Consensus is designed to find the most relevant scientific papers for your results and ground our AI-generated summaries. Every search goes through three key stages:
Consensus begins by scanning its entire database of over 220 million paper to find the most relevant results using a hybrid approach:
Semantic search (AI embeddings): Captures the intent behind your search and supports natural language queries.
Keyword search (BM25): A traditional method that anchors results to the exact terms in your query for precise keyword matching.
These methods work together to assign a relevance score to each paper by comparing your query to the titles, abstracts, and full text (when available) in the database. This helps surface papers that match your exact words, while understanding what you're really looking for.
Next, Consensus takes the top 1,500 most relevant papers and re-rank them based on key research quality signals:
Recency of publication
Citation count
Journal impact and reputation
This step ensures your results are highly relevant and back by high-quality, credible science.
In this final step, Consensus ranks the top 20 papers as accurately as possible. This works similar to Step 2, but includes two key upgrades:
Relevance is recalculated using a larger, more powerful AI model
This high-precision model is applied only to the top 20 papers to ensure efficiency and accuracy
Consensus continues to factor in recency, citation count, and journal impact, so the final list reflects both relevance and rigor.