EMBL AI图书馆员：面向AI智能体的生命科学知识层

摘要

网络越来越多地由AI智能体而非人类访问。每个智能体都需要知识，尤其是在生命科学领域，智能体流水线正快速增长。获取文献是满足这一需求的关键部分，Europe PMC等资源拥有超过4000万条索引记录，被广泛用于此目的。然而，这些资源并非为AI智能体而构建：它们接收关键词和复杂语法，并返回整篇论文，因此每个智能体都必须学习其语法、进行多次搜索并阅读全文，才能找到所需证据。我们推出了EMBL AI Librarian，这是一个知识层，将Europe PMC接口升级为面向AI智能体：智能体用自然语言提问，即可获得回答该问题的证据。单个LLM编排整个知识检索过程：它规划由实时Europe PMC搜索引擎执行的互补子查询，然后阅读选定的论文并定位相关证据。我们在四个基准上评估了Librarian：文献综合、声明验证、开放域问答，以及下游生物学任务（如实验方案问题和序列操作）。在ScholarQABench上，Librarian的引用F1比近期发布的强基线提高了超过16个百分点。作为现有声明验证流水线的检索层使用时，它提高了与专家共识的一致性；在开放式LitQA2基准上，GPT-5.4智能体基于Librarian时比使用网络搜索得分高出约8个百分点。总体而言，我们的结果表明，为生命科学智能体配备Librarian知识层可提高一系列任务的性能。我们在https://github.com/petroni-lab/librarian公开发布代码。

English

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