Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transition
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909852499968000 |
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| author | Liu, Fan Han, Jindong Lyu, Tengfei Zhang, Weijia Yang, Zhe-Rui Dai, Lu Liu, Cancheng Liu, Hao |
| author_facet | Liu, Fan Han, Jindong Lyu, Tengfei Zhang, Weijia Yang, Zhe-Rui Dai, Lu Liu, Cancheng Liu, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15280 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transition Liu, Fan Han, Jindong Lyu, Tengfei Zhang, Weijia Yang, Zhe-Rui Dai, Lu Liu, Cancheng Liu, Hao Machine Learning Artificial Intelligence Digital Libraries 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. |
| title | Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transition |
| topic | Machine Learning Artificial Intelligence Digital Libraries |
| url | https://arxiv.org/abs/2510.15280 |