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New TOME-Math Corpus Enhances AI Mathematical Reasoning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TOME-Math, a new corpus that converts 2,118 university-level mathematics textbooks into approximately 380,000 structured proof-supervision records. This corpus preserves the original organizational and evidential structure of the textbooks, including chapter order, proposition-proof links, and dependency references. Fine-tuning a base model on TOME-Math significantly improved its mathematical reasoning capabilities, increasing scores by 16.7 percentage points on held-out evaluations and outperforming existing instruction-data baselines. AI

IMPACT Enhances AI's mathematical reasoning by leveraging structured textbook data, potentially improving performance in STEM-related AI applications.

RANK_REASON The item describes a new corpus and research findings related to AI's mathematical reasoning capabilities, published on arXiv. [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 →

New TOME-Math Corpus Enhances AI Mathematical Reasoning

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The item describes a new corpus and research findings related to AI's mathematical reasoning capabilities, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Youqing Fang, Zhouqi Hua, Yinhao Tang, Yuzhe Gu, Lingkai Kong, Bin Liu, Weiming Zhang, Kai Chen, Yanan Sun, Wenwei Zhang ·

    Scaling Mathematical Proof Supervision from Structured Textbooks

    arXiv:2610.40394v1 Announce Type: cross Abstract: Mathematical supervision for language models is typically flattened into isolated problem-solution pairs, discarding the curricular organization and evidential structure of the original sources. We introduce TOME-Math (Textbook-Or…

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