Autonomous Discovery of Particle Physics Theories from Experimental Data
Stephon Alexander, Benjamin Bradley, Loukas Gouskos, Cooper Niu

TL;DR
The paper presents extsc{Albert}, a neuro-symbolic AI framework that autonomously explores particle physics theories from experimental data, successfully rediscovering the Standard Model and predicting the top quark's properties.
Contribution
Introducing extsc{Albert}, a novel AI system that systematically navigates the theory space of particle physics using formal language encoding and reinforcement learning, avoiding hallucinations.
Findings
extsc{Albert} rediscovered the Standard Model from legacy data.
It predicted the top quark mass as 178.9±5.0 GeV.
The framework demonstrated autonomous theory inference consistent with modern measurements.
Abstract
The search for physics beyond the Standard Model is hindered by a combinatorial explosion of possible theories. We introduce \textsc{Albert}, a neuro-symbolic artificial intelligence framework to systematically navigate this vast theory space. By encoding particle physics as a formal language, \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 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…
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