How Twist Class Redundancy Drives the Prediction of Traces of Frobenius of Elliptic Curves

Fuente: arXiv
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Autores principales: Babei, Angelica, Shah, Ujjawal, Kebe, Malick
Formato: Preprint
Publicado: 2026
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author Babei, Angelica
Shah, Ujjawal
Kebe, Malick
author_facet Babei, Angelica
Shah, Ujjawal
Kebe, Malick
contents Recent interest in applying machine learning methods to predict invariants of mathematical objects has yielded models with surprisingly strong performance, including those predicting traces of Frobenius for elliptic curves. We demonstrate that the underlying datasets contain significant redundancy within quadratic twist classes, which alone is sufficient to produce highly accurate predictions. To ensure future models capture new arithmetic properties rather than potentially exploiting these dataset artifacts, we introduce a benchmark dataset consisting exclusively of unique twist class representatives.
format Preprint
id arxiv_https___arxiv_org_abs_2605_14288
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle How Twist Class Redundancy Drives the Prediction of Traces of Frobenius of Elliptic Curves
Babei, Angelica
Shah, Ujjawal
Kebe, Malick
Number Theory
Recent interest in applying machine learning methods to predict invariants of mathematical objects has yielded models with surprisingly strong performance, including those predicting traces of Frobenius for elliptic curves. We demonstrate that the underlying datasets contain significant redundancy within quadratic twist classes, which alone is sufficient to produce highly accurate predictions. To ensure future models capture new arithmetic properties rather than potentially exploiting these dataset artifacts, we introduce a benchmark dataset consisting exclusively of unique twist class representatives.
title How Twist Class Redundancy Drives the Prediction of Traces of Frobenius of Elliptic Curves
topic Number Theory
url https://arxiv.org/abs/2605.14288