PlanGenLLMs: A Modern Survey of LLM Planning Capabilities
Hui Wei, Zihao Zhang, Shenghua He, Tian Xia, Shijia Pan, Fei Liu
摘要
LLMs have immense potential for generating plans, transforming an initial world state into a desired goal state. A large body of research has explored the use of LLMs for various planning tasks, from web navigation to travel planning and database querying. However, many of these systems are tailored to specific problems, making it challenging to compare them or determine the best approach for new tasks. There is also a lack of clear and consistent evaluation criteria. Our survey aims to offer a comprehensive overview of current LLM planners to fill this gap. It builds on foundational work by Kartam and Wilkins (1990) and examines six key performance criteria: completeness, executability, optimality, representation, generalization, and efficiency. For each, we provide a thorough analysis of representative works and highlight their strengths and weaknesses. Our paper also identifies crucial future directions, making it a valuable resource for both practitioners and newcomers interested in leveraging LLM planning to support agentic workflows. 1 LLM Planning Foundation ( §2)
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper14
- Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement LearningJiayu Wang, Yifei Ming, Zixuan Ke, Caiming Xiong 等NeurIPS 2025 · 被引用 7 次
- AutoTool: Efficient Tool Selection for Large Language Model AgentsJingyi Jia, Qinbin LiAAAI 2026 · 被引用 4 次
- Language Model as Planner and Formalizer under ConstraintsCassie Huang, Stuti Mohan, Ziyi Yang, Stefanie Tellex 等ACL 2026 · 被引用 3 次
- Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational AgentsNikhil Sharma, Zheng Zhang, Daniel Lee, Namita Krishnan 等CHI 2026 · 被引用 2 次
- Can Large Language Models Generalize Procedures Across Representations?Fangru Lin, Valentin Hofmann, Xingchen Wan, Weixing Wang 等ICML 2026 · 被引用 2 次
它引用的顶会 Paper37
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma 等NeurIPS 2022 · 被引用 22,562 次
- Direct Preference Optimization: Your Language Model is Secretly a Reward ModelRafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning 等NeurIPS 2023 · 被引用 10,924 次
- Tree of Thoughts: Deliberate Problem Solving with Large Language ModelsShunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran 等NeurIPS 2023 · 被引用 5,068 次
- ALFWorld: Aligning Text and Embodied Environments for Interactive LearningMohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk 等ICLR 2021 · 被引用 819 次
相关 Paper
- A Survey of Large Language Model-Based Search AgentsYunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou 等ACL 2026 · 被引用 1,216 次
- LLM Agents in Law: Taxonomy, Applications, and ChallengesShuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng 等ACL 2026 · 被引用 3 次
- Factuality of Large Language Models: A SurveyYuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu 等EMNLP 2024 · 被引用 23 次
- OrchestrationBench: LLM-Driven Agentic Planning and Tool Use in Multi-Domain ScenariosAelim Ahn, Sooyeon Lee, Hyosun Wang, Chiwan Park 等ICLR 2026
- DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable ConstraintsYinger Zhang, Shutong Jiang, Renhao Li, Jianhong Tu 等ACL 2026 · 被引用 21 次
