EMBL AI Librarian is introduced, a knowledge layer that upgrades the Europe PMC interface for AI agents that improves performance across a range of tasks: literature synthesis, claim verification, open-domain question answering, and downstream biology tasks such as protocol questions and sequence manipulation.
Abstract
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
It is argued that the IR community is well-positioned to advance this emerging problem setting, which is referred to as AgentSearch, by developing principled methods for representing, indexing, retrieving, and ranking AI agents and tools under multi-dimensional relevance criteria.
Bin Wu, To Eun Kim, Yue Feng et al.· Annual International ACM SIG...· 0 citations
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
The rapid emergence of agentic artificial intelligence (AI) has outpaced scholarly discussions of its implications for library reference services, creating a significant conceptual gap within the broader discourse on the Fifth Industrial Revolution (5IR). Existing studies have focused largely on chatbots, robotic technologies, and conventional AI applications, with limited attention to autonomous AI agents capable of reasoning, planning, and executing complex information tasks. This conceptual review addresses that gap by examining the transformative potential of agentic AI for reference services within the human-centred philosophy of the 5IR. Drawing on recent literature on agentic AI, Industry 5.0, and library and information science, the paper synthesises current knowledge to identify the defining characteristics, emerging applications, opportunities, challenges, and strategic implications of agentic AI for librarians. The review indicates that agentic AI can significantly enhance reference services through autonomous literature searching, personalised research support, intelligent knowledge discovery, multilingual assistance, and complex query resolution. Nevertheless, concerns relating to algorithmic bias, hallucination, transparency, data privacy, intellectual property, and professional accountability require careful governance. The paper argues that successful implementation depends on AI literacy, ethical oversight, human–AI collaboration, institutional governance frameworks, equitable access, and continuous evaluation. It provides a strategic framework for advancing innovative, ethical, and sustainable AI-enabled reference services.
Isu Michael Egbe, Y. Ajani, Sadiya Abubakar· Business Information Review· 0 citations
A language-model agent asked to analyse an experiment will usually return working code. Whether the analysis is defensible is a different question. A defensible analysis depends on procedural choices: which test the field accepts, which identifier namespace is authoritative, and which caveats must accompany a result. We present Scientific Agent Skills, an open library of 163 such procedures in 16 areas of practice, including genomics, cheminformatics, medical imaging, study design and scientific communication. Each skill is a directory built around a versioned, human-readable instruction file. An agent loads the file only when a task calls for it; the directory often also contains reference material and runnable scripts. We report no task-level evaluation and no host selection rate. We measure two properties of the documentation corpus: the always-resident descriptions of all 163 skills cost 7.1% of a 200,000-token window, and the median documented workflow fits within 23.9% of it, although 29 of 46 would overflow if every reference file were loaded. Openly licensed and available at https://github.com/K-Dense-AI/scientific-agent-skills.
T. Kassis, Vinayak Agarwal, Yuhuan He et al.· 0 citations
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Jianlyu Chen, Yuyang Hu, Hong-Jin Qian et al.· 1 citation
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