图结构提升大模型智能体的规划与协作能力
Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects
- 用图结构增强大模型在规划、记忆和工具使用中的能力
- 图能有效提升多智能体系统的协调效率与可信度
- 适合关注智能体系统设计的研究者和开发者
基于大语言模型（LLMs）的自主智能体已在网页导航、软件开发和具身控制等任务中展现强大能力。然而，现有LLMs在可靠规划、长期记忆、工具管理及多智能体协同等方面仍存在局限。图结构可作为辅助架构，增强复杂智能体工作流中的结构化、连续性与协调性。针对图增强型大模型智能体（GLA）研究的快速增长与分散现状，本文系统梳理了近期进展，按主要功能分为规划、记忆与工具使用三类，并分析图结构与图学习算法在各环节的作用。针对多智能体系统，进一步探讨了GLA如何促进任务编排、效率优化与可信度提升。最后，提出未来关键方向，包括提升结构适应性，构建统一、可扩展、多模态的GLA系统。本文旨在为该领域研究提供路线图，深化对图结构在大模型智能体中作用的理解。
原文摘要 · Abstract (English)
Autonomous agents based on large language models (LLMs) have demonstrated impressive capabilities in a wide range of applications, including web navigation, software development, and embodied control. While most LLMs are limited in several key agentic procedures, such as reliable planning, long-term memory, tool management, and multi-agent coordination, graphs can serve as a powerful auxiliary structure to enhance structure, continuity, and coordination in complex agent workflows. Given the rapid growth and fragmentation of research on Graph-augmented LLM Agents (GLA), this paper offers a timely and comprehensive overview of recent advances and also highlights key directions for future work. Specifically, we categorize existing GLA methods by their primary functions in LLM agent systems, including planning, memory, and tool usage, and then analyze how graphs and graph learning algorithms contribute to each. For multi-agent systems, we further discuss how GLA solutions facilitate the orchestration, efficiency optimization, and trustworthiness of MAS. Finally, we highlight key future directions to advance this field, from improving structural adaptability to enabling unified, scalable, and multimodal GLA systems. We hope this paper can serve as a roadmap for future research on GLA and foster a deeper understanding of the role of graphs in LLM agent systems.
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