Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
2026-07-10 • Machine Learning
Machine Learning
AI summaryⓘ
The authors explain that machine learning is now important in physics for speeding up data analysis and testing ideas. They review ways to check that these machine learning tools are trustworthy, especially as they become more independent. The authors point out that machine learning always has some built-in assumptions and needs enough data to learn well, and that real experiments have limits that affect discoveries. They also discuss how physicists need to carefully design experiments and check AI results to keep science reliable.
machine learningstatistical inferenceverificationinductive biassample complexityexperimental constraintsparticle physicsastrophysicscosmologyscientific rigor
Authors
Gaia Grosso, Vinicius Mikuni, Lukas Heinrich
Abstract
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.