立場：科學團隊中的AI代理應被視為人-代理系統來研究

摘要

基於大型語言模型的智能體日益被部署為科學發現中的協作者，然而目前大多數研究聚焦於「AI科學家」的自主能力。我們主張，這種觀點忽略了科學團隊合作的社會層面，而將AI科學家視為人類-智能體系統（HAS）——其分析單位為人類-智能體對——來研究，既未得到充分探索，也未受到足夠重視。我們透過文獻與實證分析確立上述觀點，並強調近期的實例與研究顯示，在科學中部署智能體而不將人類-智能體動態納入考量，會帶來近期風險，包括科學探究多樣性的降低。透過對真實世界案例研究的分析，我們顯示科學家與智能體能夠相互增強彼此的能力。我們呼籲採用HAS視角進行新的研究，以建立數學框架，來理解並促進科學發現中的人類-AI協同。

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.