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We examine the reasoning and planning capabilities of large language models (LLMs) in solving complex tasks.
An introduction to first-order logic
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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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From lsat: The progress and challenges of complex reasoning
Siyuan Wang, Zhongkun Liu, Wanjun Zhong, Ming Zhou, Zhongyu Wei, Zhumin Chen, and Nan Duan · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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Reasoning with language model is planning with world model
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Llm+p: Empowering large language models with optimal planning proficiency
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone · 2023
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Abulhair Saparov and He He · 2023
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On the planning abilities of large language models-a critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Openai o1 system card, 2024
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The neglected tails in vision-language models
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Mathematical discoveries from program search with large language models
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Mastering board games by external and internal planning with language models, 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Self-consistency improves chain of thought reasoning in language models
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Llm4drive: A survey of large language models for autonomous driving
Zhenjie Yang, Xiaosong Jia, Hongyang Li, and Junchi Yan · 2023
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi · 2023
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Graph of thoughts: Solving elaborate problems with large language models
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Everything of thoughts: Defying the law of penrose triangle for thought generation
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Llms still can’t plan; can lrms? a preliminary evaluation of openai’s o1 on planbench
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A survey on large language model based autonomous agents
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From decoding to meta-generation: Inference-time algorithms for large language models
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Tree of thoughts: Deliberate problem solving with large language models
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Natural plan: Benchmarking llms on natural language planning
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025
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