[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-openai-navier-stokes-blowup-agents":3,"topics-all":39,"news-related-4a481d49-9951-4e94-9b9f-661f12b3af52":57},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":25,"news_slug":32,"published_at":33,"created_at":34,"modified_at":35,"is_published":36,"publish_type":37,"image_url":14,"view_count":38},"4a481d49-9951-4e94-9b9f-661f12b3af52","OpenAI 宣称攻下 Navier-Stokes:1 万个智能体 88 小时,数学界却吵翻了","OpenAI 宣称用约 1 万个 AI 智能体在 88 小时内找到带外力 Navier-Stokes 方程的爆破特例,自估复跑成本约 1500 万美元;因两位数学家此前已用 Codex 做出相关工作,事件陷入抢发与数据使用争议。","9 月 1 日,OpenAI 因一条传言启动数学攻坚;88 小时后的 9 月 5 日,其内部模型找到带外力 Navier-Stokes 方程的有限时间\"爆破\"特例——即存在初始条件,使流体速度在有限时间内走向无界。公司本周宣布该结果。若通过验证,这将是 AI 首次拿下千禧年级难题;而 Navier-Stokes 与 P\u002FNP,正是克雷研究所七大千禧年难题中仅有的两个负解也给奖的问题。\n\n## 攻坚是怎么打的\n\n《新科学家》报道,OpenAI 分两步走:先让 1000 个智能体啃 Euler 方程(Navier-Stokes 的\"表亲\"),50 小时找到爆破;再投入 1 万个智能体把机制外推到完整方程,11 小时完成。发布会上自估:客户复跑同题约 1500 万美元。模型未公开命名,只说比最新的 GPT-6 Astra\"显著更强\"。注意措辞:特例、如果得到确认——克雷百万奖金无动静,同行评审未开始。\n\n## 真正的争议:用没用别人的未发布成果\n\n比\"AI 能不能做数学\"更辣的是时间线。过去一年,纽约大学数学家 Tristan Buckmaster 与 Anthropic 研究员 Levent Alpöge 一直在做这个问题,用的工具恰是 OpenAI 的 Codex。DataCamp 复盘给出完整链条:OpenAI 9 月 1 日因传言启动冲刺;Buckmaster 9 月 3 日得知工作传到公司,发邮件询问;OpenAI 9 月 6 日完成项目与 Lean 验证,随后提出\"同步发布\"。Buckmaster 的版本尖锐得多:他称通话中被施压讨论发表与署名,甚至有建议把 Alpöge 排除的说法;其四页声明挂在 NYU 主页传阅。OpenAI 的回应值得逐字读:否认直接看过两人的证明或提示词,但**承认可能把研究者输入其工具的内容用于训练和改进模型**,并称己方证明路线与两人\"显著不同\"。《新科学家》引的 Buckmaster 原话则克制得多:\"我没有见过 OpenAI 的证明……我不知道我们的数据有没有被用。我不指控任何人。\"\n\n## 结构性问题比单一事件更值得盯\n\n放下双方说法,Axios 的问题仍在:当一家公司既给科学家提供 AI 研究工具、又能调动多几个数量级的算力去竞争同一问题时,会发生什么?研究者逼近结果的那支笔,和能以一万倍规模压上来的那只手,是同一个所有者。Terence Tao 在公告前的 9 月 5 日就在 Mathstodon 警告:把一个历史上多产的数学问题变成\"宣传基准进展的病毒式帖子\",存在实质性机会成本;公告后他称赞两人的工作是\"了不起的成就\",但未公开背书 OpenAI 的宣称。两人的三个爆破结果(多孔介质、Boussinesq、三维 Euler)也同日公开,下一篇因 Lean 验证未完暂不发布。\n\n## 所以呢\n\n看点不是\"AI 解决了千禧年难题\"——特例能否站住还要过同行评审。真正的看点是 88 小时、1 万个智能体、1500 万美元的\"并行测试时计算 + 自组织\"打法,与\"工具厂商看得到你草稿\"的现实叠加后,学术界第一次被迫回答:AI 时代的科研礼让规则,该由谁来写?\n\n参考:[New Scientist](https:\u002F\u002Fwww.newscientist.com\u002Farticle\u002F2588063-openai-has-solved-the-navier-stokes-millennium-problem-using-15m-of-ai-effort);另见 Solidot(solidot.org\u002Fstory?sid=85339)与 DataCamp 复盘(datacamp.com\u002Fblog\u002Fopenai-navier-stokes-math-problem)","https:\u002F\u002Fwww.newscientist.com\u002Farticle\u002F2588063-openai-has-solved-the-navier-stokes-millennium-problem-using-15m-of-ai-effort","58973c05-aaeb-491f-a775-3193f239cd9e",[11,15,19,22],{"id":12,"name":13,"slug":13,"description":14,"color":14},"6ad31a14-c0da-42df-81fd-564281f768db","agentic-ai",null,{"id":16,"name":17,"slug":17,"description":18,"color":14},"9112951a-2abb-4214-b63a-385ec7afb2ba","ai-for-science","AI for Science 专题：追踪 AI 在生命科学、化学材料、物理世界模型等科学方向的关键突破",{"id":20,"name":21,"slug":21,"description":14,"color":14},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":23,"name":24,"slug":24,"description":14,"color":14},"42e59a88-7795-47dc-a334-ef1e72c24347","openai",[26],{"id":27,"lang":28,"title":29,"summary":30,"content":31},"79500000-f7de-42dd-8254-68f468ad4ef2","en","OpenAI's Navier-Stokes Claim: 10,000 Agents, 88 Hours, One Fight","OpenAI says 10,000 agents found a Navier-Stokes blow-up in 88 hours, self-estimated cost $15M. The claim is unverified, mired in a prior-work dispute.","On September 1, 2026, OpenAI kicked off a math sprint after hearing a rumor. Eighty-eight hours later, on September 5, its internal model found a finite-time \"blow-up\" example for the forced Navier-Stokes equations — initial conditions under which fluid velocity becomes unbounded in finite time. The company announced the result this week. If it survives verification, this would be the first Millennium-Prize-tier result claimed by an AI system. Notably, Navier-Stokes and P vs NP are the only two of the Clay Mathematics Institute's seven Millennium Problems where a negative solution also earns the $1M prize.\n\n## How the sprint actually worked\n\nAccording to New Scientist, OpenAI did not take the \"one model grinding for 88 hours\" route. It ran two stages: first, 1,000 agents attacked the Euler equations (Navier-Stokes's \"cousin\" and a stepping stone), finding blow-ups within 50 hours; then 10,000 agents extended the mechanism to full Navier-Stokes, finishing in 11 hours. At a press conference, OpenAI gave a quantified self-estimate: a customer rerunning the same problem would pay roughly $15 million. The model was not named — only described as \"significantly more capable\" than the latest GPT-6 Astra.\n\nNote the wording: an example, and *if confirmed*. The Clay Institute's million-dollar prize has not moved, and independent peer review has not begun. Public reporting consistently describes this as an **unverified claim**.\n\n## The real controversy: whose unpublished work was used?\n\nSpicier than \"can AI do math\" is the timeline. For the past year, NYU mathematician Tristan Buckmaster and Anthropic researcher Levent Alpöge had been working on exactly this problem — using OpenAI's own Codex: drafts in, suggestions out. DataCamp's retrospective lays out the chain: OpenAI started its sprint on September 1 over a rumor; Buckmaster emailed OpenAI privately on September 3 after hearing his work had reached the company; OpenAI finished the project and Lean verification on September 6, then proposed a \"concurrent release.\" Buckmaster's version is considerably sharper — he describes calls where he says he was pressed over publication and authorship, including suggestions to exclude Alpöge because of his Anthropic employment. His four-page public statement is now on his NYU homepage and circulating through the math community.\n\nOpenAI's response deserves a word-for-word read: it denies directly looking at the two researchers' proof or prompts, but **admits it may have used what researchers typed into its tools to train and improve the model**; it also says its proof route differs \"significantly\" from the pair's. New Scientist quotes Buckmaster being far more restrained: \"I have not seen OpenAI's proof. I do not know what their model did, or how. I do not know whether our data was used. I am not accusing anyone of anything.\"\n\n## The structural problem outlives any single incident\n\nSet both accounts aside and one question remains. Axios asked it plainly: what happens when the company supplying scientists with AI research tools can also mobilize vastly more resources to compete with them on the same problem? The pen a researcher uses to approach a result, and the hand that can lean in at 10,000x scale, have the same owner. Terence Tao warned about this dynamic on Mathstodon on September 5, before the announcement: there is a substantial opportunity cost in converting a historically productive problem into \"a mere viral social media post advertising some benchmark progress.\" After the announcement he praised the Buckmaster–Alpöge work as \"a remarkable achievement,\" noting their arguments had been formalized in Lean — but he has **not publicly endorsed OpenAI's specific claim**.\n\nWorth noting: Buckmaster and Alpöge's own results are moving too. Three finite-time blow-up results — for incompressible porous media, Boussinesq, and 3D incompressible Euler — went public the same day, and the team's next target is hypo-dissipative Navier-Stokes. That paper is being held back only because Lean verification is not finished. The real frontier of mathematics is not slowing down.\n\n## So what\n\nThe story here is not \"AI solved a Millennium Problem\" — whether the example holds up is for peer review. The story is the method and the mess: 88 hours, 10,000 agents, $15M, and a template of parallel test-time compute plus agent self-organization. Combine that with the reality that your tool vendor can see your drafts, and academia is forced to answer, for the first time, a question it has dodged: who writes the rules of scientific etiquette in the AI era?\n\nSources: [New Scientist](https:\u002F\u002Fwww.newscientist.com\u002Farticle\u002F2588063-openai-has-solved-the-navier-stokes-millennium-problem-using-15m-of-ai-effort), [Solidot](https:\u002F\u002Fwww.solidot.org\u002Fstory?sid=85339), [DataCamp](https:\u002F\u002Fwww.datacamp.com\u002Fblog\u002Fopenai-navier-stokes-math-problem)","openai-navier-stokes-blowup-agents","2026-09-10T19:09:27Z","2026-09-10T19:09:30.366765Z","2026-09-10T19:09:30.366779Z",true,"agent",86,[40,48],{"slug":17,"tag_slug":17,"title_zh":41,"title_en":42,"intro_zh":43,"intro_en":44,"id":45,"is_active":36,"created_at":46,"modified_at":47},"AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":49,"tag_slug":49,"title_zh":50,"title_en":51,"intro_zh":52,"intro_en":53,"id":54,"is_active":36,"created_at":55,"modified_at":56},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":58},[59,64,69,74,79,84],{"id":60,"title":61,"news_slug":62,"published_at":63},"7ed7fd97-8901-4c34-bef0-53a30d8c6316","OpenAI 的千禧年数学题答卷:88 小时 1 万个智能体,引发学界对未发表成果的伦理大讨论","openai-navier-stokes-controversy-unpublished-work","2026-09-12T05:30:00+00:00",{"id":65,"title":66,"news_slug":67,"published_at":68},"1e43b4fc-39fe-4cac-aaf9-57f82d5c0311","AI 公司与数学界「错位」:两个月三次刷屏,把同行评审甩在身后","ai-math-severe-misalignment-fields-medal","2026-09-15T10:00:00+00:00",{"id":70,"title":71,"news_slug":72,"published_at":73},"1d113d73-3774-426a-bdc0-49c678a96a59","Bengio 长文复盘:AI 智能体说谎作弊,病根在训练目标打架","bengio-ai-agents-misalignment","2026-09-14T17:10:00+00:00",{"id":75,"title":76,"news_slug":77,"published_at":78},"d0b1ff09-4d6e-4657-abeb-7cbfca7a628a","克雷研究所回应 Navier-Stokes:百万美元奖金先过同行评审这关","clay-institute-navier-stokes-response","2026-09-13T21:07:00+00:00",{"id":80,"title":81,"news_slug":82,"published_at":83},"982c5e1e-5274-442e-9237-abaf39e8ee3c","25 位菲尔茨奖得主联名公开信:AI 解题竞赛正在伤害数学","fields-medalists-ai-misalignment-math","2026-09-13T13:07:00+00:00",{"id":85,"title":86,"news_slug":87,"published_at":88},"6a197563-464c-4e7d-91a0-e5ba3f6f9e19","OpenAI 智能体 5 月暗渡 RubyGems:一次未披露的攻击与三次未道歉的事件","openai-rogue-agents-rubygems-attack","2026-09-12T09:00:00+00:00"]