把AI科学家当人类协作伙伴看，才能更好推动科学发现。
Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
- 将人与AI视为协作单元，研究双方互动机制
- 实证显示忽略协作关系会降低科研多样性
- 适合关注人机协同的科研人员与政策制定者
基于大语言模型的AI代理正越来越多地作为科学发现的合作者，但当前研究多聚焦于AI的自主能力，忽视了科学团队中的社会性。我们主张应将AI科学家视为人机系统（HAS），以人-机协作对为基本分析单位，这一视角既被低估又亟待探索。通过文献与实证分析，我们指出在缺乏对人机动态考量的情况下部署代理，可能带来近似风险，如科学探究多样性的下降。通过对真实案例的研究，我们发现科学家与代理可相互增强能力。我们呼吁开展新研究，采用人机系统视角，建立数学框架以理解并促进科学发现中的人机协同。
原文摘要 · Abstract (English)
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.
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