{"ID":2865132,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.22009","arxiv_id":"2509.22009","title":"GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation","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.","short_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-co...","url_abs":"https://arxiv.org/abs/2509.22009","url_pdf":"https://arxiv.org/pdf/2509.22009v2","authors":"[\"Cehao Yang\",\"Xiaojun Wu\",\"Xueyuan Lin\",\"Chengjin Xu\",\"Xuhui Jiang\",\"Yuanliang Sun\",\"Jia Li\",\"Hui Xiong\",\"Jian Guo\"]","published":"2025-09-26T07:45:56Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\",\"Large Language Model\",\"Generative Adversarial Network\"]","has_code":false}