这篇论文用AI判断两个物理系统是否对偶，在约10节点quiver上比传统算法快，还配了“quiver版谷歌地图”提高准度。
该论文用机器学习判定超对称quiver规范理论中的Seiberg对偶，数学上等价于判断quiver的突变。在约10个节点的quiver上，Transformer和多层感知机架构的表现优于确定性算法。加入pathfinder算法（作者称之为“quiver版Google Maps”）后，搜索效率和准确率进一步提升。论文认为这类问题可作为前沿AI模型用于理论物理的基准测试。
Learning to Trace Seiberg Dualities
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.