AI RESEARCH
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
arXiv CS.CL
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ArXi:2510.15339v3 Announce Type: replace 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