GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2509.22009v2
• Date: Tue, 30 Sep 2025 07:25:35 GMT
• Title: GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation
• Authors: Cehao Yang, Xiaojun Wu, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Yuanliang Sun, Jia Li, Hui Xiong, Jian Guo,
• Abstract summary: textscGraphSearch is a novel agentic deep searching workflow with dual-channel retrieval for GraphRAG.<n>textscGraphSearch consistently improves answer accuracy and generation quality over the traditional strategy.
• Score: 35.65907480060404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Retrieval-Augmented Generation (GraphRAG) enhances factual reasoning in LLMs by structurally modeling knowledge through graph-based representations. However, existing GraphRAG approaches face two core limitations: shallow retrieval that fails to surface all critical evidence, and inefficient utilization of pre-constructed structural graph data, which hinders effective reasoning from complex queries. To address these challenges, we propose \textsc{GraphSearch}, a novel agentic deep searching workflow with dual-channel retrieval for GraphRAG. \textsc{GraphSearch} organizes the retrieval process into a modular framework comprising six modules, enabling multi-turn interactions and iterative reasoning. Furthermore, \textsc{GraphSearch} adopts a dual-channel retrieval strategy that issues semantic queries over chunk-based text data and relational queries over structural graph data, enabling comprehensive utilization of both modalities and their complementary strengths. Experimental results across six multi-hop RAG benchmarks demonstrate that \textsc{GraphSearch} consistently improves answer accuracy and generation quality over the traditional strategy, confirming \textsc{GraphSearch} as a promising direction for advancing graph retrieval-augmented generation.

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