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Transformer learns analytic number theory heuristic from elliptic curve data

By PulseAugur Editorial · [1 sources] ·

Researchers have trained a two-layer transformer encoder to classify rational elliptic curves based on their rank, achieving over 99% accuracy using the first 128 normalized Frobenius traces. Through mechanistic interpretability techniques, they identified a sparse circuit of 20 MLP neurons sufficient for prediction, implementing a push-pull detector architecture. Notably, the model's learned input weights closely matched the Mestre-Nagao sum heuristic, indicating it learned a result from analytic number theory directly from the data. AI

IMPACT Demonstrates transformers' capability to learn complex mathematical heuristics, potentially opening new avenues for AI in theoretical sciences.

RANK_REASON The cluster contains an academic paper detailing novel research findings in machine learning applied to number theory. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Transformer learns analytic number theory heuristic from elliptic curve data

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The cluster contains an academic paper detailing novel research findings in machine learning applied to number theory. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pranav Venkata Konda ·

    Transformers Learn the Mestre-Nagao Heuristic

    arXiv:2606.15036v1 Announce Type: new Abstract: We train a two-layer transformer encoder to classify rational elliptic curves $E/\mathbb{Q}$ of conductor $\leq 10000$ as either rank 0 or rank 1 from the first 128 normalized Frobenius traces. We achieve >99% accuracy on both class…

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