BRIDGE-Prop: Reasoning-Guided Bridge Propagation for Multi-Hop Retrieval

Multi-hop question answering often requires combining evidence linked through entities that are absent from the question itself. In reasoning-based iterative GraphRAG, a language model may infer such bridge entities during multi-round retrieval. However, these entities remain in unstructured reasoning text and may be omitted or diluted in later retrieval steps, rather than being retained as explicit, reusable representations in the graph. As a result, they cannot directly guide subsequent graph propagation. We introduce BRIDGE-Prop (Reasoning-Guided Bridge Propagation), an iterative GraphRAG framework that grounds predicted bridge entities to graph nodes and retains them as a cumulative bridge memory across rounds. These bridges act as explicit seeds for the existing Personalized PageRank graph-ranking algorithm, allowing retrieval to pursue multiple intermediate connections. We evaluate BRIDGE-Prop on MuSiQue and 2WikiMultiHopQA on the HippoRAG-2 backbone. It raises 2WikiMultiHopQA recall@5 from .889 to .962 over the strongest iterative baseline, ITER-RETGEN, and lifts answer EM from .608 to .709. By question type and reasoning depth, the gain is largest where a single query cannot gather all the evidence at once: 2Wiki bridge-comparison questions and deeper MuSiQue hops. The retrieval advantage holds for both a proprietary and an open-weight model. BRIDGE-Prop shows how free-text LLM reasoning can be grounded into discrete, symbolic structure, tightening the loop between neural reasoning and structured graph-based retrieval.