When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) typically assumes that external knowledge is free, but many high-quality sources are paywalled, licensed, restricted, or otherwise costly to access. We introduce cost-aware RAG, a setting where retrieved evidence is assigned access-cost tiers and systems must answer under an explicit evidence-access budget. We instantiate this setting by augmenting MS MARCO v2.1 with access-friction tiers and evaluate budgeted evidence selection across general-domain and domain-specific QA benchmarks. Our results show that static selection is brittle: no fixed selector uniformly dominates, and larger budgets do not reliably improve answer quality, even when costly evid...
| Authors | Mingyan Wu, Han Yang, Omer Ben-Porat et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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