CHASE: Chain-Guided Hypothetical Supporting-Fact Generation for Graph-Free Multi-Hop Dense Retrieval

Graph-based retrieval-augmented generation (RAG) outperforms conventional dense-retrieval RAG on multi-hop questions by capturing cross-document connections through an explicit graph index, at the cost of offline graph construction and, for some systems, task-specific training. We ask whether the reasoning structure such a graph supplies can instead be elicited from the question itself at query time. We propose CHASE (Chain-guided Hypothetical Supporting-fact gEneration), a training-free framework in which a single LLM call infers the latent reasoning chain of a multi-hop question and emits one hypothetical supporting fact (HSF) per hop, written in the declarative form a supporting passage would take. Each HSF is issued as an independent dense query and the per-anchor results are consolidated by max-score fusion, so the generator produces a chain while the retriever consumes a set and all vector searches run in parallel. Under a matched first-stage protocol that holds the encoder, corpus and index fixed, HSF anchors raise Recall@2 over question-only retrieval by 13.9 points on MuSiQue, 11.1 on 2WikiMultiHopQA and 7.0 on HotpotQA, and anchor-aware reranking adds a further 9.5, 12.2 and 4.0 points over question-only reranking. Against published graph-based and iterative systems CHASE is competitive with GFM-RAG and IRCoT+HippoRAG, although those systems use different encoders, so we report that comparison as context rather than as a controlled result. We also map where the method applies: anchor generation calls for a generator above a capability threshold that, at Recall@2, no open model we tested at 35B parameters or below reaches on two of the three benchmarks, which makes distillation of anchor generation the clearest route to a deployable version.