ClaimWeaver: Query-Conditioned Evidence Organization for Multi-Hop Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) can retrieve relevant evidence yet fail to connect the facts needed to answer a question. This gap is acute in multi-hop question answering, where passages mix answer-bearing facts with duplicates and distractors, while the required dependencies span multiple documents. Free-form notes condense this context, but decisions about which facts to discard, combine, or connect remain implicit in prose. We introduce ClaimWeaver, a query-conditioned evidence organization framework over atomic claims. It transforms retrieved passages into source-attributed claims and builds a compact structure in which irrelevant claims are suppressed, redundant claims are consolidated, and complementary claims are linked by directed bridges. This structure persists across retrieval rounds, focuses subsequent search, and provides answer generation with an explicit evidence path. A composite reinforcement-learning objective learns this behavior without gold organization labels. Across seven QA benchmarks, ClaimWeaver achieves 50.5 EM and 56.4 F1. Compared with a configuration-matched free-form-note control, it improves average EM by 4.5 points across four multi-hop benchmarks while maintaining average single-hop EM. Human audits assess decomposed-claim faithfulness and pairwise relations, while retrained ablations and fixed-evidence interventions examine the role of organization. These results support query-conditioned claim organization as an effective interface between retrieval and multi-hop reasoning.