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#lean-formalization

@FinanceYF5: https://x.com/FinanceYF5/status/2108757519690559912

X AI KOLs Following ↗ · 2026-10-10 Cached

OpenAI 发布了一个新仓库,收录其未公开内部模型产生的 719 篇数学手稿(归为 372 个结果族),涵盖黎曼 zeta 函数零点区域、Mahler 猜想、自由群因子同构等前沿问题,其中约 42% 的结果已用 Lean 形式化验证,并附带部分推理过程摘要。

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#lean-formalization

‘Pure insanity’: Mathematicians will need years to make sense of OpenAI’s latest drop

The Verge ↗ · 2026-10-09 Cached

OpenAI 一次性发布了近 400 条 AI 生成的数学结果,涵盖 700 多篇手稿,令数学界既震惊又焦虑;但仅约 42% 的成果完成了 Lean 形式化验证,数学家们表示可能需要数年才能消化这批成果。

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#lean-formalization

OpenAI Withdraws 3 Math Papers

Hacker News Top ↗ · 2026-10-08 Cached

OpenAI 发布了一个包含由其未公开内部模型产出的 719 篇数学手稿(372 个结果族)的仓库,并附带部分 Lean 形式化证明与推理摘要,同时据标题消息撤回了其中 3 篇论文。

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#lean-formalization

@sama: We are entering a new era of discovery now:

X AI KOLs ↗ · 2026-10-07 Cached

OpenAI宣布进入科学发现新时代,发布其内部前沿模型产出的一系列重要数学成果(包括Navier-Stokes相关结果),并提供Lean形式化证明、计算资源披露,以及与IAS数学与人工智能咨询组合作制定的发布规范。

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#lean-formalization

@OpenAI: We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting…

X AI KOLs ↗ · 2026-10-06 Cached

OpenAI is releasing a broad range of new mathematical results produced by an internal frontier model, published via a GitHub repository with Lean formalizations, reasoning summaries, and compute estimates, developed in consultation with the IAS Advisory Group on Mathematics and AI.

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#lean-formalization

Recent OpenAI research has demonstrated the ability of LLMs to solve frontier problems in mathematics (1 minute read)

TLDR AI ↗ · 2026-07-01 Cached

OpenAI research shows LLMs can solve nine open math problems from COLT, FOCS, commutative algebra, and Erdős problems using a simple pipeline with GPT-5.5 Pro and Claude Opus 4.8, with Lean formalizations.

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#lean-formalization

From LLM-Generated Conjectures to Lean Formalizations: Automated Polynomial Inequality Proving via Sum-of-Squares Certificates

arXiv cs.AI ↗ · 2026-05-18 Cached

This paper presents NSPI, a neuro-symbolic framework that combines LLMs and symbolic computation to prove polynomial inequalities. It uses LLM-generated sum-of-squares conjectures, refines them symbolically, and formally verifies the proofs in Lean, demonstrating scalability on polynomials with up to 10 variables.

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