GRaSP: Latency-Aware Optimization for Geo-Distributed Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is increasingly served across geo-distributed cloud and edge infrastructures, where wide-area latency, cross-region egress cost, and data-residency rules compete with retrieval quality. Conventional pipelines rank by semantic similarity alone and ignore where content lives, inflating tail latency and cost. We formalize the Geo-RAG Selection Problem (GRaSP), a drop-in re-scoring layer that decays relevance by latency and adds an optional cost penalty under a cardinality budget and per-region caps, with no retraining. We first settle its complexity: the base formulation is a linear objective over a partition matroid, solved optimally by an O(N log N) gre...
| Authors | Hardik Ruparel, Raj Kumar Pandey, Pramod Kumar |
|---|---|
| Primary category | cs.IR |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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