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

Key points

• Problem: The authors argue that as science increasingly deals with emergent behavior, open-endedness and irreducible complexity, the limits of the four existing paradigms (experimental, theoretical, computational, data-driven) have become more evident. In drug discovery and material design the combinatorial explosion of candidate spaces makes exhaustive search infeasible, and the accumulation of experimental and observational data has outpaced the capacity to synthesize unifying theories. They therefore ask whether FMs merely enhance existing methodologies or redefine the way science is conducted.
• Argument: This is a position paper whose stated contributions are a conceptual framework, a systematic review and taxonomy, and a research agenda. The authors take the position that FMs are not only improving parts of the scientific process but beginning to reshape the paradigm through which science operates, and they call the state reached once the autonomous stage is realized the fifth scientific paradigm. They also present the opposing view that FMs are powerful yet conventional tools that amplify existing methodologies.
• Taxonomy: In stage 1, Meta-Scientific Integration, FMs are backend tools that automate procedures such as data preprocessing, literature retrieval and methodology matching within human-defined objectives, with low autonomy. In stage 2, Hybrid Human-AI Co-Creation, FMs are collaborators in research questions, hypotheses and experiment planning, while their actions are still initiated, constrained and validated by humans, with moderate autonomy. In stage 3, Autonomous Scientific Discovery, which the authors present as a forward-looking vision, FMs are independent agents that run the cycle from posing questions to executing experiments or simulations and interpreting results with minimal or no human oversight.
• Review: Section 4 organizes cases from stage 1 and early stage 2 by paradigm, citing FMs as priors or feature extractors in Bayesian optimization and FM-generated Python control scripts for instruments in the experimental paradigm, and knowledge-graph-guided hypothesis generation and formal reasoning such as Logic-LM, which couples LLMs with symbolic solvers in a feedback loop, and LeanCopilot, which assists proof construction and verification, in the theoretical paradigm. For computation it cites LLM-SR, which turns plots or text into equation skeletons, FunSearch, Neural Operator and GraphCast, for data AlphaFold 2, ESMFold and MatterGen, and as a cross-paradigm case Coscientist, which translates research goals into machine-executable protocols and controls robotic synthesis. The official repository README holds a paper list by paradigm together with example documents for topic exploration, literature survey, problem refinement, methodology design and experiment code produced with OpenAI Deep Research and ChatGPT.
• Risks and directions: The authors list four risks that grow as FMs move from tools to autonomous agents: bias and epistemic fairness inherited from training data, hallucination that produces plausible but unverified claims, reproducibility and transparency problems from opaque decision-making, and authorship, accountability and research ethics. As mitigations they propose more diverse training data and fairness-aware evaluation, symbolic logic checks and simulation-based validation, human-in-the-loop review and provenance tracking, logging of reasoning steps and version-controlled model checkpoints, and governance that mandates transparent disclosure of AI contributions. As future directions they name embodied scientific agents working with lab robotics, instruments and digital twins, closed-loop autonomy in which FMs continuously run from hypotheses through experiments to analysis, and continual learning that accumulates and refines knowledge.
• Limits: The argument rests on a conceptual framework and a review of existing work, and the authors state that the transformation is still in its early stages and that autonomous discovery is in its formative stages. They also note that most FM deployments remain confined to static prompts, predefined tasks and fixed schema and typically lack persistent memory, adaptive feedback and physical embodiment, so their contributions are largely reactive and limited to isolated stages of research. Open challenges they list include linking high-level planning with low-level control in embodied agents, robustness of closed loops to noisy observations and shifting objectives, and robust transfer across heterogeneous tasks in continual learning.

Paper links

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