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The rise of Large Reasoning Models (LRMs) signifies a paradigm shift toward advanced computational reasoning.
Alfworld: Aligning text and embodied environments for interactive learning, 2021
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht · 2010
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering, 2018
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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Selection-inference: Exploiting large language models for interpretable logical reasoning, 2022
Antonia Creswell, Murray Shanahan, and Irina Higgins · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Partially observable markov decision processes in robotics: A survey
Mikko Lauri, David Hsu, and Joni Pajarinen · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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TheoremQA: A theorem-driven question answering dataset
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Complexity-based prompting for multi-step reasoning, 2023
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot · 2023
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Towards reasoning in large language models: A survey, 2023
Jie Huang and Kevin Chen-Chuan Chang · 2023
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Llm-pruner: On the structural pruning of large language models, 2023
Xinyin Ma, Gongfan Fang, and Xinchao Wang · 2023
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Self-refine: Iterative refinement with self-feedback, 2023
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Reflexion: Language agents with verbal reinforcement learning, 2023
Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
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Progprompt: Generating situated robot task plans using large language models
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Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation, 2023
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 · 2023
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Llm a*: Human in the loop large language models enabled a* search for robotics
Hengjia Xiao and Peng Wang · 2023
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Auto-gpt for online decision making: Benchmarks and additional opinions, 2023
Hui Yang, Sifu Yue, and Yunzhong He · 2023
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Claude3.5
Claude Ahtropic · 2024
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ReST meets react: Self-improvement for multi-step reasoning LLM agent
Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, Manzil Zaheer, Felix Yu, and Sanjiv Kumar · 2024
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LLM augmented LLMs: Expanding capabilities through composition
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, and Partha Talukdar · 2024
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Do not think that much for on the overthinking of o1-like llms
Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu · 2024
Understanding the weakness of large language model agents within a complex android environment
Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang, and Zhen Xiao · 2024
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Osagent: Copiloting operating system with llm-based agent
Jiaming Xu, Kaibin Guo, Wuxuan Gong, and Runyu Shi · 2024
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Enhancing llm capabilities beyond scaling up
Wenpeng Yin, Muhao Chen, Rui Zhang, Ben Zhou, Fei Wang, and Dan Roth · 2024
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Fincon: A synthesized llm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making
Yangyang Yu, Zhiyuan Yao, Haohang Li, Zhiyang Deng, Yuechen Jiang, Yupeng Cao, Zhi Chen, Jordan Suchow, Zhenyu Cui, Rong Liu, Zhaozhuo Xu, Denghui Zhang, Koduvayur (Suba) Subbalakshmi, GUOJUN XIONG, Yueru He, Jimin Huang, Dong Li, and Qianqian Xie · 2024
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EASYTOOL: Enhancing LLM-based agents with concise tool instruction
Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Kan Ren, Dongsheng Li, and Deqing Yang · 2024
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Budgetmlagent: A cost-effective llm multi-agent system for automating machine learning tasks
Shubham Gandhi, Manasi Patwardhan, Lovekesh Vig, and Gautam Shroff · 2024
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Metagpt: Meta programming for a multi-agent collaborative framework, 2024
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An llm compiler for parallel function calling, 2024
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Alleviating action hallucination for llm-based embodied agents via inner and outer alignment
Kanxue Li, Qi Zheng, Yibing Zhan, Chong Zhang, Tianle Zhang, Xu Lin, Chongchong Qi, Lusong Li, and Dapeng Tao · 2024
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Enhancing decision-making for llm agents via step-level q-value models
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Towards efficient llm grounding for embodied multi-agent collaboration
Yang Zhang, Shixin Yang, Chenjia Bai, Fei Wu, Xiu Li, Zhen Wang, and Xuelong Li · 2024
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Exploring the limitations of large language models in compositional relation reasoning, 2024
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Hallucination detection in foundation models for decision-making: A flexible definition and review of the state of the art
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The danger of overthinking: Examining the reasoning-action dilemma in agentic tasks
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Gemini 2.0 Flash Thinking Experimental Model 01-21
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A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
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A survey on post-training of large language models
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