GraphScout: 通过内在探索能力增强大语言模型以实现代理图推理
GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
AI总结：
GraphScout通过灵活的图探索工具，使模型自主生成结构化训练数据，从而在无需人工标注的情况下提升大语言模型的图推理能力，实验显示其在多个领域表现优异。
摘要：
知识图谱为许多现实应用提供了结构化和可靠的信息，推动了将大语言模型（LLMs）与基于图的检索结合以增强事实性 grounding 的研究。最近的基于图的检索增强生成（GraphRAG）方法引入了LLM与知识图谱之间的迭代交互以提升推理能力。然而，现有方法通常依赖手动设计的指导，并通过有限的预定义工具与知识图谱交互，这在很大程度上限制了图探索。为了解决这些限制，我们提出了GraphScout，一种以训练为中心的代理图推理框架，配备了更灵活的图探索工具。GraphScout使模型能够自主与知识图谱交互，生成结构化训练数据，然后用于后训练LLMs，从而内化代理图推理能力，而无需繁琐的人工标注或任务整理。在五个知识图谱领域进行的广泛实验表明，一个小型模型（例如Qwen3-4B）通过GraphScout增强后，在平均上比基于领先LLMs（例如Qwen-Max）的基线方法高出16.7%，同时需要显著更少的推理标记。此外，GraphScout表现出稳健的跨领域迁移性能。我们的代码将公开发布~\footnote{https://github.com/Ying-Yuchen/_GraphScout_}。
英文摘要：
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_}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
