Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.
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This paper introduces gauge-equivariant graph neural networks that embed local gauge symmetries directly into message passing for learning on lattice gauge theories. The approach enables principled machine learning for systems with site-dependent symmetries and nonlocal observables.