Dependency-Aligned Chunk-to-Event Graph Retrieval for Multi-Hop Question Answering

Multi-hop question answering requires retrieving complementary evidence distributed across documents, including intermediate facts that may be weakly related to the original question. Dense retrievers often miss such evidence, while graph-based methods may traverse corpus connections that are irrelevant to the required reasoning structure. We argue that subquery dependencies provide an explicit structural prior for determining which evidence regions should be connected. Based on this insight, we introduce DreamRAG, a chunk-to-event retrieval framework that decomposes a question into a dependency graph and represents each subquery-specific seed region with a virtual supernode. Its core mechanism, Dependency-Aligned Supernode Bridging, maps predicted dependencies to targeted minimum-cost path searches between the corresponding supernodes. DreamRAG further decouples chunk-level navigation from fine-grained event-level evidence selection. Experiments on HotpotQA, 2WikiMultihopQA, and MuSiQue demonstrate that DreamRAG significantly improves macro-average EM and F1 over strong dense and graph-based RAG baselines.