Understanding the planning of LLM agents: A survey

By Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen. arXiv.org, 2024

483 citations recorded by the source corpus.

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. We provide a taxonomy of existing works on LLM-Agent planning, which can be categorized into Task Decomposition, Plan Selection, External Module, Reflection and Memory. Comprehensive analyses are conducted for each direction, and further challenges for the field of research are discussed.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/7e281e8ab380affd3c5724feae038274df378511

Citations in the corpus

• Multi-Agent Collaboration Mechanisms: A Survey of LLMs (2025, unknown): “They enhance long-term planning by delegating tasks across agents, supporting persistent problem-solving over extended interactions [58].”
• Tool learning with large language models: a survey (2024, unknown): “This limitation extends to tasks involving code generation [164, 165] and chemistry and physics problems [73, 74], etc ., further under-scoring the gap in their expertise in more specialized areas.”
• A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, unknown): “CodeAgent [214] focuses on the repo-level code generation task, which commonly requires complex dependencies and extensive documentation.”
• A Survey of Model Architectures in Information Retrieval (2025, unknown): “We omit detailed discussions here and refer readers to the original papers.”