Pith. sign in

REVIEW 2 cited by

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.10357 v1 pith:6HXGPTDN submitted 2025-02-14 math.NT cs.LG

Learning Euler Factors of Elliptic Curves

Angelica Babei , Fran\c{c}ois Charton , Edgar Costa , Xiaoyu Huang , Kyu-Hwan Lee , David Lowry-Duda , Ashvni Narayanan , Alexey Pozdnyakov This is my paper
classification math.NT cs.LG
keywords bmodmodelscurvesellipticpredicttracesabsenceaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We apply transformer models and feedforward neural networks to predict Frobenius traces $a_p$ from elliptic curves given other traces $a_q$. We train further models to predict $a_p \bmod 2$ from $a_q \bmod 2$, and cross-analysis such as $a_p \bmod 2$ from $a_q$. Our experiments reveal that these models achieve high accuracy, even in the absence of explicit number-theoretic tools like functional equations of $L$-functions. We also present partial interpretability findings.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Computer vision and converse theorems

    math.NT 2026-04 unverdicted novelty 6.0

    2D CNNs on image-encoded elliptic curve twists outperform 1D CNNs on Frobenius traces alone at separating conductor families from Sato-Tate random matrices and can predict analytic rank.

  2. Large Lemma Miners: Can LLMs do Induction Proofs for Hardware?

    cs.LO 2025-11 conditional novelty 6.0

    A neurosymbolic method using two LLM prompting frameworks generates provably correct inductive arguments for 84% of a set of mid-size open-source RTL hardware designs.