HKVM: Learning to Select Multi-Hop Evidence with Hypergraph Key-Value Memory

Multi-hop retrieval-augmented generation depends on selecting a small set of passages that jointly support an answer within a limited context budget. Relational indexes expose connections across passages, but those connections do not by themselves determine which evidence should be retained. We introduce HKVM (Hypergraph Key-Value Memory), a framework that couples relational indexing with learned evidence selection. HKVM groups locally extracted triples around shared bridge entities to form hypergraph retrieval keys, while retaining the original passages as values. A supervised selector combines structural and text-retrieval signals to rank candidate passages. We evaluate HKVM on the development splits of 2WikiMultiHopQA, MuSiQue, and HotpotQA with fixed candidate pools and an up-to-five-passage selection budget. Compared with a learned controller built on the same ColBERTv2 retrieval source, HKVM improves support-selection F1 (precision-recall F1 over supporting passages) by 1.61, 1.52, and 0.36 points and increases the fraction of questions retaining all supporting passages by 6.54, 3.57, and 1.16 percentage points, respectively. Across six retrieval sources, supervised reranking improves support-selection F1 for every source, while comparisons among learned alternatives vary by dataset and by whether performance is measured by average support or complete-support retention. These results show that relational indexing is most useful as a learned evidence-control layer: its value lies in how structural signals guide passage selection and preserve complete multi-hop support.