Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning 事件

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning arXiv:2507.21892v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. GraphRAG methods improve RAG by modeling knowledge as entity-relation graphs, but still face challenges in high construction cost, fixed one-time retrieval, and reliance on long-context rea