# EMBL AI Librarian：面向AI智能体的生命科学知识层

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-07-30 22:00
- AIHOT 分数：57
- AIHOT 链接：https://aihot.virxact.com/items/cmsdcn9gc00itrotj7pytfndi
- 原文链接：https://arxiv.org/abs/2607.28229

## AI 摘要

EMBL AI Librarian是一个知识层，将Europe PMC接口升级为面向AI智能体的自然语言问答形式，由单个LLM规划互补子查询并阅读论文定位证据。在ScholarQABench上，其Citation F1比近期强基线提升超16个百分点；在LitQA2基准上，接入Librarian的GPT-5.4智能体比使用网页搜索得分高约8分。代码已公开。

## 正文

The web is increasingly accessed by AI agents rather than humans. Every agent needs knowledge, especially in the life-sciences, where agentic pipelines are growing fast. Access to the literature is a crucial part of that need, and resources such as Europe PMC, with over 40M indexed records, are widely used to meet it. Yet these resources were not built for AI agents: they take keywords and complex syntax and return whole papers, so every agent must learn the syntax, issue several searches, and read full papers to find the evidence it needs. We introduce EMBL AI Librarian, a knowledge layer that upgrades the Europe PMC interface for AI agents: an agent asks in natural language and receives evidence that answers it. A single LLM orchestrates the whole knowledge retrieval process: it plans complementary subqueries executed by the live Europe PMC search engine, then reads the selected papers and locates the relevant evidence. We evaluate Librarian across four benchmarks: literature synthesis, claim verification, open-domain question answering, and downstream biology tasks such as protocol questions and sequence manipulation. On ScholarQABench, Librarian improves Citation F1 by more than $16$ points over strong recently published baselines. Used as the retrieval layer of an existing claim-verification pipeline, it increases agreement with expert consensus; and on the open-form LitQA2 benchmark, a GPT-5.4 agent scores about $8$ points higher when grounded in Librarian than with web search. Overall, our results show that equipping life-science agents with the Librarian knowledge layer improves performance across a range of tasks. We release our code publicly at https://github.com/petroni-lab/librarian