IndexRAG: Index-Time Reasoning for Multi-Hop Retrieval-Augmented Generation
Multi-hop question answering (QA) requires reasoning across multiple documents, yet existing retrieval-augmented generation (RAG) approaches address this either through graph-based methods requiring additional online processing or iterative multi-step reasoning. We present IndexRAG, a novel approach that shifts cross-document reasoning from online inference to offline indexing. IndexRAG identifies bridge entities shared across documents and generates bridging facts as independently retrievable units, requiring no additional training or fine-tuning. Experiments on three widely-used multi-hop QA benchmarks (HotpotQA, 2WikiMultiHopQA, MuSiQue) show that IndexRAG improves F1 over Naive RAG by...
| Authors | Zhenghua Bao, Yi Shi |
|---|---|
| Primary category | cs.CL |
| Published | 17 March 2026 |
| Last updated at source | 28 September 2026 |
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