We're introducing Q2D-Web (Query2Doc-Web), a benchmark and public leaderboard for evaluating retrieval in agentic RAG systems.
Q2D-Web tests how embedding models perform on large-scale web search using agent-reformulated search queries.
Read more: https://t.co/s476SxkE1L
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
Why it matters
A new public benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition → and leaderboard for retrieval quality on AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition →-reformulated web queries, letting engineers pick embeddingA list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.Full definition → models by measured Recall@1000 instead of vendor marketing.
