiushi Sun, Fangzhi Xu, Junlin Hu, Tianbao Xie, and Zhiyong Wu. Agentstore: Scalable integration of heterogeneous agents as specialized generalist computer assistant. In Findings of the Association for Computational Linguistics: ACL 2025, pages 8908–8934, 2025.
- [647] Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yutao Zhu, Yongkang Wu, Ji-Rong Wen, and Zhicheng Dou. Webthinker: Empowering large reasoning models with deep research capability. arXiv preprint arXiv:2504.21776, 2025.
122
Agentic Reasoning for Large Language Models
- [648] Marissa Radensky, Simra Shahid, Raymond Fok, Pao Siangliulue, Tom Hope, and Daniel S Weld. Scideator: Human-llm scientific idea generation grounded in research-paper facet recombination. arXiv preprint arXiv:2409.14634, 2024.
- [649] Ruochen Li, Teerth Patel, Qingyun Wang, and Xinya Du. Mlr-copilot: Autonomous machine learning research based on large language models agents. arXiv preprint arXiv:2408.14033, 2024.
- [650] Jiakang Yuan, Xiangchao Yan, Shiyang Feng, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, and Bowen Zhou. Dolphin: Moving towards closed-loop auto-research through thinking, practice, and feedback. arXiv preprint arXiv:2501.03916, 2025.
- [651] 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:2408.06292v3, 2024. URL https://www.arxiv.org/abs/2408.06292v3.
- [652] Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim, Zhenhailong Wang, Dilek Hakkani-Tur, and Heng Ji. Infogent: An agent-based framework for web information aggregation. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 5745–5758, 2025.
- [653] Minsoo Kim, Victor Bursztyn, Eunyee Koh, Shunan Guo, and Seung-won Hwang. Rada: Retrieval-augmented web agent planning with llms. In Findings of the Association for Computational Linguistics ACL 2024, pages 13511–13525, 2024.
- [654] Anonymous. WebRAGent: Retrieval-augmented generation for multimodal web agent planning. In Submitted to The Fourteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=L1VPZFbAcu. under review.
- [655] Longtao Zheng, Rundong Wang, Xinrun Wang, and Bo An. Synapse: Trajectory-as-exemplar prompting with memory for computer control. arXiv preprint arXiv:2306.07863, 2023.
- [656] Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Shuai Ren, Hao Wang, Shibo He, and Wenchao Meng. Learnact: Few-shot mobile gui agent with a unified demonstration benchmark. arXiv preprint arXiv:2504.13805, 2025.
- [657] Sunjae Lee, Junyoung Choi, Jungjae Lee, Munim Hasan Wasi, Hojun Choi, Steven Y Ko, Sangeun Oh, and Insik Shin. Explore, select, derive, and recall: Augmenting llm with human-like memory for mobile task automation. arXiv preprint arXiv:2312.03003, 2023.
- [658] Bofei Zhang, Zirui Shang, Zhi Gao, Wang Zhang, Rui Xie, Xiaojian Ma, Tao Yuan, Xinxiao Wu, Song-Chun Zhu, and Qing Li. Tongui: Internet-scale trajectories from multimodal web tutorials for generalized gui agents, 2025. URL https://arxiv.org/abs/2504.12679.
- [659] Ran Xu, Kaixin Ma, Wenhao Yu, Hongming Zhang, Joyce C Ho, Carl Yang, and Dong Yu. Retrieval-augmented gui agents with generative guidelines. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 17877–17886, 2025.
- [660] Gabriel Sarch, Lawrence Jang, Michael Tarr, William W Cohen, Kenneth Marino, and Katerina Fragkiadaki. Vlm agents generate their own memories: Distilling experience into embodied programs of thought. Advances in Neural Information Processing Systems, 37:75942–75985, 2024.
123
Agentic Reasoning for Large Language Models
- [661] Ke Yang, Yao Liu, Sapana Chaudhary, Rasool Fakoor, Pratik Chaudhari, George Karypis, and Huzefa Rangwala. Agentoccam: A simple yet strong baseline for llm-based web agents. arXiv preprint arXiv:2410.13825, 2024.
- [662] Danqing Zhang, Balaji Rama, Jingyi Ni, Shiying He, Fu Zhao, Kunyu Chen, Arnold Chen, and Junyu Cao. Litewebagent: The open-source suite for vlm-based web-agent applications. arXiv preprint arXiv:2503.02950, 2025.
- [663] Sunjae Lee, Junyoung Choi, Jungjae Lee, Munim Hasan Wasi, Hojun Choi, Steve Ko, Sangeun Oh, and Insik Shin. Mobilegpt: Augmenting llm with human-like app memory for mobile task automation. In Proceedings of the 30th Annual International Conference on Mobile Computing and Networking, pages 1119–1133, 2024.
- [664] 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.
- [665] Weihua Cheng, Ersheng Ni, Wenlong Wang, Yifei Sun, Junming Liu, Wangyu Shen, Yirong Chen, Botian Shi, and Ding Wang. Mga: Memory-driven gui agent for observation-centric interaction. arXiv preprint arXiv:2510.24168, 2025.
- [666] Zhenhailong Wang, Haiyang Xu, Junyang Wang, Xi Zhang, Ming Yan, Ji Zhang, Fei Huang, and Heng Ji. Mobile-agent-e: Self-evolving mobile assistant for complex tasks. arXiv preprint arXiv:2501.11733, 2025.
- [667] Yuquan Xie, Zaijing Li, Rui Shao, Gongwei Chen, Kaiwen Zhou, Yinchuan Li, Dongmei Jiang, and Liqiang Nie. Mirage-1: Augmenting and updating gui agent with hierarchical multimodal skills. arXiv preprint arXiv:2506.10387, 2025.
- [668] Ruhana Azam, Aditya Vempaty, and Ashish Jagmohan. Reflection-based memory for web navigation agents. arXiv preprint arXiv:2506.02158, 2025.
- [669] Ruhana Azam, Tamer Abuelsaad, Aditya Vempaty, and Ashish Jagmohan. Multimodal auto validation for self-refinement in web agents. arXiv preprint arXiv:2410.00689, 2024.
- [670] Kaiwen He, Zhiwei Wang, Chenyi Zhuang, and Jinjie Gu. Recon-act: A self-evolving multi-agent browser-use system via web reconnaissance, tool generation, and task execution. arXiv preprint arXiv:2509.21072, 2025.
- [671] Revanth Gangi Reddy, Tanay Dixit, Jiaxin Qin, Cheng Qian, Daniel Lee, Jiawei Han, Kevin Small, Xing Fan, Ruhi Sarikaya, and Heng Ji. Winell: wikipedia never-ending updating with llm agents. arXiv preprint arXiv:2508.03728, 2025.
- [672] Guanzhong He, Zhen Yang, Jinxin Liu, Bin Xu, Lei Hou, and Juanzi Li. Webseer: Training deeper search agents through reinforcement learning with self-reflection. arXiv preprint arXiv:2510.18798, 2025.
- [673] Tao Li, Gang Li, Zhiwei Deng, Bryan Wang, and Yang Li. A zero-shot language agent for computer control with structured reflection. arXiv preprint arXiv:2310.08740, 2023.
- [674] Penghao Wu, Shengnan Ma, Bo Wang, Jiaheng Yu, Lewei Lu, and Ziwei Liu. Gui-reflection: Em-powering multimodal gui models with self-reflection behavior. arXiv preprint arXiv:2506.08012, 2025.
124
Agentic Reasoning for Large Language Models
- [675] Ziwei Wang, Leyang Yang, Xiaoxuan Tang, Sheng Zhou, Dajun Chen, Wei Jiang, and Yong Li. History-aware reasoning for gui agents. arXiv preprint arXiv:2511.09127, 2025.
- [676] Ning Li, Xiangmou Qu, Jiamu Zhou, Jun Wang, Muning Wen, Kounianhua Du, Xingyu Lou, Qiuying Peng, and Weinan Zhang. Mobileuse: A gui agent with hierarchical reflection for autonomous mobile operation. arXiv preprint arXiv:2507.16853, 2025.
- [677] Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang, Yue Zhang, and Linyi Yang. Cycleresearcher: Improving automated research via automated review. arXiv preprint arXiv:2411.00816, 2024.
- [678] Weizhi Zhang, Yangning Li, Yuanchen Bei, Junyu Luo, Guancheng Wan, Liangwei Yang, Chenxuan Xie, Yuyao Yang, Wei-Chieh Huang, Chunyu Miao, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Yankai Chen, Chunkit Chan, Peilin Zhou, Xinyang Zhang, Chenwei Zhang, Jingbo Shang, Ming Zhang, Yangqiu Song, Irwin King, and Philip S. Yu. From web search towards agentic deep research: Incentivizing search with reasoning agents. arXiv preprint arXiv:2506.18959v3, 2025. URL https: //www.arxiv.org/abs/2506.18959v3.
- [679] Yao Zhang, Zijian Ma, Yunpu Ma, Zhen Han, Yu Wu, and Volker Tresp. Webpilot: A versatile and autonomous multi-agent system for web task execution with strategic exploration. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 23378–23386, 2025.
- [680] Yingxuan Yang, Mulei Ma, Yuxuan Huang, Huacan Chai, Chenyu Gong, Haoran Geng, Yuanjian Zhou, Ying Wen, Meng Fang, Muhao Chen, et al. Agentic web: Weaving the next web with ai agents. arXiv preprint arXiv:2507.21206, 2025.
- [681] Di Zhao, Longhui Ma, Siwei Wang, Miao Wang, and Zhao Lv. Cola: A scalable multi-agent framework for windows ui task automation. arXiv preprint arXiv:2503.09263, 2025.
- [682] Junyang Wang, Haiyang Xu, Haitao Jia, Xi Zhang, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent-v2: Mobile device operation assistant with effective navigation via multi-agent collaboration. Advances in Neural Information Processing Systems, 37:2686–2710, 2024.
- [683] Junyang Wang, Haiyang Xu, Xi Zhang, Ming Yan, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent-v: A video-guided approach for effortless and eficient operational knowledge injection in mobile automation, 2025. URL https://arxiv.org/abs/2502.17110.
- [684] Quanfeng Lu, Zhantao Ma, Shuai Zhong, Jin Wang, Dahai Yu, Michael K Ng, and Ping Luo. Swirl: A staged workflow for interleaved reinforcement learning in mobile gui control. arXiv preprint arXiv:2508.20018, 2025.
- [685] Samuel Schmidgall and Michael Moor. Agentrxiv: Towards collaborative autonomous research. arXiv preprint arXiv:2503.18102, 2025.
- [686] Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. Autonomous chemical research with large language models. Nature, 624(7992):570–578, 2023.
- [687] Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Xuanhe Zhou, Yufei Huang, Chaojun Xiao, Chi Han, Yi R. Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi, Yuzhang Zhu, Zhenning Dai, Lan Yan, Xin Cong, Yaxi Lu, Weilin Zhao, Yuxiang Huang,
125
Agentic Reasoning for Large Language Models
Junxi Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Guoliang Li, Zhiyuan Liu, and Maosong Sun. Tool learning with foundation models. ACM Comput. Surv., 57(4): 101:1–101:40, 2025. doi: 10.1145/3704435. URL https://doi.org/10.1145/3704435.
- [688] Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, and Chao Zhang. Toolqa: A dataset for LLM question answering with external tools. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine, editors, Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, De-cember 10 - 16, 2023, 2023. URL http://papers.nips.cc/paper_files/paper/2023/hash/ 9cb2a7495900f8b602cb10159246a016-Abstract-Datasets_and_Benchmarks.html.
- [689] Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, and Lichao Sun. Metatool benchmark for large language models: Deciding whether to use tools and which to use. 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=R0c2qtalgG.
- [690] Zehui Chen, Weihua Du, Wenwei Zhang, Kuikun Liu, Jiangning Liu, Miao Zheng, Jingming Zhuo, Songyang Zhang, Dahua Lin, Kai Chen, and Feng Zhao. T-eval: Evaluating the tool utilization capability of large language models step by step. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024, pages 9510–9529. Association for Computational Linguistics, 2024. doi: 10.18653/V1/2024.ACL-LONG.515. URL https://doi.org/10.18653/v1/2024.acl-long.515.
- [691] Jize Wang, Zerun Ma, Yining Li, Songyang Zhang, Cailian Chen, Kai Chen, and Xinyi Le. Gta: A benchmark for general tool agents. arXiv preprint arXiv:2407.08713, 2024. URL https://www. arxiv.org/abs/2407.08713.
- [692] Zhengliang Shi, Yuhan Wang, Lingyong Yan, Pengjie Ren, Shuaiqiang Wang, Dawei Yin, and Zhaochun Ren. Retrieval models aren’t tool-savvy: Benchmarking tool retrieval for large language models. CoRR, abs/2503.01763, 2025. doi: 10.48550/ARXIV.2503.01763. URL https://doi.org/10.48550/ arXiv.2503.01763.
- [693] Qiantong Xu, Fenglu Hong, Bo Li, Changran Hu, Zhengyu Chen, and Jian Zhang. On the tool manipulation capability of open-source large language models. CoRR, abs/2305.16504, 2023. doi: 10.48550/ARXIV.2305.16504. URL https://doi.org/10.48550/arXiv.2305.16504.
- [694] 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, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023, pages 3102–3116. Association for Computational Linguistics, 2023. doi: 10.18653/V1/2023.EMNLP-MAIN.187. URL https://doi.org/10.18653/v1/2023.emnlp-main.187.
- [695] Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, and Qun Liu. Planning, creation, usage: Benchmarking llms for comprehensive tool utilization in real-world complex scenarios. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, pages 4363–4400.
126
Agentic Reasoning for Large Language Models
Association for Computational Linguistics, 2024. doi: 10.18653/V1/2024.FINDINGS-ACL.259. URL https://doi.org/10.18653/v1/2024.findings-acl.259.
- [696] Pei Wang, Yanan Wu, Noah Wang, Jiaheng Liu, Xiaoshuai Song, Z. Y. Peng, Ken Deng, Chenchen Zhang, Jiakai Wang, Junran Peng, Ge Zhang, Hangyu Guo, Zhaoxiang Zhang, Wenbo Su, and Bo Zheng. Mtu-bench: A multi-granularity tool-use benchmark for large language models. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. URL https://openreview.net/forum?id=6guG2OlXsr.
- [697] Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, et al. Webwalker: Benchmarking llms in web traversal. arXiv preprint arXiv:2501.07572, 2025.
- [698] Yunjia Xi, Jianghao Lin, Menghui Zhu, Yongzhao Xiao, Zhuoying Ou, Jiaqi Liu, Tong Wan, Bo Chen, Weiwen Liu, Yasheng Wang, et al. Infodeepseek: Benchmarking agentic information seeking for retrieval-augmented generation. arXiv preprint arXiv:2505.15872, 2025.
- [699] Yilong Xu, Xiang Long, Zhi Zheng, and Jinhua Gao. Ravine: Reality-aligned evaluation for agentic search. arXiv preprint arXiv:2507.16725, 2025.
- [700] Ryan Wong, Jiawei Wang, Junjie Zhao, Li Chen, Yan Gao, Long Zhang, Xuan Zhou, Zuo Wang, Kai Xiang, Ge Zhang, et al. Widesearch: Benchmarking agentic broad info-seeking. arXiv preprint arXiv:2508.07999, 2025.
- [701] Tian Lan, Bin Zhu, Qianghuai Jia, Junyang Ren, Haijun Li, Longyue Wang, Zhao Xu, Weihua Luo, and Kaifu Zhang. Deepwidesearch: Benchmarking depth and width in agentic information seeking. arXiv preprint arXiv:2510.20168, 2025.
- [702] Shan Chen, Pedro Moreira, Yuxin Xiao, Sam Schmidgall, Jeremy Warner, Hugo Aerts, Thomas Hartvigsen, Jack Gallifant, and Danielle S Bitterman. Medbrowsecomp: Benchmarking medical deep research and computer use. arXiv preprint arXiv:2505.14963, 2025.
- [703] Chanyeol Choi, Jihoon Kwon, Alejandro Lopez-Lira, Chaewoon Kim, Minjae Kim, Juneha Hwang, Jaeseon Ha, Hojun Choi, Suyeol Yun, Yongjin Kim, et al. Finagentbench: A benchmark dataset for agentic retrieval in financial question answering. In Proceedings of the 6th ACM International Conference on AI in Finance, pages 632–637, 2025.
- [704] Hang He, Chuhuai Yue, Chengqi Dong, Mingxue Tian, Zhenfeng Liu, Jiajun Chai, Xiaohan Wang, Yufei Zhang, Qun Liao, Guojun Yin, et al. Localsearchbench: Benchmarking agentic search in real-world local life services. arXiv preprint arXiv:2512.07436, 2025.
- [705] Dongzhi Jiang, Renrui Zhang, Ziyu Guo, Yanmin Wu, Jiayi Lei, Pengshuo Qiu, Pan Lu, Zehui Chen, Chaoyou Fu, Guanglu Song, et al. Mmsearch: Benchmarking the potential of large models as multi-modal search engines. arXiv preprint arXiv:2409.12959, 2024.
- [706] Xijia Tao, Yihua Teng, Xinxing Su, Xinyu Fu, Jihao Wu, Chaofan Tao, Ziru Liu, Haoli Bai, Rui Liu, and Lingpeng Kong. Mmsearch-plus: Benchmarking provenance-aware search for multimodal browsing agents. arXiv preprint arXiv:2508.21475, 2025.
- [707] Shilong Li, Xingyuan Bu, Wenjie Wang, Jiaheng Liu, Jun Dong, Haoyang He, Hao Lu, Haozhe Zhang, Chenchen Jing, Zhen Li, et al. Mm-browsecomp: A comprehensive benchmark for multimodal browsing agents. arXiv preprint arXiv:2508.13186, 2025.
127
Agentic Reasoning for Large Language Models
- [708] Yixiao Song, Katherine Thai, Chau Minh Pham, Yapei Chang, Mazin Nadaf, and Mohit Iyyer. Bearcubs: A benchmark for computer-using web agents. arXiv preprint arXiv:2503.07919, 2025.
- [709] Daoyu Wang, Mingyue Cheng, Shuo Yu, Zirui Liu, Ze Guo, and Qi Liu. Paperarena: An evaluation bench-mark for tool-augmented agentic reasoning on scientific literature. arXiv preprint arXiv:2510.10909, 2025.
- [710] Zhengyang Liang, Yan Shu, Xiangrui Liu, Minghao Qin, Kaixin Liang, Paolo Rota, Nicu Sebe, Zheng Liu, and Lizi Liao. Video-browsecomp: Benchmarking agentic video research on open web. arXiv preprint arXiv:2512.23044, 2025.
- [711] Chengwen Liu, Xiaomin Yu, Zhuoyue Chang, Zhe Huang, Shuo Zhang, Heng Lian, Kunyi Wang, Rui Xu, Sen Hu, Jianheng Hou, et al. Watching, reasoning, and searching: A video deep research benchmark on open web for agentic video reasoning. arXiv preprint arXiv:2601.06943, 2026.
- [712] Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang, Baojun Wang, Wanjun Zhong, Zezhong Wang, and Kam-Fai Wong. Perltqa: A personal long-term memory dataset for memory classification, retrieval, and fusion in question answering. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10), pages 152–164, 2024.
- [713] Thibaut Thonet, Jos Rozen, and Laurent Besacier. Elitr-bench: A meeting assistant benchmark for long-context language models. arXiv preprint arXiv:2403.20262, 2024.
- [714] Yun He, Di Jin, Chaoqi Wang, Chloe Bi, Karishma Mandyam, Hejia Zhang, Chen Zhu, Ning Li, Tengyu Xu, Hongjiang Lv, et al. Multi-if: Benchmarking llms on multi-turn and multilingual instructions following. arXiv preprint arXiv:2410.15553, 2024.
- [715] Ved Sirdeshmukh, Kaustubh Deshpande, Johannes Mols, Lifeng Jin, Ed-Yeremai Cardona, Dean Lee, Jeremy Kritz, Willow Primack, Summer Yue, and Chen Xing. Multichallenge: A realistic multi-turn conversation evaluation benchmark challenging to frontier llms. arXiv preprint arXiv:2501.17399, 2025.
- [716] Yiran Zhang, Mo Wang, Xiaoyang Li, Kaixuan Ren, Chencheng Zhu, and Usman Naseem. Turnbench-ms: A benchmark for evaluating multi-turn, multi-step reasoning in large language models. arXiv preprint arXiv:2506.01341, 2025.
- [717] Luanbo Wan and Weizhi Ma. Storybench: A dynamic benchmark for evaluating long-term memory with multi turns. arXiv preprint arXiv:2506.13356, 2025.
- [718] Haoran Tan, Zeyu Zhang, Chen Ma, Xu Chen, Quanyu Dai, and Zhenhua Dong. Membench: Towards more comprehensive evaluation on the memory of llm-based agents. arXiv preprint arXiv:2506.21605, 2025.
- [719] Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, et al. Mmrc: A large-scale benchmark for understanding multimodal large language model in real-world conversation. arXiv preprint arXiv:2502.11903, 2025.
- [720] Zeyu Zhang, Quanyu Dai, Luyu Chen, Zeren Jiang, Rui Li, Jieming Zhu, Xu Chen, Yi Xie, Zhenhua Dong, and Ji-Rong Wen. Memsim: A bayesian simulator for evaluating memory of llm-based personal assistants. arXiv preprint arXiv:2409.20163, 2024.
128
Agentic Reasoning for Large Language Models
- [721] Dong-Ho Lee, Adyasha Maharana, Jay Pujara, Xiang Ren, and Francesco Barbieri. Realtalk: A 21-day real-world dataset for long-term conversation. arXiv preprint arXiv:2502.13270, 2025.
- [722] Yuanzhe Hu, Yu Wang, and Julian McAuley. Evaluating memory in llm agents via incremental multi-turn interactions. arXiv preprint arXiv:2507.05257, 2025.
- [723] Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambham-pati. Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change. Advances in Neural Information Processing Systems, 36:38975–38987, 2023.
- [724] Harsha Kokel, Michael Katz, Kavitha Srinivas, and Shirin Sohrabi. Acpbench: Reasoning about action, change, and planning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 26559–26568, 2025.
- [725] Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, and Ping Luo. Text2world: Benchmarking large language models for symbolic world model generation. arXiv preprint arXiv:2502.13092, 2025.
- [726] Longling Geng and Edward Y Chang. Realm-bench: A benchmark for evaluating multi-agent systems on real-world, dynamic planning and scheduling tasks. arXiv preprint arXiv:2502.18836, 2025.
- [727] Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, and Yu Su. Travelplanner: A benchmark for real-world planning with language agents. arXiv preprint arXiv:2402.01622, 2024.
- [728] Ruixuan Xiao, Wentao Ma, Ke Wang, Yuchuan Wu, Junbo Zhao, Haobo Wang, Fei Huang, and Yongbin Li. Flowbench: Revisiting and benchmarking workflow-guided planning for llm-based agents. arXiv preprint arXiv:2406.14884, 2024.
- [729] Yu Zheng, Longyi Liu, Yuming Lin, Jie Feng, Guozhen Zhang, Depeng Jin, and Yong Li. Urbanplan-bench: A comprehensive urban planning benchmark for evaluating large language models. arXiv preprint arXiv:2504.21027, 2025.
- [730] Lianmin Zheng, Jiacheng Yang, Han Cai, Ming Zhou, Weinan Zhang, Jun Wang, and Yong Yu. Magent: A many-agent reinforcement learning platform for artificial collective intelligence. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
- [731] Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob Foerster, Julian Togelius, Kyunghyun Cho, and Joan Bruna. Pommerman: A multi-agent playground. arXiv preprint arXiv:1809.07124, 2018.
- [732] Mikayel Samvelyan, Tabish Rashid, Christian Schroeder De Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson. The starcraft multi-agent challenge. arXiv preprint arXiv:1902.04043, 2019.
- [733] Xianhao Yu, Jiaqi Fu, Renjia Deng, and Wenjuan Han. Mineland: Simulating large-scale multi-agent interactions with limited multimodal senses and physical needs. arXiv preprint arXiv:2403.19267, 2024.
- [734] Qian Long, Zhi Li, Ran Gong, Ying Nian Wu, Demetri Terzopoulos, and Xiaofeng Gao. Teamcraft: A benchmark for multi-modal multi-agent systems in minecraft. arXiv preprint arXiv:2412.05255, 2024.
129
Agentic Reasoning for Large Language Models
- [735] Joel Z Leibo, Edgar A Dueñez-Guzman, Alexander Vezhnevets, John P Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, and Thore Graepel. Scalable evaluation of multi-agent reinforcement learning with melting pot. In International conference on machine learning, pages 6187–6199. PMLR, 2021.
- [736] Matteo Bettini, Amanda Prorok, and Vincent Moens. Benchmarl: Benchmarking multi-agent rein-forcement learning. Journal of Machine Learning Research, 25(217):1–10, 2024.
- [737] Yuhang Song, Andrzej Wojcicki, Thomas Lukasiewicz, Jianyi Wang, Abi Aryan, Zhenghua Xu, Mai Xu, Zihan Ding, and Lianlong Wu. Arena: A general evaluation platform and building toolkit for multi-agent intelligence. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 7253–7260, 2020.
- [738] Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Mont-gomery Alban, Iman Fadakar, Zheng Chen, et al. Smarts: Scalable multi-agent reinforcement learning training school for autonomous driving. arXiv preprint arXiv:2010.09776, 2020.
- [739] Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, and Jakob Foerster. Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. Advances in Neural Information Processing Systems, 35:3962–3974, 2022.
- [740] Xianliang Yang, Zhihao Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, and Jiang Bian. A versatile multi-agent reinforcement learning benchmark for inventory management. arXiv preprint arXiv:2306.07542, 2023.
- [741] Pascal Leroy, Pablo G Morato, Jonathan Pisane, Athanasios Kolios, and Damien Ernst. Imp-marl: a suite of environments for large-scale infrastructure management planning via marl. Advances in neural information processing systems, 36:53522–53551, 2023.
- [742] Alexey Skrynnik, Anton Andreychuk, Anatolii Borzilov, Alexander Chernyavskiy, Konstantin Yakovlev, and Aleksandr Panov. Pogema: A benchmark platform for cooperative multi-agent pathfinding. arXiv preprint arXiv:2407.14931, 2024.
- [743] Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Zhongxia Yan, and Cathy Wu. Intersectionzoo: Eco-driving for benchmarking multi-agent contextual reinforcement learning. arXiv preprint arXiv:2410.15221, 2024.
- [744] Saaket Agashe, Yue Fan, Anthony Reyna, and Xin Eric Wang. Llm-coordination: evaluating and analyzing multi-agent coordination abilities in large language models. arXiv preprint arXiv:2310.03903, 2023.
- [745] Jonathan Light, Min Cai, Sheng Shen, and Ziniu Hu. Avalonbench: Evaluating llms playing the game of avalon. arXiv preprint arXiv:2310.05036, 2023. URL https://www.arxiv.org/abs/2310.05036.
- [746] Gabriel Mukobi, Hannah Erlebach, Niklas Lauffer, Lewis Hammond, Alan Chan, and Jesse Clifton. Welfare diplomacy: Benchmarking language model cooperation. arXiv preprint arXiv:2310.08901, 2023.
- [747] Lin Xu, Zhiyuan Hu, Daquan Zhou, Hongyu Ren, Zhen Dong, Kurt Keutzer, See Kiong Ng, and Jiashi Feng. Magic: Investigation of large language model powered multi-agent in cognition, adaptability, rationality and collaboration. arXiv preprint arXiv:2311.08562, 2023.
130
Agentic Reasoning for Large Language Models
- [748] Timothy Ossowski, Jixuan Chen, Danyal Maqbool, Zefan Cai, Tyler Bradshaw, and Junjie Hu. Comma: A communicative multimodal multi-agent benchmark. arXiv preprint arXiv:2410.07553, 2024.
- [749] Elad Levi and Ilan Kadar. Intellagent: A multi-agent framework for evaluating conversational ai systems. arXiv preprint arXiv:2501.11067, 2025.
- [750] Tajamul Ashraf, Amal Saqib, Hanan Ghani, Muhra AlMahri, Yuhao Li, Noor Ahsan, Umair Nawaz, Jean Lahoud, Hisham Cholakkal, Mubarak Shah, et al. Agent-x: Evaluating deep multimodal reasoning in vision-centric agentic tasks. arXiv preprint arXiv:2505.24876, 2025.
- [751] Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, and Tim Rocktäschel. Balrog: Benchmarking agentic llm and vlm reasoning on games. arXiv preprint arXiv:2411.13543, 2024. URL https://www.arxiv.org/abs/2411.13543.
- [752] Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang, and Zhen Xiao. Understanding the weakness of large language model agents within a complex android environment. arXiv preprint arXiv:2402.06596v1, 2024. URL https://www.arxiv.org/abs/2402.06596v1.
- [753] Weihao Tan, Changjiu Jiang, Yu Duan, Mingcong Lei, Jiageng Li, Yitian Hong, Xinrun Wang, and Bo An. Stardojo: Benchmarking open-ended behaviors of agentic multimodal llms in production-living simulations with stardew valley. arXiv preprint arXiv:2507.07445v2, 2025. URL https: //www.arxiv.org/abs/2507.07445v2.
- [754] Ran Gong, Qiuyuan Huang, Xiaojian Ma, Hoi Vo, Zane Durante, Yusuke Noda, Zilong Zheng, Song-Chun Zhu, Demetri Terzopoulos, Li Fei-Fei, and Jianfeng Gao. Mindagent: Emergent gaming interac-tion. arXiv preprint arXiv:2309.09971, 2023. URL https://www.arxiv.org/abs/2309.09971.
- [755] Dominik Jeurissen, Diego Perez-Liebana, Jeremy Gow, Duygu Cakmak, and James Kwan. Playing nethack with llms: Potential & limitations as zero-shot agents. arXiv preprint arXiv:2403.00690, 2024. URL https://www.arxiv.org/abs/2403.00690.
- [756] Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, and Tao Yu. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. arXiv preprint arXiv:2404.07972v2, 2024. URL https: //www.arxiv.org/abs/2404.07972v2.
- [757] Peter Jansen, Marc-Alexandre Côté, Tushar Khot, Erin Bransom, Bhavana Dalvi Mishra, Bod-hisattwa Prasad Majumder, Oyvind Tafjord, and Peter Clark. Discoveryworld: A virtual environment for developing and evaluating automated scientific discovery agents. Advances in Neural Information Processing Systems, 37:10088–10116, 2024.
- [758] Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. Scienceworld: Is your agent smarter than a 5th grader? arXiv preprint arXiv:2203.07540, 2022. URL https: //www.arxiv.org/abs/2203.07540.
- [759] Ziru Chen, Shijie Chen, Yuting Ning, Qianheng Zhang, 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. arXiv preprint arXiv:2410.05080, 2024. URL https://www.arxiv.org/abs/2410.05080.
131
Agentic Reasoning for Large Language Models
- [760] Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, Michaela M. Hinks, Michael J. Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D. White, and Samuel G. Rodriques. Lab-bench: Measuring capabilities of language models for biology research. arXiv preprint arXiv:2407.10362, 2024. URL https://www.arxiv.org/abs/2407.10362.
- [761] Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec. Mlagentbench: Evaluating language agents on machine learning experimentation. arXiv preprint arXiv:2310.03302, 2023. URL https://www. arxiv.org/abs/2310.03302.
- [762] 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.
- [763] Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault L De Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, and Alexandre Drouin. Workarena++: Towards compositional planning and reasoning-based common knowledge work tasks. Advances in Neural Information Processing Systems, 37:5996–6051, 2024.
- [764] Zilong Wang, Yuedong Cui, Li Zhong, Zimin Zhang, Da Yin, Bill Yuchen Lin, and Jingbo Shang. Oficebench: Benchmarking language agents across multiple applications for ofice automation. arXiv preprint arXiv:2407.19056, 2024. URL https://www.arxiv.org/abs/2407.19056.
- [765] Darshan Deshpande, Varun Gangal, Hersh Mehta, Jitin Krishnan, Anand Kannappan, and Rebecca Qian. Trail: Trace reasoning and agentic issue localization. arXiv preprint arXiv:2505.08638, 2025. URL https://www.arxiv.org/abs/2505.08638.
- [766] Yuji Zhang, Sha Li, Jiateng Liu, Pengfei Yu, Yi R Fung, Jing Li, Manling Li, and Heng Ji. Knowl-edge overshadowing causes amalgamated hallucination in large language models. arXiv preprint arXiv:2407.08039, 2024.
- [767] Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, and Peter Clark. Clin: A continually learning language agent for rapid task adaptation and generalization. arXiv preprint arXiv:2310.10134, 2023. URL https: //www.arxiv.org/abs/2310.10134.
- [768] Mingchen Zhuge, Changsheng Zhao, Dylan Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chan-dra, and Jürgen Schmidhuber. Agent-as-a-judge: Evaluate agents with agents. arXiv preprint arXiv:2410.10934, 2024. URL https://www.arxiv.org/abs/2410.10934.
- [769] Samuel Schmidgall, Rojin Ziaei, Carl 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. URL https://www.arxiv.org/abs/2405.07960.
- [770] Yixing Jiang, Kameron C. Black, Gloria Geng, Danny Park, James Zou, Andrew Y. Ng, and Jonathan H. Chen. Medagentbench: A realistic virtual ehr environment to benchmark medical llm agents. arXiv preprint arXiv:2501.14654, 2025. URL https://www.arxiv.org/abs/2501.14654.
- [771] Xiangru Tang, Daniel Shao, Jiwoong Sohn, Jiapeng Chen, Jiayi Zhang, Jinyu Xiang, Fang Wu, Yilun Zhao, Chenglin Wu, Wenqi Shi, Arman Cohan, and Mark Gerstein. Medagentsbench: Bench-marking thinking models and agent frameworks for complex medical reasoning. arXiv preprint arXiv:2503.07459, 2025. URL https://www.arxiv.org/abs/2503.07459.
132
Agentic Reasoning for Large Language Models
- [772] Karishma Thakrar, Shreyas Basavatia, and Akshay Daftardar. Architecting clinical collaboration: Multi-agent reasoning systems for multimodal medical vqa, 2025. URL https://arxiv.org/abs/ 2507.05520.
- [773] Zhen Xiang, Linzhi Zheng, Yanjie Li, Junyuan Hong, Qinbin Li, Han Xie, Jiawei Zhang, Zidi Xiong, Chulin Xie, Carl Yang, Dawn Song, and Bo Li. Guardagent: Safeguard llm agents by a guard agent via knowledge-enabled reasoning. arXiv preprint arXiv:2406.09187, 2024. URL https: //www.arxiv.org/abs/2406.09187.
- [774] Yichen Pan, Dehan Kong, Sida Zhou, Cheng Cui, Yifei Leng, Bing Jiang, Hangyu Liu, Yanyi Shang, Shuyan Zhou, Tongshuang Wu, and Zhengyang Wu. Webcanvas: Benchmarking web agents in online environments. arXiv preprint arXiv:2406.12373v3, 2024. URL https://www.arxiv.org/abs/ 2406.12373v3.
- [775] Xing Han Lù, Zdeněk Kasner, and Siva Reddy. Weblinx: Real-world website navigation with multi-turn dialogue. arXiv preprint arXiv:2402.05930, 2024. URL https://www.arxiv.org/abs/2402. 05930.
- [776] Peilin Zhou, Bruce Leon, Xiang Ying, Can Zhang, Yifan Shao, Qichen Ye, Dading Chong, Zhiling Jin, Chenxuan Xie, Meng Cao, Yuxin Gu, Sixin Hong, Jing Ren, Jian Chen, Chao Liu, and Yining Hua. Browsecomp-zh: Benchmarking web browsing ability of large language models in chinese. arXiv preprint arXiv:2504.19314, 2025. URL https://www.arxiv.org/abs/2504.19314.
- [777] Kaixin Ma, Hongming Zhang, Hongwei Wang, Xiaoman Pan, Wenhao Yu, and Dong Yu. Laser: Llm agent with state-space exploration for web navigation. arXiv preprint arXiv:2309.08172, 2023. URL https://www.arxiv.org/abs/2309.08172.
- [778] Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate, Mayank Agarwal, Maxwell Crouse, Yara Rizk, Kelsey Bradford, Asim Munawar, Sadhana Kumaravel, Saurabh Goyal, et al. Nestful: A benchmark for evaluating llms on nested sequences of api calls. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 33526–33535, 2025.
- [779] Joykirat Singh, Raghav Magazine, Yash Pandya, and Akshay Nambi. Agentic reasoning and tool integration for llms via reinforcement learning. arXiv preprint arXiv:2505.01441v1, 2025. URL https://www.arxiv.org/abs/2505.01441v1.
- [780] Divij Handa, Pavel Dolin, Shrinidhi Kumbhar, Tran Cao Son, and Chitta Baral. Actionreasoningbench: Reasoning about actions with and without ramification constraints. arXiv preprint arXiv:2406.04046, 2024. URL https://www.arxiv.org/abs/2406.04046.
- [781] Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, and Gongshen Liu. R-judge: Benchmarking safety risk awareness for llm agents. arXiv preprint arXiv:2401.10019, 2024. URL https://www.arxiv.org/ abs/2401.10019.
- [782] Cheng Qian, Zuxin Liu, Akshara Prabhakar, Jielin Qiu, Zhiwei Liu, Haolin Chen, Shirley Kokane, Heng Ji, Weiran Yao, Shelby Heinecke, et al. Userrl: Training interactive user-centric agent via reinforcement learning. arXiv preprint arXiv:2509.19736, 2025.
- [783] Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, and Tat-Seng Chua. Hello again! llm-powered personalized agent for long-term dialogue. In Proceedings of the 2025 Conference of the
133
Agentic Reasoning for Large Language Models
Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5259–5276, 2025.
- [784] Yufei Xiang, Yiqun Shen, Yeqin Zhang, and Nguyen Cam-Tu. Retrospex: Language agent meets ofline reinforcement learning critic. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4650–4666, 2024.
- [785] Yuxiang Ji, Ziyu Ma, Yong Wang, Guanhua Chen, Xiangxiang Chu, and Liaoni Wu. Tree search for llm agent reinforcement learning. arXiv preprint arXiv:2509.21240, 2025.
- [786] Quentin Carbonneaux, Gal Cohen, Jonas Gehring, Jacob Kahn, Jannik Kossen, Felix Kreuk, Emily McMilin, Michel Meyer, Yuxiang Wei, David Zhang, et al. Cwm: An open-weights llm for research on code generation with world models. arXiv preprint arXiv:2510.02387, 2025.
- [787] Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104, 2023.
- [788] Hao Tang, Darren Key, and Kevin Ellis. Worldcoder, a model-based llm agent: Building world models by writing code and interacting with the environment. In Conference on Neural Information Processing Systems (NeurIPS), pages 70148–70212, 2024.
- [789] Hyungjoo Chae, Namyoung Kim, Kai Tzu-iunn Ong, Minju Gwak, Gwanwoo Song, Jihoon Kim, Sunghwan Kim, Dongha Lee, and Jinyoung Yeo. Web agents with world models: Learning and leveraging environment dynamics in web navigation. arXiv preprint arXiv:2410.13232, 2024.
- [790] Dezhao Luo, Bohan Tang, Kang Li, Georgios Papoudakis, Jifei Song, Shaogang Gong, Jianye Hao, Jun Wang, and Kun Shao. Vimo: A generative visual gui world model for app agents. arXiv preprint arXiv:2504.13936, 2025.
- [791] Mingkai Deng, Jinyu Hou, Zhiting Hu, and Eric Xing. Simura: A world-model-driven simulative reasoning architecture for general goal-oriented agents. arXiv preprint arXiv:2507.23773, 2025.
- [792] Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors. In The Twelfth International Conference on Learning Representations, 2023.
- [793] Chen Qian, Zihao Xie, Yifei Wang, Wei Liu, Kunlun Zhu, Hanchen Xia, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, et al. Scaling large language model-based multi-agent collaboration. arXiv preprint arXiv:2406.07155, 2024.
- [794] Florian Grötschla, Luis Müller, Jan Tönshoff, Mikhail Galkin, and Bryan Perozzi. Agentsnet: Coordina-tion and collaborative reasoning in multi-agent llms. arXiv preprint arXiv:2507.08616v1, 2025. URL https://www.arxiv.org/abs/2507.08616v1.
- [795] Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Bo Zheng, Wei Chen, and Haochao Ying. Enabling agents to communicate entirely in latent space. arXiv preprint arXiv:2511.09149, 2025.
- [796] Tianyu Fu, Zihan Min, Hanling Zhang, Jichao Yan, Guohao Dai, Wanli Ouyang, and Yu Wang. Cache-to-cache: Direct semantic communication between large language models. arXiv preprint arXiv:2510.03215, 2025.
134
Agentic Reasoning for Large Language Models
- [797] Kun Wang, Guibin Zhang, Zhenhong Zhou, Jiahao Wu, Miao Yu, Shiqian Zhao, Chenlong Yin, Jinhu Fu, Yibo Yan, Hanjun Luo, et al. A comprehensive survey in llm (-agent) full stack safety: Data, training and deployment. arXiv preprint arXiv:2504.15585, 2025.