When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2606.02245v1
• Date: Mon, 01 Jun 2026 13:39:39 GMT
• Title: When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generation
• Abstract summary: 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.<n>We study agentic cost-aware RAG, where an LLM decides when to retrieve, which tier to access, and when to stop.<n>These findings suggest that cost-aware evidence acquisition is a central challenge for the next generation of RAG systems.
• Score: 14.80584946722667
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 evidence is domain-matched. We then study agentic cost-aware RAG, where an LLM decides when to retrieve, which tier to access, and when to stop. Agents show strong promise as adaptive evidence-acquisition controllers, but their behavior remains highly model- and task-dependent. These findings suggest that cost-aware evidence acquisition is a central challenge for the next generation of RAG systems. All code and data are available at https://github.com/Mignonmy/Cost-Aware.

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