{"ID":2853910,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15339","arxiv_id":"2510.15339","title":"AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction","abstract":"Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal graph structures. To bridge this gap, we introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). AutoGraph-R1 trains an LLM constructor by framing graph generation as a policy learning problem, where the reward is derived from the graph's functional utility in a RAG pipeline. We design two novel, task-aware reward functions, one for graphs as knowledge carriers and another as knowledge indices. Across multiple QA benchmarks, AutoGraph-R1 consistently enables graph RAG methods to achieve significant performance gains over using task-agnostic baseline graphs. Our work shows it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically ``good'' graphs to building demonstrably ``useful'' ones.","short_abstract":"Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal...","url_abs":"https://arxiv.org/abs/2510.15339","url_pdf":"https://arxiv.org/pdf/2510.15339v3","authors":"[\"Hong Ting Tsang\",\"Jiaxin Bai\",\"Haoyu Huang\",\"Qiao Xiao\",\"Tianshi Zheng\",\"Baixuan Xu\",\"Shujie Liu\",\"Yangqiu Song\"]","published":"2025-10-17T06:03:36Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"RAG\",\"Reinforcement Learning\",\"Large Language Model\"]","has_code":false}