[2603.01410] GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

Computer Science > Artificial Intelligence arXiv:2603.01410 (cs) [Submitted on 2 Mar 2026] Title:GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning Authors:Yuchen Ying, Weiqi Jiang, Tongya Zheng, Yu Wang, Shunyu Liu, Kaixuan Chen, Mingli Song View a PDF of the paper titled GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning, by Yuchen Ying and 6 other authors View PDF 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 annotat...