Autonomous Discovery of Particle Physics Theories from Experimental Data

The search for physics beyond the Standard Model is hindered by a combinatorial explosion of possible theories. We introduce $\text{textsc}Albert$, a neuro-symbolic artificial intelligence framework to systematically navigate this vast theory space. By encoding particle physics as a formal language, $\text{textsc}Albert$ generates tokenized sequences representing symmetries, particles, and interactions under a rule-based grammar, eliminating the hallucinations common in large language models. The reinforcement learning environment enforces first-principle theoretical constraints, computes observables with radiative corrections, and evaluates statistical likelihood via ${\chi }^2$ analysis against experimental data. As a proof of concept, we train a 25-million-parameter transformer model using only legacy data from the Large Electron-Positron Collider, which contains no direct evidence of the top quark. Remarkably, $\text{textsc}Albert$ successfully rediscovered the Standard Model and autonomously inferred necessity and properties of the top quark, predicting its mass at $178.9\pm 5.0\text{ GeV}$, consistent with its modern measurement at the Large Hadron Collider. These results demonstrate the potential of AI-driven theory exploration as a rigorous, hallucination-free, and scalable paradigm for autonomous discovery of new physics.