Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough

2026-07-10

AI Summary

This work proposes the VERaiPHY framework to ensure the reliability and scientific rigor of machine learning systems in fundamental physics discovery. It systematically delineates, for the first time, the validation requirements and applicability boundaries of artificial intelligence across particle physics, astrophysics, and cosmology. By integrating statistical inference, hypothesis testing, and machine learning verification methodologies, the framework establishes a reliability assessment paradigm tailored to fundamental physics. It elucidates how inductive biases, sample complexity, and experimental constraints fundamentally limit AI-driven scientific discovery. Furthermore, the study underscores the dual role of physicists—as both domain experts and evaluators—in the design and validation of AI systems, thereby providing a theoretical foundation and practical guidance for the responsible integration of artificial intelligence into scientific discovery processes.

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.

Problem

• AI verification
• fundamental physics
• machine learning reliability
• scientific discovery
• inductive bias

Innovation

• AI verification
• fundamental physics
• machine learning reliability
• statistical discovery workflow
• inductive bias