Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems

摘要

Reasoning-intensive retrieval aims to surface evidence that maximizes downstream reasoning utility rather than only topical similarity. This capability is increasingly vital for agentic retriever-in-the-loop systems such as Deep-Research. However, existing retriever evaluation benchmarks, exemplified by Bright, provide narrow gold sets and evaluate retrievers in isolation, which obscures their value inside realistic agent workflows. We introduce Bright-Pro, an evaluation framework that assesses the effectiveness of retrievers in agentic search systems. Bright-Pro covers a broad range of queries across diverse professional domains. For each query, we provide expert-annotated reasoning aspects, positive documents, a reference response, and evaluation rubrics, enabling fine-grained assessment of retriever performance. Beyond static evaluation, we further assess retrievers in the context of agentic search systems, measuring their practical utility when serving as core components within agentic workflows. Using Bright-Pro, we evaluate classical lexical, general-purpose, and reasoning-intensive retrievers, providing actionable insights for future retriever development.