Mathematical physics sits at the fascinating intersection where abstract equations meet the fundamental laws of our universe. This field uses rigorous mathematical tools to model everything from the behavior of subatomic particles to the curvature of spacetime, turning complex theories into testable predictions. It is the language through which physicists describe reality, bridging the gap between pure mathematics and physical observation.

On Gist.Science, we process every new preprint published in this category on arXiv to make these dense studies accessible to everyone. Whether you are a specialist or a curious reader, you will find both plain-language overviews and detailed technical summaries for each paper. Below are the latest mathematical physics papers from arXiv, curated to help you explore the cutting edge of theoretical science.

🔢 mathematics

Finite group actions on genus two SL(2,C)SL(2, \mathbb{C})-character variety and applications to SCFTs

This paper investigates the irreducible components of fixed point sets within the SL(2,C)SL(2, \mathbb{C})-character variety of a genus two surface under finite group actions, utilizing the genus two DAHA to identify geometric transitions that yield novel candidates for symmetry-reduced moduli spaces in 4d N=2\mathcal{N}=2 SCFTs.

Semeon Arthamonov, Anton Pribytok2026-03-10
🔢 mathematics

WKB-asymptotics for multipoint Virasoro conformal blocks and applications

This paper derives WKB-asymptotic expressions for multipoint Virasoro conformal blocks in the comb channel on the sphere by applying the WKB method to the classical BPZ equation, validating the results against known exact solutions and AGT correspondence while demonstrating their utility for generalizing Zamolodchikov's elliptic recursion and numerically evaluating minimal string theory amplitudes.

Aleksandr Artemev, Dmitry Khromov2026-03-10
🤖 machine learning

Group Entropies and Mirror Duality: A Class of Flexible Mirror Descent Updates for Machine Learning

This paper introduces a comprehensive framework that unifies formal group theory and group entropies to create a flexible, infinite family of Mirror Descent optimization algorithms, featuring a novel "mirror duality" mechanism that adapts to diverse data geometries and statistical distributions while enhancing convergence and regularizer design in machine learning.

Andrzej Cichocki, Piergiulio Tempesta2026-03-10