图顾问：通过多智能体协同增强大语言模型推理的自适应图探索

Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning

June 4, 2025

作者: Junqi Gao, Xiang Zou, YIng Ai, Dong Li, Yichen Niu, Biqing Qi, Jianxing Liu

cs.AI

摘要

图检索增强生成（GraphRAG）通过显式建模知识关系，有效提升了外部知识整合能力，从而增强了大型语言模型（LLMs）在专业领域的事实准确性和生成质量。然而，现有方法存在两个固有局限：1）信息聚合效率低下：它们依赖单一代理和固定迭代模式，难以自适应地捕捉图数据中的多层次文本、结构和度信息。2）推理机制僵化：采用预设推理方案，无法动态调整推理深度，也无法实现精确的语义校正。为克服这些局限，我们提出了基于多代理协作的GraphRAG方法——Graph Counselor。该方法利用自适应图信息提取模块（AGIEM），其中规划、思考和执行代理协同工作，精确建模复杂图结构并动态调整信息提取策略，解决了多层次依赖建模和自适应推理深度的难题。此外，多视角自反思（SR）模块通过自反思和逆向推理机制，提升了推理结果的准确性和语义一致性。实验表明，Graph Counselor在多项图推理任务中优于现有方法，展现出更高的推理准确性和泛化能力。我们的代码已发布于https://github.com/gjq100/Graph-Counselor.git。

English

Graph Retrieval Augmented Generation (GraphRAG) effectively enhances external knowledge integration capabilities by explicitly modeling knowledge relationships, thereby improving the factual accuracy and generation quality of Large Language Models (LLMs) in specialized domains. However, existing methods suffer from two inherent limitations: 1) Inefficient Information Aggregation: They rely on a single agent and fixed iterative patterns, making it difficult to adaptively capture multi-level textual, structural, and degree information within graph data. 2) Rigid Reasoning Mechanism: They employ preset reasoning schemes, which cannot dynamically adjust reasoning depth nor achieve precise semantic correction. To overcome these limitations, we propose Graph Counselor, an GraphRAG method based on multi-agent collaboration. This method uses the Adaptive Graph Information Extraction Module (AGIEM), where Planning, Thought, and Execution Agents work together to precisely model complex graph structures and dynamically adjust information extraction strategies, addressing the challenges of multi-level dependency modeling and adaptive reasoning depth. Additionally, the Self-Reflection with Multiple Perspectives (SR) module improves the accuracy and semantic consistency of reasoning results through self-reflection and backward reasoning mechanisms. Experiments demonstrate that Graph Counselor outperforms existing methods in multiple graph reasoning tasks, exhibiting higher reasoning accuracy and generalization ability. Our code is available at https://github.com/gjq100/Graph-Counselor.git.