Perplexity launches Q2D-Web, a benchmark and leaderboard for agentic RAG retrieval
perplexity_ai · x · 2026-09-10
Perplexity released Q2D-Web (Query2Doc-Web), a public benchmark and leaderboard evaluating retrieval in agentic RAG systems, testing how embedding models handle agent-reformulated search queries at web scale.
Key details:
- Corpus: top 5,000 production retrieval results per query, deduplicated with MinHash-LSH; every document plausibly matches at least one query, including hard distractors that match the topic but miss a required date, entity, or version.
- Relevance labels: three sets built from agent citations, production web rankings, and LLM-judgment expansions, reducing false negatives and single-pipeline dependence.
- Coverage: programming, law, health, science, finance, consumer goods, travel, entertainment, and local info across ten languages (English 65.8%).
Related event: Perplexity Releases Q2D-Web, an Open Benchmark for Agentic RAG Retrieval(10 posts)→
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