立场：科学团队中的智能体应作为人机系统（HAS）进行研究
Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
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中文总结 AI 辅助
针对当前AI科学家研究忽视科学团队社会层面的问题，提出将其作为人机系统（HAS）研究，分析相关风险并呼吁开发人机协同数学框架。
中文摘要 AI 辅助
基于大语言模型的智能体正越来越多地被部署为科学发现中的合作者，但当前大多数研究聚焦于“AI科学家”的自主能力。我们认为这忽视了科学团队的社会层面，将AI科学家作为人机系统（HAS，分析单位为人机对）进行研究，既未被充分探索也未得到应有的重视。我们通过文献和实证分析确立这些观点，并强调近期案例与研究表明，在科学中部署智能体时若不考虑人机动态，会带来近期风险，包括科学探究多样性降低。通过真实案例研究分析，我们发现科学家与智能体可增强彼此能力。我们呼吁开展新研究，采用HAS视角开发用于理解和促进科学发现中人机协同的数学框架。
英文摘要
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.
