A 190-million-document test for web retrievers
A retriever can look strong on a small benchmark simply because the hard negatives never made it into the room. Q2D-Web scales evaluation to 190 million web documents and 69,721 agent-reformulated queries across ten languages, then checks relevance three ways: citations, production rankings, and additional LLM judgments. That makes leaderboard gains harder to fake and more relevant to the first stage of agentic RAG.