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

Fan Liu, Jindong Han, Tengfei Lyu, Weijia Zhang, Zhe-Rui Yang, Lu Dai, Cancheng Liu, Hao Liu

20 Oct 2025 3 min read

AI-generated image, based on the article abstract

Quick Insight

How AI Foundations Like GPT‑4 Are Changing the Way Science Works

Imagine a lab partner that never sleeps, can read millions of papers in seconds, and even suggests fresh experiments. Scientists have discovered that powerful AI tools such as GPT‑4 and AlphaFold are becoming that partner, turning the traditional “think‑then‑test” routine into a faster, smarter adventure. First, these models help researchers tidy up data and spot patterns—like a super‑charged microscope for ideas. Next, they join forces with humans, brainstorming hypotheses together, much like a duet where each voice lifts the other. Finally, researchers are dreaming of AI that can run entire studies on its own, proposing questions and testing answers with minimal human input. This shift is not just a speed‑up; it could rewrite how breakthroughs happen, from new medicines to greener materials. It’s a breakthrough that promises everyday benefits—healthier drugs reaching us sooner and smarter solutions for climate challenges. As we watch this AI‑driven wave roll in, we’re reminded that the future of discovery may be a partnership where human curiosity and machine brilliance walk hand in hand. Stay curious and watch the science of tomorrow unfold today.

Short Review

Overview: Foundation Models Reshaping Scientific Discovery

The article argues that Foundation Models (FMs) are fundamentally redefining scientific research, moving beyond mere enhancement of existing methodologies. It introduces a robust three-stage framework: Meta-Scientific Integration, Hybrid Human-AI Co-Creation, and Autonomous Scientific Discovery. Through this lens, the paper reviews current applications and emerging capabilities across scientific paradigms, identifying critical risks and outlining future directions for FM-enabled scientific advancement. 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.

Critical Evaluation: Assessing the Transformative Role of FMs

Strengths: A Vision for AI-Driven Science

The paper's primary strength lies in its visionary and structured approach to understanding the evolving role of Foundation Models in science. The proposed three-stage framework offers a clear, progressive model for conceptualizing how FMs integrate and transform scientific processes, from augmenting existing tasks to enabling autonomous discovery. This framework provides a valuable lens for researchers to categorize and anticipate AI's impact. Furthermore, the article delivers a comprehensive review of current FM applications, illustrating their utility in enhancing experiment design, accelerating computation-driven science, and facilitating knowledge discovery from multimodal data. By tracing the evolution of scientific paradigms, the paper effectively highlights the immense potential of AI to reshape scientific inquiry and accelerate breakthroughs.

Weaknesses: Navigating the Challenges of Autonomous Discovery

While the paper effectively outlines the transformative potential, it also critically identifies significant challenges associated with the increasing autonomy of Foundation Models. Key risks include potential for bias and hallucination in FM outputs, compromising scientific integrity and reproducibility. The article also raises crucial ethical concerns, such as authorship and epistemic fairness, where training data can inadvertently shape scientific agendas and outcomes. The increasing complexity and "black box" nature of FMs pose challenges for interpretability and validation, necessitating robust mechanisms for responsible governance. While these risks are clearly articulated, the paper primarily identifies them, underscoring the need for further research and practical solutions to mitigate these challenges.

Conclusion: Charting the Future of Scientific Research with FMs

In conclusion, this paper makes an impactful contribution by framing Foundation Models not merely as advanced tools but as catalysts for a new scientific paradigm. Its detailed three-stage framework provides a crucial roadmap for understanding the progression from AI-enhanced workflows to fully autonomous scientific discovery. By comprehensively reviewing current applications and foresightfully identifying both the immense potential and the critical risks, the article serves as an essential guide for the scientific community. It successfully fosters reflection on the profound implications of FMs, urging a proactive and responsible approach to integrating these powerful technologies to shape the future of scientific research.