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About this paper
Linden Disney-Hogg, Max Zuev
The authors train a pairwise classifier to predict whether generating vectors encode topologically equivalent finite-group actions on Riemann surfaces. The model compares tokenized descriptions using an adapted text-matching architecture. Its best test accuracy is 85.8% on 86,760 class-uniform pairs, but performance varies across pair types; this is supervised prediction, not an exact equivalence test or demonstrated replacement for orbit computation.
Summary by alphaXiv.
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