GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

TL;DR

GraphScout is a novel framework that enables large language models to autonomously explore and reason over knowledge graphs, improving factual reasoning without manual guidance or extensive annotation.

Contribution

It introduces a training-centric agentic graph reasoning approach with flexible exploration tools, internalizing reasoning ability in LLMs through synthesized training data.

Findings

Small models with GraphScout outperform larger baselines by 16.7% on average.

GraphScout requires fewer inference tokens than baseline methods.

It demonstrates strong cross-domain transfer performance.

Abstract

Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large language models (LLMs) with graph-based retrieval to improve factual grounding. Recent Graph-based Retrieval-Augmented Generation (GraphRAG) methods therefore introduce iterative interaction between LLMs and knowledge graphs to enhance reasoning capability. However, existing approaches typically depend on manually designed guidance and interact with knowledge graphs through a limited set of predefined tools, which substantially constrains graph exploration. To address these limitations, we propose GraphScout, a training-centric agentic graph reasoning framework equipped with more flexible graph exploration tools. GraphScout enables models to autonomously interact with knowledge graphs to synthesize structured training data which are then used to post-train LLMs, thereby internalizing agentic graph reasoning ability without laborious manual annotation or task curation. Extensive experiments across five knowledge-graph domains show that a small model (e.g., Qwen3-4B) augmented with GraphScout outperforms baseline methods built on leading LLMs (e.g., Qwen-Max) by an average of 16.7\% while requiring significantly fewer inference tokens. Moreover, GraphScout exhibits robust cross-domain transfer performance. Our code will be made publicly available~\footnote{https://github.com/Ying-Yuchen/_GraphScout_}.