Text-Graph Synergy: A Bidirectional Verification and Completion Framework for RAG

ArXi:2605.05643v1 Announce Type: new Retrieval-Augmented Generation (RAG) has become a core paradigm for enhancing factual grounding and multi-hop reasoning in Large Language Models (LLMs). Traditional text-based RAG often retrieves logically irrelevant pseudo-evidence, while graph-based RAG is frequently hindered by search-time pruning, which may discard potentially valid reasoning paths.