Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation
For RAG systems handling complex queries, GeoRAG provides a principled, unsupervised method to overcome the structural limit of ranking-based selection, achieving strong gains over multiple baselines.
GeoRAG recasts context selection in RAG as an information demand coverage optimization problem, using Sinkhorn-Wasserstein distance to select diverse context. It improves exact match by +6.5 to +7.5 points over top-k truncation on six open-domain QA benchmarks, with up to +9.7 on HotpotQA and ASQA.
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-weighted facility-location objective is monotone submodular, giving a $1-1/e$ greedy guarantee, which we approximate with a Sinkhorn-based marginal-gain surrogate. The method is unsupervised, training-free, and retrieval-agnostic. We further show that single-point, query-proximity scorers cannot cover multi-modal demands, exposing a structural limit of ranking-based selection. On six open-domain QA benchmarks, GeoRAG improves exact match (EM) by +6.5 to +7.5 points over top-k truncation (up to +9.7 on HotpotQA and ASQA) and outperforms strong baselines including MMR, DPP, BGE-Reranker, SMART-RAG, and AdaGReS, with stable gains across context budgets and sub-query generators.