Towards AI-assisted Neutrino Flavor Theory Design

arXiv stat.ML / 4/17/2026

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Key Points

The paper proposes AMBer, an Autonomous Model Builder that uses reinforcement learning to automate key steps in neutrino flavor model construction, including choosing symmetry groups and assigning field representations.
AMBer interacts with a streamlined physics-software pipeline to search the model-building space efficiently while aiming to minimize newly introduced free parameters.
The authors validate the method in established regions of neutrino flavor theory space and then apply it to explore a new, previously unexamined symmetry group.
Although the work is demonstrated for neutrino flavor theories, the authors argue the RL-plus-physics-software-feedback approach could generalize to other particle-physics model-building problems.
Overall, the study frames theorist intuition as something that can be partially replaced by an automated search guided by physics computation and experimental comparison needs.

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 an 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 viable 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 novel, 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.