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Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Evaluating first-stage retrievers in large-scale production RAG requires a benchmark that pairs a large-scale corpus with a large set of agent-reformulated search queries based on real user queries and their conversation threads, and that labels many relevant documents per query. No existing public benchmark evaluates this setting: large-scale collections typically provide only a small number of evaluation queries, whereas benchmarks with many queries generally contain only millions of documents. Moreover, most benchmarks assess human-written queries, while the first-stage retrievers in agenti

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