EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
- Published
- Source
- arXiv
- Paper number
- 784
- Field
- LLMs / NLP
- arXiv ID
- 2607.28229
Key points
- It builds a natural-language knowledge layer for AI agents on top of Europe PMC, which contains 40 million records.
- A single LLM orchestrates the full pipeline, from query planning to execution, paper parsing, and evidence-snippet extraction.
- It handles the process through live search instead of a dense vector database that would require 744 GB, which greatly reduces infrastructure cost.
- On ScholarQA-Bench, it improves Citation F1 by 16 points, and on LitQA2 it raises accuracy from 70.3 with GPT-5.4 plus web search to 78.9 with GPT-5.4 plus the Librarian.
- Applied to the ProClaim verification pipeline, it improves agreement with expert consensus by 5 points, and the code is public.
Paper links
External research summaries. These are not HDATF publications or measured product results.