Foundation models (FMs), such as GPT-4 and AlphaFold, are reshaping the landscape of scientific research. Beyond accelerating tasks such as hypothesis generation, experimental design, and result interpretation, they prompt a more fundamental question: Are FMs merely enhancing existing scientific methodologies, or are they redefining the way science is conducted? In this paper, we argue that FMs are catalyzing a transition toward a new scientific paradigm. We introduce a three-stage framework to describe this evolution: (1) Meta-Scientific Integration, where FMs enhance workflows within traditional paradigms; (2) Hybrid Human-AI Co-Creation, where FMs become active collaborators in problem formulation, reasoning, and discovery; and (3) Autonomous Scientific Discovery, where FMs operate as independent agents capable of generating new scientific knowledge with minimal human intervention. Through this lens, we review current applications and emerging capabilities of FMs across existing scientific paradigms. We further identify risks and future directions for FM-enabled scientific discovery. This position paper aims to support the scientific community in understanding the transformative role of FMs and to foster reflection on the future of scientific discovery. Our project is available at https://github.com/usail-hkust/Awesome-Foundation-Models-for-Scientific-Discovery.

翻译：以GPT-4和AlphaFold为代表的基础模型正在重塑科学研究的面貌。除了加速假设生成、实验设计和结果解释等任务外，它们引发了一个更为根本的问题：基础模型仅仅是增强现有科学方法论，还是在重新定义科学实践的方式？本文认为，基础模型正在催化向新科学范式的过渡。我们提出了一个三阶段框架来描述这一演进过程：（1）元科学整合阶段，基础模型在传统范式内增强工作流程；（2）人机混合协同创造阶段，基础模型成为问题构建、推理与发现过程中的主动协作伙伴；（3）自主科学发现阶段，基础模型作为独立智能体，能够在极少人为干预下生成新的科学知识。基于此框架，我们系统回顾了基础模型在现有科学范式中的当前应用与新兴能力，并进一步识别了基础模型赋能科学发现的风险与未来发展方向。本立场论文旨在帮助科学界理解基础模型的变革性作用，并促进对科学发现未来的深入思考。项目资源发布于 https://github.com/usail-hkust/Awesome-Foundation-Models-for-Scientific-Discovery。