# Diffusion-model approach to flavor models: A case study for S4^prime modular flavor model

arXiv: https://arxiv.org/abs/2504.00944 · HTML: https://mlsanity.com/paper/2504.00944/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://mlsanity.com/paper/2304.14176/) (2023)
- [Reinforcement learning-based statistical search strategy for an axion model from flavor](https://mlsanity.com/paper/2409.10023/) (2024)
- [Exploring the flavor structure of leptons via diffusion models](https://mlsanity.com/paper/2503.21432/) (2025)

## Built on by (most cited first, 1 of 1)

- [Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data](https://mlsanity.com/paper/2607.08384/) (2026)

## Abstract (arXiv)

We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models, which are classified as a type of generative artificial intelligence (generative AI). As a specific example, we consider the S4^prime modular flavor model and construct a neural network that reproduces quark masses, the CKM matrix, and the Jarlskog invariant by treating free parameters in the flavor model as generating targets. By generating new parameters with the trained network and local optimization, we find various phenomenologically interesting parameter regions. Additionally, we confirm that the spontaneous CP violation occurs in the S4^prime model. The diffusion model enables an inverse problem approach, allowing the machine to provide a series of plausible model parameters from given experimental data.