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Large language models (LLMs) are being used to solve planning problems that require search.
Introduction to Algorithms
Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest · 1990
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On the complexity of blocks-world planning
Naresh Gupta and Dana S. Nau · 1992
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Sokoban: A challenging single-agent search problem
Andreas Junghanns and Jonathan Schaeffer · 1997
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VAL’s progress: The automatic validation tool for PDDL2.1 used in the International Planning Competition
Richard Howey and Derek Long · 2003
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The Fast Downward planning system
Malte Helmert · 2006
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A reminder about the importance of computing and exploiting invariants in planning
Vidal Alcázar and Álvaro Torralba · 2015
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Semi-black box: Rapid development of planning based solutions
Michael Katz, Dany Moshkovich, and Erez Karpas · 2018
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, et al · 2021
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Codenet: A large-scale AI for code dataset for learning a diversity of coding tasks
Ruchir Puri, David S. Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir R. Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss · 2021
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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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Plansformer: Generating symbolic plans using transformers
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, and Andrea Loreggia · 2022
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Planning with large language models via corrective re-prompting
Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, and Stefanie Tellex · 2022
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PDDL planning with pretrained large language models
Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
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Leveraging pre-trained large language models to construct and utilize world models for model-based task planning
Lin Guan, Karthik Valmeekam, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Wang, and Zhiting Hu · 2023
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Selfevolve: A code evolution framework via large language models, 2023
Shuyang Jiang, Yuhao Wang, and Yu Wang · 2023
Earlier work this paper cites.
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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Self-refine: Iterative refinement with self-feedback
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
Cited alongside, same era.
Understanding the capabilities of large language models for automated planning
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Biplav Srivastava, Lior Horesh, Francesco Fabiano, and Andrea Loreggia · 2023
Cited alongside, same era.
Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Abulhair Saparov and He He · 2023
Cited alongside, same era.
Algorithm of thoughts: Enhancing exploration of ideas in large language models
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Lu Wang, Ruoxi Jia, and Ming Jin · 2023
Cited alongside, same era.
Reflexion: language agents with verbal reinforcement learning
CRITIC: Large language models can self-correct with tool-interactive critiquing
Zhibin Gou, Zhihong Shao, Yeyun Gong, yelong shen, Yujiu Yang, Nan Duan, and Weizhu Chen · 2024
Closest in time.
Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou · 2024
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Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, et al · 2024
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Position: LLMs can’t plan, but can help planning in LLM-modulo frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Paul Saldyt, and Anil B Murthy · 2024
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Thought of search: Planning with language models through the lens of efficiency
Michael Katz, Harsha Kokel, Kavitha Srinivas, and Shirin Sohrabi · 2024
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Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
Cited alongside, same era.
Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change
Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2023
Cited alongside, same era.
On the planning abilities of large language models - A critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati · 2023
Cited alongside, same era.
Translating natural language to planning goals with large-language models
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh · 2023
Cited alongside, same era.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
Cited alongside, same era.
Self-edit: Fault-aware code editor for code generation
Kechi Zhang, Zhuo Li, Jia Li, Ge Li, and Zhi Jin · 2023
Cited alongside, same era.
Language agent tree search unifies reasoning acting and planning in language models
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang · 2023
Cited alongside, same era.
Llama 3 model card
AI@Meta · 2024
Cited alongside, same era.
Guiding enumerative program synthesis with large language models
Yixuan Li, Julian Parsert, and Elizabeth Polgreen · 2024
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Octopack: Instruction tuning code large language models
Niklas Muennighoff, Qian Liu, Armel Randy Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre · 2024
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Openai gpt-4o system card, 2024
OpenAI, :, Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, et al · 2024
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Large language models as planning domain generators
James Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz, and Shirin Sohrabi · 2024
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Generalized planning in PDDL domains with pretrained large language models
Tom Silver, Soham Dan, Kavitha Srinivas, Josh Tenenbaum, Leslie Pack Kaelbling, and Michael Katz · 2024
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On the self-verification limitations of large language models on reasoning and planning tasks
Kaya Stechly, Karthik Valmeekam, and Subbarao Kambhampati · 2024
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SWE-agent: Agent-computer interfaces enable automated software engineering
John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press · 2024
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Li Zhong, Zilong Wang, and Jingbo Shang · 2024
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Planetarium: A rigorous benchmark for translating text to structured planning languages
Max Zuo, Francisco Piedrahita Velez, Xiaochen Li, Michael L. Littman, and Stephen H. Bach · 2024
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Augusto B. Corrêa, André G. Pereira, and Jendrik Seipp · 2025
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Aster: Natural and multi-language unit test generation with llms
Rangeet Pan, Myeongsoo Kim, Rahul Krishna, Raju Pavuluri, and Saurabh Sinha · 2025
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Llm-generated heuristics for ai planning: Do we even need domain-independence anymore?, 2025
Alexander Tuisov, Yonatan Vernik, and Alexander Shleyfman · 2025
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