Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
Authors: Gaia Grosso, Vinicius Mikuni, Lukas Heinrich
Organizations: NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, MA · MIT Laboratory for Nuclear Science, Cambridge, MA · School of Engineering and Applied Sciences, Harvard University, Cambridge, MA · Nagoya University, Kobayashi-Maskawa Institute, Japan · Technical University Munich · Munich Center for Machine Learning (MCML)
Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems grow increasingly autonomous, ensuring their reliability for discovery claims becomes critical. This review synthesizes the VERaiPHY (Validation & Evaluation for Robust AI in PHYsics) initiative's frameworks for rigorous ML assessment across particle physics, astrophysics, and cosmology. We establish when verification is essential by contextualizing ML within the statistical discovery workflow. We emphasize fundamental limitations: inductive bias is unavoidable, sample complexity bounds learning, and experimental constraints limit discovery. We reflect on physicists' evolving role as both experimental designers and evaluators whose judgments encode scientific rigor into AI systems. Responsible integration requires understanding ML's transformative potential alongside its intrinsic boundaries.