Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding. This assumption breaks down for complex queries, such as multi-hop or ambiguous questions, where top-k selection tends to over-cover one semantic aspect while ignoring critical sub-questions. We propose GeoRAG, which recasts context selection as Information Demand Coverage Optimization. GeoRAG builds a multi-dimensional demand distribution through diverse sub-query generation and reverse-validation weighting, then selects context by minimizing the Sinkhorn-Wasserstein distance between this demand distribution and the coverage of the selected set. The resulting demand...
| Authors | Bingxue Zhang, Jianying Jia, Feida Zhu |
|---|---|
| Primary category | cs.IR |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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