最新资讯
reddit.com OpenAI 称 Astra 仅满足 10% 安全任务标准 dev.to FreeToken:在自有 GPU 上运行前沿 MoE 模型及其内存需求 dev.to 当 AI Agent 说“完成”时,究竟完成了什么? dev.to 部署 Underdog Bark 3.0 Pro:逐字转写、自定义后端与分块设置 dev.to 为艰难对话构建 AI:五个 Agent、一条安全正则与一位盲评监督者 techcrunch.com Palo Alto Networks 洽谈以 5 亿美元收购 AI Agent 安全初创公司 Clutch theinformation.com 黄仁勋被要求就涉华间谍指控向国会作简报 www.infoq.com Edera 推出 Object Capability Bounded Authority,试图将凭证与 AI Agent 工作负载隔离 wechat 这波杀疯了!旗舰级模型不限量、1个月视频额度免费领 bloomberg.com AI 抛售蔓延至信用市场,软银领跌亚洲美元债 dev.to 如何在专用 GPU 服务器上用 OpenAI 兼容 API 部署 LLM dev.to 审计 153 份 Claude Code 与 Codex 会话记录:User-Agent 与 URL 中的邮箱泄露
多元动态
登录 注册
智能体 www.aibase.com ·

AI数学里程碑:Claude 11天完成费马大定理端到端形式化证明

原文 我的收藏

Anthropic宣布其AI模型Claude在11天内自主完成了费马大定理的端到端Lean形式化证明,生成了约1300万行代码,证明了约3万个定理,最终证明由Lean验证。该项目由Anthropic研究员Tianyi Peng发起,采用多智能体协作,基于Prove2Me平台,消耗约60亿输出token。该工作是将现有数学证明转化为计算机可验证的形式,并非AI重新发现证明。

AI推荐理由 Anthropic官方宣布其模型Claude在11天内自主完成费马大定理的端到端Lean形式化证明,生成约1300万行代码,证明约3万个定理,消耗60亿输出token。该成果具有明确的事实依据和官方来源,信息密度高,时效性强,对AI推理和数学形式化领域有重要价值。但需注意,该工作是将现有证明形式化,并非AI独立发现新证明,且模型为内部研究模型,非公开产品。

85

正文

AI世界的数学里程碑:Claude在11天内完成费马大定理的端到端形式化 AIbase基地 发表于AI新闻 · 6分钟阅读 · 2026年9月7日 33 Anthropic最近宣布,其AI模型在自主运行11天后,成功完成了费马大定理(FLT)的首个端到端、计算机检查的Lean形式化证明。这项工作并非让AI重新发现该定理的数学证明,而是将现有的数学证明转换为可以由Lean证明助手逐步验证的形式。Lean是一种用于编写和验证形式化数学证明的证明助手,能够通过计算机检查证明中的逻辑步骤。 在此过程中,Claude生成了约1300万行Lean代码,并证明了约30,300个定理,其中约29,500个中间定理最终被纳入费马大定理的完整证明中。整个证明由Lean检查,仅使用了Lean的三个标准公理。这项工作的目标是形式化英国数学家安德鲁·怀尔斯于1995年完成的费马大定理的证明。费马大定理指出,当整数指数$n$大于2时,不存在正整数$a$、$b$和$c$满足$a^n + b^n = c^n$。怀尔斯的原始证明长达129页,在发表前经过了数月的人工验证。 数学形式化的难点在于,人类的数学证明常常省略许多被认为显而易见的推导步骤,而Lean要求每一步逻辑都明确验证。此外,数学家们长期引用大量尚未形式化的数学结果,因此将完整证明转换为计算机可验证的形式通常需要大量时间。Anthropic此前估计,形式化费马大定理可能需要数年时间。 这个项目由Anthropic研究员田一鹏发起。Claude并非由单个代理完成所有工作,而是通过多代理协作来处理概念定义、证明中间定理以及推导更复杂的命题。该项目使用了田一鹏及其哥伦比亚大学同事开发的Prove2Me平台,该平台使用有向无环图记录待证明的定理及其依赖关系,并支持多个Claude代理并行工作。最终证明遵循了Darmon、Diamond和Taylor对怀尔斯证明的简化版本。人类研究人员主要提供少量高级指令,例如确定某些数学对象或定理的优先级,而具体的证明过程主要由Claude完成。 整个项目消耗了约60亿个输出令牌,使用了一个大致相当于Claude Fable5.1的通用内部研究模型。最终生成的证明规模是主要数学证明库Mathlib的五倍以上。Anthropic特别强调,这一成就的重点不在于AI独立提出了费马大定理的新证明,因为该定理已于1995年被怀尔斯证明。这项工作的创新之处在于利用AI大规模自动形式化证明,然后由Lean对最终结果进行计算机验证。 Anthropic认为,如果类似的自动化形式化技术能够扩展到更多现代数学成就,可能会显著降低数学研究中验证新证明的人工成本。随着AI生成的数学结果数量增加,提供既有人类可读又有形式化版本的数学证明可能成为常见做法。该项目的完整Lean证明已在GitHub上公开。同时,数学家Kevin Buzzard审查了该证明,认为这一自动化形式化结果显示了AI辅助形式化大型数学成就的重要进展。 新AI术语Lean费马大定理Claude 本文来自AIbase日报 扫码查看 欢迎来到[AI日报]专栏!这是您每日探索人工智能世界的指南。每天,我们为您呈现AI领域的热点话题,聚焦开发者,帮助您了解技术趋势,并了解创新的AI产品应用。 ——由AIbase日报团队创建 © 版权所有 AIbase基地 2024,点击查看来源 -
查看原文(中文正文为 AI 翻译)
Mathematical Milestone in the AI World: Claude Achieves End-to-End Formalization of Fermat's Last Theorem in Just 11 Days AIbase基地 Published inAI News · 6 min read · Sep 7, 2026 33 Anthropic recently announced that its AI model successfully completed the first end-to-end, computer-checked Lean formal proof of Fermat's Last Theorem (FLT) after running autonomously for 11 days. This work did not involve the AI rediscovering a mathematical proof of the theorem, but rather converting existing mathematical proofs into forms that can be step-by-step verified by the Lean proof assistant. Lean is a proof assistant used to write and verify formal mathematical proofs, capable of checking the logical steps in a proof through a computer. In this process, Claude generated approximately 13 million lines of Lean code and proved about 30,300 theorems, with about 29,500 intermediate theorems eventually incorporated into the complete proof of Fermat's Last Theorem. The entire proof was checked by Lean, using only three standard axioms from Lean. The goal of this work was to formalize the proof of Fermat's Last Theorem completed by British mathematician Andrew Wiles in 1995. Fermat's Last Theorem states that when the integer exponent $n$ is greater than 2, there are no positive integers $a$, $b$, and $c$ that satisfy $a^n + b^n = c^n$. Wiles' original proof was 129 pages long and underwent several months of manual verification before publication. The difficulty of mathematical formalization lies in the fact that human mathematical proofs often omit many derivation steps considered obvious, while Lean requires explicit verification of every logical step. Additionally, mathematicians have long cited a large number of mathematical results that have not yet been formalized, so converting a complete proof into a computer-verifiable form usually requires significant time. Anthropic had previously estimated that formalizing Fermat's Last Theorem might take several years. This project was initiated by Anthropic researcher Tianyi Peng. Claude did not complete all the work by a single agent, but rather through multi-agent collaboration to handle concept definitions, prove intermediate theorems, and derive more complex propositions. The project used the Prove2Me platform developed by Peng and his colleagues at Columbia University, which records theorems to be proven and their dependencies using a directed acyclic graph and supports multiple Claude agents working in parallel. The final proof followed a simplified version of Wiles' proof by Darmon, Diamond, and Taylor. Human researchers mainly provided a small number of high-level instructions, such as determining the priority of certain mathematical objects or theorems, while the specific proof process was primarily completed by Claude. The entire project consumed approximately 6 billion output tokens, using a general internal research model roughly equivalent to Claude Fable5.1. The final generated proof is more than five times the size of Mathlib, the main mathematical proof library. Anthropic specifically emphasized that the focus of this achievement is not that AI independently proposed a new proof of Fermat's Last Theorem, as the theorem was already proven by Wiles in 1995. The innovation of this work lies in using AI to automatically formalize proofs on a large scale, and then having Lean perform computer verification of the final result. Anthropic believes that if similar automated formalization technology can be extended to more modern mathematical achievements, it could significantly reduce the manual cost of verifying new proofs in mathematical research. As the amount of mathematical results generated by AI increases, it may become a common practice to provide both human-readable and formalized versions of mathematical proofs. The complete Lean proof from this project has been made publicly available on GitHub. At the same time, mathematician Kevin Buzzard reviewed the proof and believes that this automated formalization result shows important progress in AI-assisted formalization of large mathematical achievements. New AI TermsLeanFermat's Last TheoremClaude This article is from AIbase Daily Scan to view Welcome to the [AI Daily] column! This is your daily guide to exploring the world of artificial intelligence. Every day, we present you with hot topics in the AI field, focusing on developers, helping you understand technical trends, and learning about innovative AI product applications. —— Created by the AIbase Daily Team © Copyright AIbase Base 2024, Click to View Source -

来源报道 · 1 篇 · 以下链接均前往原始媒体

← 返回精选动态 原文

更多内容