CLOSED-LOOP BRIDGE RETRIEVAL FOR MULTI-HOP QUESTION ANSWERING

Multi-hop retrieval-augmented generation has to connect supporting evidencescattered across documents, and the right next retrieval target usually becomesclear only after earlier evidence accumulates. Iterative RAG systems adapt re-trieval through query rewriting, subquestion generation, or sufficiency assessment,but this adaptation stays open-loop at the bridge that routes the next hop: a candi-date intermediate entity steers the following retrieval step, and whatever evidencecomes back is folded into the context without asking again whether the bridge–gapbinding behind it is actually supported, so a wrong route is carried forward insteadof revoked. What remains open is when retrieval should commit to an interme-diate bridge and how it should recover once later evidence contradicts that com-mitment. We propose CLBR-RAG (Closed-Loop Bridge Retrieval), which closesthe loop by turning each bridge commitment into a provisional control state thatcan be revoked. A commitment binds an evidence-supported intermediate entityto an unresolved information gap. Complementary lexical and semantic querieskeep coverage over candidate bridges before any commitment is made; the Ev-idence Gate admits a source-grounded binding only when it matches an activegap and agrees with the accumulated context, and a deterministic composer theninstantiates its targeted next-hop query. The evidence returned by that branchdecides whether the commitment survives: unsupported branches are retracted,supported ones keep their evidence, and BROAD exploration resumes when notargeted branch is accepted. Validated cross-document bridge relations are car-ried into a compact, provenance-preserving context for final reasoning. Acrossthree multi-hop benchmarks and TriviaQA as a broader control, CLBR-RAG isconsistently effective. Code will be released upon publication.