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AI has solved a "moderately interesting" problem in FrontierMath: Open Problems, a benchmark of real research problems that mathematicians have tried and failed to solve epochai.substack.com/p/first-ai-sol…

We’re launching with two new posts. Can AI do theoretical physics? Harvard physicist Matthew Schwartz led Claude Opus 4.5 through a graduate-level calculation. AI can’t yet do original work autonomously, but it can vastly accelerate it. Read more: anthropic.com/research/vibe-…

#New_arXiv_paper Jakob Greilhuber and *Roohani Sharma*, A dividing line for structural kernelization of component order connectivity via distance to bounded pathwidth, 2026. arxiv.org/abs/2603.22240

@MTHajiaghayi Congratulations!

Excited to lead a UMD–MIT team on a $2.6M DARPA expMath grant to advance AI for tackling long-standing open problems. Program x.com/patrickshafto/… initiated by @patrickshafto include Sanjiv Arora, Shafi Goldwasser, Venkat G, Amit Sahai, Terry Tao. More: today.umd.edu/umd-to-lead-da…

The second round of #FirstProof is announced: 1stproof.org. Glad to see the organizers planning a rigorous process. Most AI benchmarks today still rely on the "honor system". That’s billions of valuation dollars hinging on integrity, even while there are many performance-boosting tricks that are undetectable in results (adding hints to prompts, human intervention, best-of-N, etc). For this reason, one should interpret one-off performances by proprietary models (e.g., on math contests) with a grain of salt–especially when they are carried out after solutions are already posted online. FirstProof will be the only evaluation based on the natural distribution of research mathematics that guarantees autonomy and transparency of results. It should be considered the gold standard benchmark in the AI4Math space.

I'm releasing OpenProver v1.0.0! It's 1) an open-source automated theorem prover inspired by DeepMind's Aletheia (@tonylfeng @gjb_ai @lmthang), and 2) a "Claude Code for mathematicians", allowing interactive proof search in English and formalization in Lean.

오늘 KAIST에서 작년 연구실 사고로 부상을 입은 오신비 학생을 다시 만났습니다. 지난 해 병원에서 처음 본 이후 세 번째 만남으로, 이번에는 총장님, 지도교수님과 함께 학생의 치료 경과를 세심히 살피며 재활과 학업 복귀 지원 방안에 대해 진솔한 대화를 나눴습니다. 오신비 학생은 사고 이후 긴 치료를 이어가면서도 현재 집에서 수업을 들으며 학업과 논문 작성을 계속하고 있습니다. 오늘 만남에서도 학업에 대한 의지와 연구를 향한 열망이 느껴졌습니다. 손 부위 역시 여전히 치료가 필요한 상황이지만, 재활 과정에서 직접 만든 인형과 향기 나는 꽃을 선물로 건네주었습니다. 그 작은 선물에서 얼마나 큰 용기와 시간을 버텨왔는지 느낄 수 있었습니다. 아이를 키우는 부모의 입장에서 이 학생의 시간을 생각하면 마음이 무겁기도 합니다. 그럼에도 불구하고 다시 일어서려는 모습에 깊은 존경심이 듭니다. 과학기술은 도전 위에서 발전하지만, 그 과정에서 연구자 한 사람, 한 사람의 안전과 삶은 그 무엇과도 바꿀 수 없는 가치입니다. 정부도 치료와 학업 복귀를 끝까지 지원하고, 연구실 안전과 재발 방지를 위한 제도 개선도 함께 챙기겠습니다. 다음에는 학교에 직접 나올 수 있게 되면 같은 연구실 학우들과 함께 학식을 먹으며 다시 만나자고 약속했습니다. 그 약속이 지켜질 수 있도록 끝까지 관심을 가지고 지원하겠습니다. #KAIST #연구실안전 #인재 #과학기술

“원심분리기 문제”는 슬롯이 n개인 원심분리기에 시료 k개를 균형있게 배치할 수 있는 조건을 찾는 수학 문제. 신기하게도, k와 n-k를 n의 소인수들의 합으로 나타낼 수 있다는 게 필요충분 조건. 예를 들어, 7과 12-7=5는 2와 3의 합으로 나타낼 수 있으므로 배치 가능. mattbaker.blog/2018/06/25/the…

#New_accepted_paper Seoyoung Kim, Chi Hoi Yip, and *Semin Yoo*, f-Diophantine sets over finite fields via quasi-random hypergraphs from multivariate polynomials, Mathematika, accepted, 2026. arxiv.org/abs/2503.19603

4w ago I was a Claude Code skeptic. I'm not a coder. None of the use cases were relevant. I managed teams & projects, drowning in email & overdue reminders. So I tried creating tools that would help me and... holy crap. Now I'm sharing the tools I built: claudeblattman.com

Serious advice to scientists and mathematicians, especially those doing theoretical work: start using LLMs, and set your sights higher!

My daily vibe coding of math-ML demonstration project: edu.kimsh.kr

Terence Tao responding to a question on what advice he would give someone considering a career in math in 2026: 'Yeah, so we live in a time of change. It is, as I said, we live in a particularly unpredictable era. And I think things that we've taken for granted for centuries may not hold anymore. So, yeah, the way we... do everything, not just mathematics, will change. In many ways, I would prefer the much more boring, quiet era where things are much the same as they were 10 years ago, 20 years ago. But I think one just has to embrace that there's going to be a lot of change and that, you know, the things that you study, some of them may become obsolete or revolutionized, but some things will be retained. There'll be a lot of opportunities for things that you wouldn't be able to do before. So, I mean, in math, you previously had to basically go through years and years of education to be a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level or whatever, that you could get involved in a math project and actually make a real contribution because of all these AI tools and lean and everything else. So there'll be a lot of non-traditional opportunities to learn. So you need a very adaptable mindset. There'll be one for pursuing things just for curiosity, for playing around. And I mean, you still need to get your credentials. I mean, I think for a while it would still be important to sort of still go through traditional education and learn math and science and so forth the old-fashioned way for a while. Yeah, but you should also be open to very, very different ways of doing science, some of which don't exist yet. Yeah, so it's a scary time, but also very exciting.'

Our AlphaProof paper is in this week’s issue of @Nature! In 2024, @GoogleDeepMind's proof agents AlphaProof & AlphaGeometry together made a substantial leap in AI by achieving the silver-medal standard in solving IMO problems. The Nature paper describes the technical innovations required—in particular, the RL loop bridging natural language & symbolic rigor—that made AlphaProof possible.

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@mathematics_inc @DARPA Please fix the installer script. github.com/math-inc/OpenG…

Today, at the @DARPA expMath kickoff, we launched 𝗢𝗽𝗲𝗻𝗚𝗮𝘂𝘀𝘀, an open source and state of the art autoformalization agent harness for developers and practitioners to accelerate progress at the frontier. It is stronger, faster, and more cost-efficient than off-the-shelf alternatives. On FormalQualBench, running with a 4-hour timeout, it beats @HarmonicMath's Aristotle agent with no time limit. Users of OpenGauss can interact with it as much or as little as they want, can easily manage many subagents working in parallel, and can extend / modify / introspect OpenGauss because it is permissively open-source. OpenGauss was developed in close collaboration with maintainers of leading open-source AI tooling for Lean. Read the report and try it out:

We congratulate Gerd Faltings as the 2026 Abel Prize laureate! 🎉 He recives the Abel Prize "for introducing powerful tools in arithmetic geometry and resolving long-standing diophantine conjectures of Mordell and Lang".

Aristotle이라는, AI를 사용하여 수학 증명을 형식 증명으로 만드는 도구를 오늘 처음 사용해보았습니다. 최근에 arXiv에 올린 제 논문을 lean 언어로 바꾸라고 했더니 반나절만에 성공했구요, 심지어 제 논문에서 lemma 하나에서 가정 빼먹은 사소한 실수도 지적해주었습니다.