Think-on-Graph 3.0：基于多智能体双演进上下文检索的异构图高效自适应大语言模型推理

摘要

检索增强生成（RAG）及基于图的RAG已成为利用外部知识增强大型语言模型（LLMs）的重要范式。然而，现有方法面临一个根本性的权衡。虽然基于图的方法本质上依赖于高质量的图结构，但它们在实际应用中受到显著限制：手动构建的知识图谱扩展成本高昂，而从语料库自动提取的图谱则受限于底层LLM提取器的性能，尤其是在使用较小、本地部署的模型时。本文提出了Think-on-Graph 3.0（ToG-3），一个引入多智能体上下文演化与检索（MACER）机制的新颖框架，以克服这些局限。我们的核心创新在于动态构建并优化一个Chunk-Triplets-Community异构图索引，首次融合了查询演化与子图演化的双重机制，实现精确的证据检索。这一方法解决了先前基于图的RAG方法的关键限制，即通常一次性构建静态图索引而不适应实际查询。一个由构造器、检索器、反思器与响应器智能体组成的多智能体系统，协作进行证据检索、答案生成、充分性反思，以及至关重要的查询与子图演化。这一双演化的多智能体系统使ToG-3能够在推理过程中自适应地构建目标图索引，缓解了静态一次性图构建的固有缺陷，即便使用轻量级LLM也能实现深度精确的推理。大量实验表明，ToG-3在深度与广度推理基准上均优于对比基线，消融研究也证实了MACER框架各组成部分的有效性。

English

Retrieval-Augmented Generation (RAG) and Graph-based RAG has become the important paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing approaches face a fundamental trade-off. While graph-based methods are inherently dependent on high-quality graph structures, they face significant practical constraints: manually constructed knowledge graphs are prohibitively expensive to scale, while automatically extracted graphs from corpora are limited by the performance of the underlying LLM extractors, especially when using smaller, local-deployed models. This paper presents Think-on-Graph 3.0 (ToG-3), a novel framework that introduces Multi-Agent Context Evolution and Retrieval (MACER) mechanism to overcome these limitations. Our core innovation is the dynamic construction and refinement of a Chunk-Triplets-Community heterogeneous graph index, which pioneeringly incorporates a dual-evolution mechanism of Evolving Query and Evolving Sub-Graph for precise evidence retrieval. This approach addresses a critical limitation of prior Graph-based RAG methods, which typically construct a static graph index in a single pass without adapting to the actual query. A multi-agent system, comprising Constructor, Retriever, Reflector, and Responser agents, collaboratively engages in an iterative process of evidence retrieval, answer generation, sufficiency reflection, and, crucially, evolving query and subgraph. This dual-evolving multi-agent system allows ToG-3 to adaptively build a targeted graph index during reasoning, mitigating the inherent drawbacks of static, one-time graph construction and enabling deep, precise reasoning even with lightweight LLMs. Extensive experiments demonstrate that ToG-3 outperforms compared baselines on both deep and broad reasoning benchmarks, and ablation studies confirm the efficacy of the components of MACER framework.

Think-on-Graph 3.0：基于多智能体双演进上下文检索的异构图高效自适应大语言模型推理

Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval

摘要

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