The original article reports on how AI is changing the mathematics profession, as one mathematician reflects on the shift. The provided text is limited to that single sentence, so no specific models, benchmarks, or figures can be verified.
Generating more output tokens doesn’t necessarily translate to a higher score. GPT-6 Astra (max) scores 8.6% on ~81k output tokens per task, under half the ~180k of Grok 4.7 (xhigh). Three Claude models generated the most output tokens (~202k to ~562k per task) and score 2.8% to 6.4%.
Interesting to see, given the controversy over the OpenAI release of a series of proofs and what it means for the discipline of mathematics, that at least some of the OpenAI proofs seem to have kicked off extremely rapid iterative advances from a wide community of collaborators.
Lewis Tunstall quotes Kyle Cranmer's post about a paper by Nate Gunnarsson on a non-perturbative approach to chiral fermions in the Standard Model, extending Lüscher's abelian result. The paper's acknowledgments state that OpenAI's GPT-6 Astra model was essential, proposing refinement strategies, writing rewrites of the proof, and carrying out Lean verification.
Stanford HAI associate directors Risa Wechsler and Russ Altman, speaking at a Stanford orientation, argued that AI agents can deepen scientific research but must be paired with interdisciplinary collaboration. They stressed rigorous, reproducible methods and clearly measured uncertainty, since convincing AI answers are not enough. They also said labs must weigh agent costs and preserve mentorship so that automation supports human participation in research.
Wow, this is truly inspiring!!
TypeSafe AI released Jev, a "decision model" that returns a floating-point probability rather than text, which can answer yes/no and multiple-choice questions and score content against criteria. The source cites two browser-extension examples: semantic Ctrl+F search and webpage quality scoring. Simon Willison's criticism is that Jev offers no explanation for its numbers.
Ethereum researcher Justin Drake warned on X that AI-assisted math could, in the worst case, break the signature system used by crypto wallets within months, and urged a "bunker mode" in which users move funds to addresses that have never signed a transaction. Vitalik Buterin agreed but cautioned against moving too fast, saying he has lost more money to botched migrations than to hacks. No one has yet broken the current ECDSA signature scheme in practice.
You can either hold crypto and keep being stressed out if a math breakthrough would drain your wallet; or someone stealing your keys would drain your wallet; or someone kidnapping you and forcing you to hand over your keys would drain your wallet Or you can just not hold crypto
Scott Aaronson reports, based on his sources, that some AI companies have begun discreetly investigating whether their latest internal models can break important cryptographic protocols and primitives. He notes that cryptography is conspicuously absent from OpenAI's list of 376 papers, and the quoted post adds that the US government has censored academic quantum cryptanalysis results.
François Chollet compares AI progress to human learning, noting that 1980s research found programming training improves coding but does not transfer to general reasoning. He argues general intelligence is a fundamental brain property rather than a trainable skill, since domain practice improves only that domain. The post is framed as background for his question whether AI's jagged frontier, driven by math and code via RLVR, reflects general capability or continued human-data bottlenecks.
Scott Aaronson on The Mathocalypse. 'The UGC proof invents a completely new bizarre code with a noise test. It's some crazy recursive construction. It's not the long code, not the short code - some alien craziness' I hear a lot of this today. Get used to alien craziness.
Ethan Mollick shares early first-hand accounts from mathematicians grappling with hundreds of AI proofs released by OpenAI. He highlights problems solved in ways no human has yet understood, raising questions about what it means to know something. The linked Scott Aaronson post quotes a researcher, Dana, describing the proofs as unclear and hard to read without AI help, with some possibly verified by a Lean certificate.
Noam Brown says LLMs have crossed a threshold by surpassing top human experts on some research problems, a jump that makes the recent surge in math results feel sudden. He expects breakthroughs in other domains to follow as models keep improving, though capabilities remain jagged and often still weaker than humans.
Math was bound to fall first. The purer the field the easier it is for AI to crack.
The AI models had no information about SDPO. Hence, they either had to independently invent something like it, or invent another technique with comparable benefits, under similar constraints.
The post argues that companies are increasingly recognizing business opportunities from reinforcement learning, since many real-world tasks need specialized models, harnesses, and data flywheels rather than AGI. It predicts a new post-training era led by full-stack AI companies, though it provides no concrete figures or raw data to support the claim.
Gary Marcus argues that OpenAI's math announcement omits the procedure, the model architecture, and the failure rate, so its generalizability cannot be assessed. He says it could be a step toward AGI or a Lean-based verification trick in a verifiable domain, and the initial report cannot distinguish the two. The post includes a quoted Terence Tao post that shares a satirical press release about a fictional film-endings repository.
The Navier-Stokes moment was always much more about the figure below than about the Navier-Stokes problem itself. The run represents a decade of mathematical progress in a week. Can't wait to point these tools at life sciences, building our next models, and alignment!
Deedy argues LLMs have made substantial progress on four of the seven Millennium Prize problems, including a claimed Navier-Stokes result, conditional on verification. He says OpenAI's results averaged only 3 hours of thinking compute on unreleased models. He concludes that by most definitions of AGI, we have already achieved it.
Prompt engineering is not dead. It was never alive in the first place. The better these models become, the less we'll need to resort to phrasing tricks or weird instructions. The endgame is models that understand the way we communicate. "Prompt engineering" will simply become "communication skills".
OpenAI has released a document with over 300 math solutions, many of potentially historic importance, at varying stages of verification. The author argues that the results leave human mathematicians as spectators, with AI now doing the discovery work.
OpenAI released 722 mathematical manuscripts in 372 families, produced by an unreleased frontier model, with the average result taking the equivalent of three hours of ChatGPT Pro thinking. The author notes many results are verified in Lean but not all, and suggests mathematics could divide into vast machine-verified work and a compressed human 'effective theory' that people can actually understand.
LLMs have many dimensions that can be scaled. But OpenAI's recent underperformance tells us that verifier scaling currently appears to be the most boring, slowest, and worst-experience way to scale.
Jake Boggan, a Hacker News commenter, reacted to reports that Barnette's Conjecture, a graph theory problem he spent years studying, has been proven, as listed in openai/math problem 180. He said he had spent thousands of hours on the problem and had briefly believed he solved it last summer. He described the news as bittersweet.
2 years ago in 2024, we were laughing at GPT-4o for getting “Is 9.9 > 9.11?” terribly wrong. Now it feels like all the hardest math problems will be solved by AI. What a wild time we’re living in.
This generalizes to everything and continues to scale.
This is the most important plot from the Beam release IMO. The Chinese models are great, but horribly token inefficient (try running an eval with max reasoning to feel the pain). I'm looking forward to a future where open models start competing on this axis!
Mike Knoop argues AI can now automate conceptual search, transformation, and verification toward new science. He says AI can tell whether an open problem needs new ideas or whether the answer is already latent in existing knowledge. He calls this the most significant change in the philosophy of science since writing was invented about 6,000 years ago.
A breakdown of OpenAI's released internal-model math results shows about 73 disproofs and counterexamples, roughly 20% of the total. The author argues this counters claims that recent math breakthroughs are concentrated in counterexamples because models are only good at brute-force search.
i’m surprised all of the ai lab math results have been real so far: we haven’t found any with profound errors or a real bug yet, when you should expect so from humans. i assume at least a couple of these results today shouldnt survive scrutiny?
François Chollet asks whether the jagged frontier of AI capability is mainly math and code, which can be pushed far with RLVR. He questions whether steady gains in non-verifiable areas come from higher generalization driven by RLVR or only from continued injection of new human data.
AI x mathematics ftw. What an incredible release today from OpenAI 🤯 There's a lot of work to do to get models of the same caliber in the physical sciences, but just think of the possibilities as we get there. And we will get there.
OpenAI has published a repository called Openai/math, which the author reads as a sign that math problems, or any verifiable problems, are being solved. The author says OpenAI's tools exhausted their Pro token allowance on subagent tests unrelated to their main task, concluding that the work was aimed at verification for its own sake.
Time for every AI account to pretend they deeply understand number theory again to opine on the importance of the latest math breakthroughs.
nephs may not remember that in 2023 GPT-4 was getting confused by elementary school story problems
A post by Will DePue titled "Fable 5.1's list" presents 100 mathematical results and says 59% were released today, 87% AI and 13% human. The list includes items attributed to OpenAI, Anthropic, Google DeepMind and human mathematicians, each marked by a colored indicator, and it describes many entries as formalized in Lean or as openai/math family numbers. The post supplies no independent verification of these claims.
Microsoft Research podcast host Chad Atalla interviews Jennifer Neville, a partner research manager at Microsoft, about her path into AI and her work on how evaluation exposes surprising failures in models tested beyond traditional benchmarks. The conversation also covers practical guidance for working with current AI systems and why examining underlying data matters when results defy expectations.
Dust is a zeroth-order method that pretrains transformers and sometimes matches or exceeds backprop given large compute. The authors report it is about 1,000 to 10,000x more compute efficient than EGGROLL, the state-of-the-art ES method, for training transformers. The post also cites the gradient-alignment result up to 1B tokens and the virtual population idea for scaling.
Encrypted reasoning is the worst thing to happen to LLM evals