Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transition

2025-10-16

AI Summary

Foundational models (e.g., GPT-4, AlphaFold) are shifting scientific research from “tool-augmented” to “autonomous discovery.” This paper proposes the first systematic, three-stage paradigm-transition framework: (1) embedding foundation models into conventional research workflows; (2) human-AI co-generation and validation of hypotheses; and (3) fully autonomous, closed-loop scientific exploration. Methodologically, we integrate metascientific analysis, human-AI collaboration mechanisms, and autonomous agent technologies to build a scalable technical framework, empirically characterizing its applicability boundaries, risks, and evolutionary trajectories across disciplines. Our core contributions are threefold: (i) the first formal theoretical and structural articulation of pathways toward autonomous scientific discovery; (ii) a reconceptualization of scientific agency and knowledge-production logic; and (iii) open-sourcing of an evaluation benchmark and prototype system to foster community-driven development and paradigm evolution.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Assessing whether foundation models enhance or redefine scientific methodologies
• Proposing a three-stage framework for AI evolution in scientific discovery
• Exploring risks and future directions of AI-driven scientific research

Innovation

• FMs enhance workflows within traditional scientific paradigms
• FMs collaborate with humans in problem formulation and reasoning
• FMs operate as independent agents for autonomous discovery