Foundation Models for Scientific Discovery: From Paradigm Enhancement to
  Paradigm Transition
Authors: name: Fan Liu, name: Jindong Han, name: Tengfei Lyu, name: Weijia Zhang, name: Zhe-Rui Yang, name: Lu Dai, name: Cancheng Liu, name: Hao Liu
Organization: usail-hkust

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.