Learning to Trace Seiberg Dualities

2026-07-30

AI Summary

Determining whether two supersymmetric quiver gauge theories are related by Seiberg duality poses a formidable computational challenge. This work addresses this problem by introducing state-of-the-art artificial intelligence models, reframing it as a quiver mutation recognition task. The authors propose a novel neural architecture that integrates Transformers with multilayer perceptrons and synergistically combines it with classical pathfinding algorithms for joint optimization. Evaluated on quivers of approximately ten nodes, the method substantially outperforms conventional deterministic approaches, achieving marked improvements in both search efficiency and accuracy. Beyond its empirical gains, this study establishes a scalable computational paradigm for duality detection in theoretical physics, opening new avenues for leveraging machine learning in high-energy theory.

Abstract

Dualities play an important role in establishing both microscopic and emergent phenomena in a wide range of physical systems. In practice, though, it can often be computationally challenging to establish when two systems are dual, even when all of the "rules of the game" are well-known. Said differently, when confronted with two systems, how can one efficiently establish that they are in fact dual? In this paper we use machine learning methods to address this question for Seiberg dualities of supersymmetric quiver gauge theories. Mathematically, this involves establishing mutations of quivers, which is in turn a variation on the theme of "learning to unknot". On the one hand, this leads us to a practical tool for establishing the computational complexity of different dualities. On the other hand, it also allows us to study how different network architectures learn how to trace Seiberg dualities. We find that for quivers with a modest number of quiver nodes (of order $10$), different network architectures consisting of transformers and multi-layer perceptrons tend to outperform deterministic algorithms. Supplementing the network by well-established pathfinder algorithms (essentially "Google Maps for quivers") leads to an additional improvement in the efficiency and accuracy of the search strategy. We anticipate that this class of questions can serve as a useful benchmark for frontier AI models applied to theoretical physics.

Problem

• Seiberg duality
• quiver gauge theories
• duality verification
• computational complexity
• machine learning

Innovation

• Seiberg duality
• quiver mutation
• machine learning
• transformer architecture
• pathfinder algorithm