Foundation Models for Scientific Discovery: From Paradigm Enhancement to
  Paradigm Transition
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.
68f59b3a8589920bf4d3210e
Fan Liu
68f59b3a8589920bf4d3210f
Jindong Han
68f59b3a8589920bf4d32110
Tengfei Lyu
68f59b3a8589920bf4d32111
Weijia Zhang
68f59b3a8589920bf4d32112
Zhe-Rui Yang
68f59b3a8589920bf4d32113
Lu Dai
68f59b3a8589920bf4d32114
Cancheng Liu
68f59b3a8589920bf4d32115
Hao Liu
Foundation Models for Scientific Discovery: From Paradigm Enhancement to
  Paradigm Transition
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.
Foundation models are evolving scientific methodologies from enhancement to autonomous discovery, prompting a new paradigm in research.