Work

Towards AI-assisted neutrino flavor theory design

Year: 2026

Type: article

Abstract: Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model’s construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data. We develop Autonomous Model Builder (AMBer), a framework in which a reinforcement learning agent interacts with a streamlined physics software pipeline to search these spaces efficiently. AMBer selects symmetry groups, particle content, and group representation assignments to construct models while minimizing the number of free parameters introduced. We validate our approach in well-studied regions of theory space and extend the exploration to a previously unexamined symmetry group. While demonstrated in the context of neutrino flavor theories, this approach of reinforcement learning with physics software feedback may be extended to other theoretical model-building problems in the future. The vast landscape of particle physics model-building, particularly for neutrino flavor mixing, presents challenges in identifying appropriate symmetry groups and minimizing theoretical complexity. Here, the authors develop an Autonomous Model Builder (AMBer) using reinforcement learning to efficiently explore theory spaces, offering a novel approach that could transform theoretical model-building across physics.

Source: Communications Physics

DOI: 10.1038/s42005-026-02627-2