Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
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...
| Authors | Gaia Grosso, Vinicius Mikuni, Lukas Heinrich |
|---|---|
| Primary category | physics.data-an |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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