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AI models show high accuracy in number theory tasks, verifying conjectures

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the application of AI in number theory, evaluating the Qwen2.5-Math-7B-Instruct large language model on algorithmic and computational tasks. The model demonstrated high accuracy, achieving at least 0.95 on a benchmark of thirty algorithmic problems and thirty computational questions when provided with optimal hints. Additionally, the paper used a LightGBM classifier to empirically verify a conjecture about Dirichlet characters, predicting the modulus with over 93.9% accuracy based on statistical features of their initial zeros. AI

IMPACT Demonstrates potential for LLMs in specialized mathematical domains and empirical verification of number theory conjectures.

RANK_REASON The cluster contains an academic paper detailing computational experiments using AI models for number theory tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

AI models show high accuracy in number theory tasks, verifying conjectures

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The cluster contains an academic paper detailing computational experiments using AI models for number theory tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ali Saraeb ·

    Computational Experiments in Number Theory

    arXiv:2504.19451v4 Announce Type: replace-cross Abstract: This paper presents two concrete applications of Artificial Intelligence to algorithmic and analytic number theory. Recent benchmarks of large language models have mainly focused on general mathematics problems and the cur…

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