Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

[2608.14667] Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14667 (cs) [Submitted on 2 Aug 2026] Title: Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems Authors: Patrick Emami , Sameera Horawalavithana , Truc Nguyen , Gihan Panapitiya , Bruno Jacob , Siddhisanket Raskar , Saumya Sinha , Jared D. Willard , Andrew Glaws , Nithin Somasekharan , Ling Yue , Brian Lu , Shaowu Pan , Jason Eisner View a PDF of the paper titled Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems, by Patrick Emami and 13 other authors View PDF HTML (experimental) Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists". We argue that this overlooks the social aspects of scientific teamwork, and that studying AI Scientists as human-agent systems (HAS)--where the unit of analysis is the human-agent pair--is both underexplored and undervalued. We establish these points through literature and empirical analysis, and highlight recent incidences and studies which show that deploying agents in science without accounting for human-agent dynamics introduces near-term risks, including reduced diversity of scientific inquiry. Through analysis of real-world case studies, we show that scientists and agents can augment each other's capabilities. We call for new research that adopts the HAS lens to develop mathematical frameworks for understanding and fostering human-AI synergy in scientific discovery. Comments: 15 pages. Accepted at the COLM 2nd Workshop on Language Models for Scientific Discovery Subjects: Artificial Intelligence (cs.AI) ; Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.14667 [cs.AI] (or arXiv:2608.14667v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14667 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Patrick Emami [ view email ] [v1] Sun, 2 Aug 2026 05:27:36 UTC (53 KB) Full-text links: Access Paper: View a PDF of the paper titled Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems, by Patrick Emami and 13 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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