Retrievers for agentic AI systems need to be evaluated and trained differently—they must surface complementary evidence across multiple aspects and search iterations, not just find topically similar passages.
This paper tackles how search systems find evidence for AI agents that need to reason through complex problems. Current retrieval systems just match keywords, but agentic systems need diverse, complementary evidence across multiple search rounds.