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

2026-03-01

AI Summary

This work proposes GraphScout, a framework that overcomes the limitations of existing graph reasoning methods—which rely heavily on human guidance and constrained toolsets—by enabling large language models to autonomously interact with knowledge graphs. Through a training-driven approach, GraphScout allows models to synthesize structured training data from graph interactions and internalize graph reasoning capabilities via post-training, achieving agent-like reasoning without any human annotations. The framework supports cross-domain transfer and significantly enhances exploratory flexibility. Experimental results across five knowledge graph domains demonstrate that smaller models, such as Qwen3-4B, outperform the Qwen-Max baseline by an average of 16.7% while incurring lower inference costs.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search

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_}.

Problem

• Graph-based Retrieval-Augmented Generation
• knowledge graph exploration
• large language models
• agentic reasoning
• manual guidance

Innovation

• GraphScout
• agentic graph reasoning
• training-centric framework
• autonomous graph exploration
• GraphRAG