2026-10-11 17:15 UTC

Terence Tao says the methods discussed in Buckmaster and Alpöge’s reported AI-assisted mathematics program could extend to Navier–Stokes despite enormous technical difficulties, potentially opening a route toward resolving the regularity problem.

state: corroboratedheat: mediumuncertainty: highconvergesscott: highai-assisted-math research-agentsTristan BuckmasterLevent AlpögeTerence Tao
Surfaced 2026-09-08T17:43:19Z — priced heat=high at reprice: The episode has shifted from a conditional mathematical research direction to a publicly attributed OpenAI solution claim, with newly quoted details describing an internal next-generation model and a large agent-based research campaign. Those details make this relevant to research-agent capability and economics, but neither proof correctness, autonomous discovery, nor the disputed provenance is established.

What is this?

On 7–8 September 2026, NYU mathematician Tristan Buckmaster and Anthropic-employed Levent Alpöge announced AI-assisted (LLM-driven, Lean-formalized) finite-time blowup proofs for smoothly forced 3D Euler and related equations, building on work by Córdoba and Martínez-Zoroa; Terence Tao amplified the work on his blog and Mathstodon, calling it a remarkable push of a promising approach and saying extension toward the full Navier–Stokes regularity problem is plausible in principle but enormously difficult. Roughly twelve hours later OpenAI claimed its own internally-developed model, coordinating roughly 10,000 agents, produced a Lean-formalized blowup proof for the forced Navier–Stokes variant, prompting a priority and provenance dispute — Buckmaster alleges his progress leaked to OpenAI, which denies influence. The Clay Institute has acknowledged the announcement without validating it, noting its own two-year qualifying-review process. As of early October 2026 an ArXiv critique ('Navier–Stokes Lost in Translation') reportedly argues the Lean formalization does not correspond to the advertised natural-language proof, but per the supplied material its full scope and any OpenAI response remain unestablished; the mathematical claims are under active audit, with snippets corroborating the public episode but not the proofs themselves.

Why it matters to Scott

The case converges on three load-bearing Scott positions at once: (1) Mechanically Different Verifiers — the ArXiv 'Lost in Translation' critique is the first citable field instance of a Lean formalization diverging from its advertised natural-language proof, making the verifier-scope failure mode concrete; (2) The Mature Token Law — OpenAI's own campaign accounting (130B output tokens, 88h search + 17h formalization, ~10k agents) provides a dated, high-visibility receipt for 'tokens are fuel, not the score'; (3) Long-Running Agents / Scatter-Gather Cognition — the guided 10k-agent swarm with grouped teams, cached tools, and cross-pollination is a lab-scale instance of stateless workers + stateful external kernel orchestration. The audit phase sharpens all three positions and opens a publishing window for dated receipts, but does not yet change what Scott builds or argues.
ip:concept.mechanically-different-verifiersip:framework.the-mature-token-lawip:framework.long-running-agentsip:concept.human-ai-collaborationip:concept.formalisation-bottleneckip:framework.cognition-scarcity-auditip:concept.scatter-gather-cognitionip:concept.agent-receiptsip:concept.claim-bounded-adversarial-verificationip:framework.falsifiability-spinedev:concept.progressive-resolution-orchestrationdev:concept.hierarchical-task-decompositiondev:concept.bounded-cognitive-worker-ladderradar:hopf-proof-codex-lean-formalizationradar:claude-rank-30-elliptic-curveradar:anthropic-fermat-lean-formalizationradar:proofatlas-collatz-formalizationradar:dots-swarm-covering-recordradar:openai-research-accelerationradar:openai-rosalind-workbenchradar:arxiv-submission-rate-limitradar:frontiermath-tier3-saturationradar:epoch-ai-innovation-benchmark
queries asked of Scott's wikis
  • mechanically different verifiers Lean proof of claimed statement vs advertised claim
  • tokens are fuel not the score dated token receipts agent campaigns
  • long-running research agents swarm orchestration guided scatter-gather
  • human-AI collaboration division of labour in mathematical research
  • AI autoformalization translation fidelity natural language to formal proof
  • AI strip-mining open problems non-renewable problem selection research incentives

Measured heat

now 0 pts/hpeak 152 pts/hcomments 0/hpeers p50momentum: steady4 platformsage 802h
points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

09-09 17:33⭐ origin directly observedLean certificates accompanying Navier-Stokes and Euler results
thunderbong on hacker news
—
09-08 05:37first on r/singularity · published · +-35.9hBreakthrough for Navier-Stokes from Tristan Buckmaster + Levent Alpoge?
FateOfMuffins
—
09-08 05:42first on hacker news · published · +-35.9hNavier-Stokes – Tristan Buckmaster [pdf]
procedurecall
—
09-08 06:23first on paper (echo) · first seen by us · +-35.2hA statement on Navier–Stokes; the supplied evidence identifies the document but does not reproduce its contents.
Tristan Buckmaster
—
09-08 10:00first on openai · published · +-31.6hOn the Navier–Stokes Millennium Prize Problem
OpenAI
—
09-08 17:27first on r/OpenAI · published · +-24.1hOpenAI captures another Millennium Prize Problem
Happypig375
—
09-08 17:42first on r/MachineLearning · published · +-23.9hOpenAl Says It Has Cracked One of Math's “Millennium Problems” (Navier-Stokes) [N]
Shizuka_Kuze
—
09-08 18:05first on r/artificial · published · +-23.5hNavier–Stokes Millennium Prize Problem Solved
Collegesniffer
—
09-08 05:37amplified on r/singularityreddit.post.1wafkt4
FateOfMuffins
peak 404 · 264 comments · 2% of case engagement
09-08 05:42amplified on hacker news 👑hn.story.49605915
procedurecall
peak 1949 · 799 comments · 15% of case engagement
09-08 06:47amplified on r/singularityreddit.post.1wagu5g
Outside-Iron-8242
peak 150 · 104 comments · 1% of case engagement
09-08 15:02amplified on hacker newshn.story.49611362
bsilvereagle
peak 17 · 4 comments · 0% of case engagement
09-08 16:43amplified on hacker newshn.story.49612811
colinhb
peak 2 · 0 comments · 0% of case engagement
09-08 16:57amplified on hacker newshn.story.49613033
doubledamio
peak 32 · 9 comments · 0% of case engagement
56 more amplifiers in ainews.case_chain
09-08 06:20our radar first saw it · +-35.2hdiscovery anchor: reddit.post.1wafkt4—
09-08 17:46reached heat=high · +-23.8h · via ledger——
pace: p100 vs 519 stories at the 720h mark (now 802h old) — ahead of openai-gpt-astra-release (1.0x), behind prime-agent-acp-mcp-tools (0.6x)

Evidence (66) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟠 redditBreakthrough for Navier-Stokes from Tristan Buckmaster + Levent Alpoge?
singularity
FateOfMuffins404264
🟧 hnNavier-Stokes – Tristan Buckmaster [pdf]procedurecall1949799
🟧 echo.paperA statement on Navier–Stokes; the supplied evidence identifies the document but does not reproduce its contents.Tristan Buckmaster——
🟠 redditBubeck denies Buckmaster’s allegations in Navier–Stokes dispute
singularity
Outside-Iron-8242150104
🟧 hnHas OpenAI model solved 80-year-old Navier-Stokes problem?bsilvereagle174
🟠 redditA Solution to the Navier-Stokes Millennium Prize Problem
singularity
ResultBackground24501991812
🟠 redditOpenAI Says It Has Cracked One of Math’s ‘Millennium Problems’
singularity
TheOneTrueEris32293
🟧 hnOn the Navier–Stokes Millennium Prize Problemtedsanders13381127
🟧 hnOpenAI Says It Has Cracked One of Math's 'Millennium Problems'doener101
🟧 hnControversy over OpenAI's Maths Breakthroughdoubledamio329
🟧 hnOpenAI's historic math solution overshadowed by credit controversycolinhb20
🟠 redditOpenAI has a solution to the Navier–Stokes existence and smoothness Millennium Prize problem
OpenAI
owenzzzhang21457
🟠 redditNavier–Stokes Millennium Prize Problem Solved
artificial
Collegesniffer20
🟠 redditOpenAI claims blockbuster math breakthrough amid swirl of controversy
OpenAI
scientificamerican30
🟠 redditOpenAl Says It Has Cracked One of Math's “Millennium Problems” (Navier-Stokes) [N]
MachineLearning
Shizuka_Kuze704281
🟠 redditOpenAI claims a model more capable than GPT 6 Astra solved Navier Stokes
OpenAI
Playful-Second-39344411
🟠 redditMillenium Prize solution discovered at OpenAI
OpenAI
Kind_Fisherman3060707294
🟠 redditOpenAI captures another Millennium Prize Problem
OpenAI
Happypig37518
🟠 redditAI may have just solved a million-dollar math problem. The field will never be the same
artificial
Gari_30508
🟠 redditSebastien Bubeck's post refuting the claims
singularity
Wonderful_Buffalo_3211244
🟠 redditMeanwhile in r/physics
singularity
heyhellousername318113
🟧 hnOpenAI Just Claimed a Math Discovery. Some Academics Are Crying Foulolalonde72
🟠 redditOpenAI stole the Navier Stokes solution
OpenAI
PsychicorAI184244
🟠 redditOpenAI Just Claimed a Huge Math Discovery. Some Academics Are Crying Foul
OpenAI
wiredmagazine302134
🟠 redditOpenAI's next-generation model produces a to the Navier-Stokes Millennium Prize Problem
OpenAI
rasheed10600
🟠 redditA timeline of recent events leading to the solution of Navier-Stokes
singularity
S2S2S2S2S217842
🟧 hnWe're Sharing a Solution to the Navier-Stokes Millennium Prize Problemsajid31
🟧 hnOpenAI Says It Has Solved a Millennium Prize Problem–A Holy Grail of Mathfortran7752
🟠 redditToday is a historical moment.
singularity
No-Head-Royal1251652
🟠 redditOpenAI Used a Model "Significantly More Capable" than Astra to Solve Naiver-Stokes | Per Axios & Chubby on Twitter
singularity
141_13376648
🟠 redditOpenAI threatened to ruin star mathematician's career
OpenAI
PsychicorAI2365502
🟧 hnImprove the model for everyone: OpenAI and the Navier–Stokes problemdimberman31
🟠 redditOpenAI fought dirty on career-making math problem, says NYU mathematician
singularity
Nikvest2532
🟠 redditChris Combs, professor of Aerospace engineering, throws some cold water on OpenAI’s NS solution
singularity
Mindrust597220
🟧 hnTao: Open math problems being non-renewably mined by AI_alternator_407354
🟠 redditThousands of agents on a single engineering problem.
OpenAI
Klutzy-Smile-98399763
🟠 redditOpenAI researcher responds to scrutiny around its latest math breakthrough
artificial
LinkedInNews24
🟧 hnThe Navier–Stokes Millennium Prize Problemtosh122103
🟠 redditDid AI just cross a line we weren't expecting?
OpenAI
Enough_Basis_1997024
🟧 hnHow An AI math breakthrough ignited a controversypseudolus220232
🟠 redditOpenAI threw 10,000 agents at a 90-year-old math problem. Is this what billions buy?
OpenAI
Recent-Tangerine2745013
🟠 redditOpenAI just did a series of runs on a set of near-impossible problems with swarms that dwarf the huggingface swarm and a model that's 2 generations smarter...
singularity
wabawanga6749
🟠 redditWhat OpenAI’s latest controversy tells us about the future of math
OpenAI
techreview00
🟠 redditI Don’t Understand Why the Navier–Stokes Result Is Such a Sensation
OpenAI
kaljakin032
🟧 hnTo Serve Man: AI, Math, and Navier–Stokesbearseascape41
🟧 hn ⭐Lean certificates accompanying Navier-Stokes and Euler resultsthunderbong10
🟧 hnThe part of Navier-Stokes no one is talking aboutibobev180177
🟧 hnOpenAI's Bubeck denies trying to cut Anthropic mathematician from credithtk40
🟧 hn'Not selling my soul': Why this Aussie maths professor took on OpenAIAlien1Being80
🟧 hnA kernel run on the Navier-Stokes blowup Lean projectravanova40
🟧 openaiOn the Navier–Stokes Millennium Prize Problem
Retrieved article excerpt

Open article · Retrieved 2026-09-25T22:21:14.085075+00:00

September 8, 2026

[Research](https://openai.com/news/research/)[Publication](https://openai.com/research/index/publication/)

# On the Navier–Stokes Millennium Prize Problem

[Read the paper](https://cdn.openai.com/pdf/32d9f210-8b73-45e0-91bc-82a30aef8a9a/navier-stokes.pdf)[Link to Lean formalized proof](https://github.com/openai/NavierStokesAndEuler)

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We’re sharing a solution to the Navier–Stokes existence and smoothness problem, one of the Millennium Prize Problems. This proof, produced by an internal OpenAI system, shows that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time. We’re sharing both a writeup of the proof and a formalization in Lean.

The [Millennium Prize Problems⁠(opens in a new window)](https://www.claymath.org/millennium-problems/) represent some of the deepest questions at the frontier of mathematics. The question of whether smooth three-dimensional fluid motion can break down has remained unresolved for roughly 90 years.

A major goal of our work is to empower scientists to advance research and technology that benefits all of humanity. To solve the Navier–Stokes problem, we used an internal model that is significantly more capable than GPT‑6 Astra. We believe it is important to inform the world about the pace of AI progress and what to expect from upcoming models.

## The problem

The Navier–Stokes equations use Newton’s second law of motion (“F=ma”) to describe how fluids move. Importantly, they treat a fluid as a continuous medium rather than tracking individual molecules. These equations are used for aircraft design, weather forecasting, and the study of blood flow.

A fundamental open question for these dynamical equations has been whether the continuum approximation of the fluid can break down. Specifically, can the Navier–Stokes equations for a three-dimensional incompressible fluid with constant density develop a “singularity,” even when the motion starts smoothly? Here, a singularity means the dynamics lead to speeds in the fluid growing without bound within a finite amount of time. The development of a singularity would have to happen despite the presence of viscosity, which tends to smooth out motion. Because a real fluid cannot move infinitely fast, this would mark a breakdown in how the equations model the fluid. To continue modeling the system, one would then need to track the behaviour of each particle individually.

The equations date to the nineteenth-century work of Claude-Louis Navier and George Gabriel Stokes. In 1934, Jean Leray proved that solutions exist in a generalized sense, but whether they always remain smooth became a central unanswered question. In 2000, the Clay Mathematics Institute named the Navier–Stokes existence and smoothness problem one of seven Millennium Prize Problems.

## The result

Our system produced an analytical proof and a Lean formalization that an initially smooth fluid at rest can develop a singularity in a finite time. The fluid has a smooth force applied to it, and its energy remains finite through the entire dynamics, from rest to the formation of the singularity. This resolves the Navier–Stokes Millennium Prize problem by establishing statement “C” (and also “D”) in the [official Millennium Prize formulation⁠(opens in a new window)](https://www.claymath.org/wp-content/uploads/2022/06/navierstokes.pdf).

The solution is a vortex, a spinning swirl of fluid, that spirals inward and gets increasingly elongated, like spaghetti. This central region shrinks while it speeds up in such a way that its energy still stays finite, as required by the laws of physics. The technical challenge is for the equations to develop the breakdown through the motion of the fluid itself, rather than, for example, us putting in an infinite force by hand. More mathematically, the terms in the Navier–Stokes equations that describe the motion—acceleration, pressure gradients, momentum transfer, viscosity—must both become *big* yet *cancel* in a precise way. This detailed balance leaves a smooth external force even as the velocity of the fluid grows without bound.

Diagram of a swirling vortex illustrating inward spiral and axial stretching.

*A snapshot of local incompressible motion. Orange marks faster angular rotation; teal marks slower rotation. Circulating speed also depends on radius. The trajectories show inward spiraling and axial stretching.*

## How we found the proof

Since August 28 we have been training a new internal model that has exhibited unprecedented performance in our benchmarks, including mathematics. This model’s training is ongoing and its performance continues to improve.

On Tuesday, September 1, we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors and by the step change in performance of our internal model, we launched an effort to evaluate it on all open Millennium Prize problems and a few other high-impact problems.

We used a system of coordinating agents powered by our internal model. The agents had access to tools such as the ability to read from a cached version of the internet and the ability to run code. Agents were subdivided into groups with the ability to communicate within the group. The groups varied in size, and the group that produced the Navier–Stokes resolution involved on the order of 10,000 concurrent agents. At all times we maintained the same strict safeguards that we apply to all our frontier model evaluations, including monitoring and isolation.

For each problem, we prompted different groups of agents with different variants of the problem statement, covering all variants of the problem. For the Navier–Stokes problem, we suggested versions “A” and “B” (particular forms of the Navier–Stokes problem which would result in a proof) and versions “C” and “D” (which would result in a disproof) to separate groups of agents.

In addition to the full Millennium Prize problems, we asked our multiagent system to try a set of “easier” problems. One of these problems was a similar blowup question for the limit of the Navier–Stokes problem with the viscosity term removed. This is known as the regularity problem for the Euler equations, and our agents surprised us by resolving this question. The specific variant of the question that they resolved was the *unforced* version, where no external force is applied to the fluid. Nearly 100 agents worked together for approximately 50 hours to produce our Euler regularity disproof[1](https://openai.com/index/navier-stokes-solution/#citation-bottom-1).

Once we saw the Euler solution, we thought that Navier–Stokes was the most promising problem to work on. Thus, we decided to devote our resources to Navier–Stokes. To do so, we shifted agents away from the other Millennium Problems and prompted these agents with the Euler resolution. When a further trained version of our internal model became available over the course of the effort, we updated our agents to that model.

We encouraged different groups of agents to explore a diversity of approaches. After some time, we cross-pollinated the agent groups by using Codex to consolidate the most useful insights from each agent group. These follow-up prompts drew on the agents’ own intermediate results. The group that found the solution to Navier–Stokes was guided in such a way.

The agents arrived at their resolution on Saturday, September 5, about 88 hours after the first agents were launched. Lean formalization and verification took an additional 17 hours via GPT‑6 Astra.

Across all attempted problems, the agents sent 4.9 million messages and used about 300 billion output tokens. In the process of resolving the Navier–Stokes problem, the agents sent 2.7 million messages and used approximately 130 billion output tokens.

## Concurrent work

Our effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge, an Anthropic employee, and Tristan Buckmaster, a math professor at NYU.[2](https://openai.com/index/navier-stokes-solution/#citation-bottom-2) After the completion of our full project and Lean verification (on September 6th), believing from the rumor they also had a solution of Navier–Stokes, we reached out to them to offer a concurrent release of our result and to recognize their priority in a joint announcement. At that point we found out that, using an internal Anthropic model, they had produced a resolution of the forced Euler problem. In these discussions we offered them visibility into all of the prompts we used and later to see the proof. We recognize the priority of their work on forced Euler and congratulate them on their remarkable mathematical achievement.

We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem.

Following an investigation, we have confirmed that Buckmaster’s Codex prompts over the two months preceding this announcement and paper on September 8, 2026, could not have influenced the system in any way, including through training. The OpenAI internal model used for this result was developed through large-scale reinforcement learning on top of a previously pretrained model. Our proofs also differ significantly. In the Euler case, Alpöge and Buckmaster proved a result with external forcing, while OpenAI’s system proved a result without external forcing.

## Progress and responsibility

Our goal in releasing this result is to report on the substantial progress of our AI models. We do not intend to claim the Millennium Prize for this result.

This milestone represents substantial work by mathematicians and AI researchers. However, this is not a culmination, but rather a snapshot in time, of progress on AI development.

[We believe we are now in the next period of AI progress⁠](https://openai.com/index/research-acceleration-view-inside-openai/), and today’s results provide further evidence of this. We are focusing on understanding this model, and using what we learn to help us guide and pace how we pursue further advances in capability. One of our [key goals⁠](https://openai.com/index/built-to-benefit-everyone-our-plan/) is to build AI systems which are steerable, accountable, and connected to people, which may require more deliberate choices about the pace of progress, as we continue our mission to ensure AGI benefits all of humanity.

- [2026](https://openai.com/news/?tags=2026)
- [Generative Models](https://openai.com/news/?tags=generative-models)

## Author

OpenAI

## Footnotes

1. 1

   [Read the Euler proof paper⁠(opens in a new window)](https://cdn.openai.com/pdf/315b36cd-ec98-4023-8342-93345194ece1/euler.pdf) · [Link to Lean formalized proof⁠(opens in a new window)](https://github.com/openai/NavierStokesAndEuler)
2. 2

   Update — September 10, 2026: We have updated “Concurrent work” with findings from our investigation into whether user inputs could have influenced this result.

## Keep reading

[View all](https://openai.com/news/)

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ProductSep 22, 2026](https://openai.com/index/introducing-gpt-6-sol-and-luna/)

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ResearchSep 16, 2026](https://openai.com/index/model-misalignment-reporting-framework/)
OpenAI——
🟧 hnClay Mathematics Institute on the Navier-Stokes Problemrvz337286
🟠 redditQuestion about the Lean formalization of the recent Navier–Stokes blow-up result: is compact support of the force postulated rather than proved?
OpenAI
Illustrious-Bench72652
🟠 reddit"Today, CMI shares in the excitement of the global mathematical community as we contemplate the announcement that the Navier-Stokes problem has apparently been settled"
singularity
badumtsssst14627
🟧 hnAfter Maththrowaway81523155169
🟧 hnThe proof passed. Can you see why it works?ashdee20
🟠 redditWhere the targets of recent AI results would rank in the top 500 open math problems if they were still open
singularity
zero0_one12121
🟠 redditDid open ai actually stole the solution to the Navier Stokes Equation,
OpenAI
ExamImmediate8956016
🟧 hnDid OpenAI solve the wrong Navier-Stokes problem?tomjakubowski12258
🟧 hnDid OpenAI solve the wrong Navier-Stokes problem?algoth180
🟧 hnWe're gonna need a lot more mathematicianssrcreigh408498
🟠 redditOpenAI revealed how its ~10,000-agent Navier–Stokes swarm actually worked: separate research teams explored different ideas, communicated across groups, and combined what worked
singularity
141_133715532
🟠 redditIn the description of Numberphile's latest video of an interview with Tristan Buckmaster on Navier-Stokes problem
OpenAI
Busy-Contact-513340
🟧 hnThe Future of Mathematicssmilelamp12390
🟧 hnNavier–Stokes Lost in Translationnill0359233
🟠 reddit[2610.08144] Navier-Stokes lost in translation: Why Lean verification of AI autoformalisation does not guarantee correct natural language proofs
OpenAI
Turbulent_Breath_548172

Interpretation history

Decision trace