# Discovering the Underlying Analytic Structure Within Standard Model Constants Using Artificial Intelligence

arXiv: https://arxiv.org/abs/2507.00225 · HTML: https://mlsanity.com/paper/2507.00225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Abstract (arXiv)

This paper presents a method for uncovering hidden analytic relationships among the fundamental parameters of the Standard Model (SM), a foundational theory in physics that describes the fundamental particles and their interactions, using symbolic regression and genetic programming. Using this approach, we identify the simplest analytic relationships connecting pairs of these constants and report several notable expressions obtained with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidden patterns among the SM constants, or potentially used as building blocks for a deeper underlying law that connects all parameters of the SM through a small set of fundamental constants.