Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on
  Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval
Authors: name: Xiaojun Wu; name: Cehao Yang; name: Xueyuan Lin; name: Chengjin Xu; name: Xuhui Jiang; name: Yuanliang Sun; name: Hui Xiong; name: Jia Li; name: Jian Guo
Published: 2025-09-26 00:13:10

Abstract: 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.