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

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.

15 pages. Accepted at the COLM 2nd Workshop on Language Models for Scientific Discovery

Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2608.14667 [cs.AI]

(or arXiv:2608.14667v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.14667 arXiv-issued DOI via DataCite (pending registration)