Back in May, we released the AlphaProof Nexus paper as a preprint. Following peer review, it has now appeared in @ScienceMagazine. science.org/doi/10.1126/sc…

I loved working on this project for many reasons. We clearly demonstrated the value of AI-aided formal methods and open njectures in research math. I learned a lot from my excellent teammates. The greatest joy, though, was to see AlphaProof Nexus help mathematicians such as @GergelyBerczi and @MarioKrenn6240 in their work.

AI-for-math has come a long way since May. These developments only strengthen my conviction that formal proof assistants are an essential trust substrate for AI-for-math, and that meaningful progress in this field requires genuine collaboration with mathematical communities. Onward!

This post points to something deeper for me. The amount of AI-generated content in presentations, speaker notes, research ideas, and academic papers is getting insanely high. Pangram is just an off-the-shelf detector, but it doesn’t detect maybe more than 90% of the meta-cheating gymnastics happening behind the scene with multi-agent supply chain.

There are times when I genuinely worry about the homogenization of our thoughts, slides, concerns, and work. Othertimes, I tell myself: this is a mass experiment. I do hope we find an equilibrium in which we can benefit from AI without losing our authenticity and depth.

Gemini 4 Argon is now globally available.

Built to solve your hardest problems from start to finish, the model brings frontier capabilities across coding, enterprise workflows, and cyber defense.

A new AI framework called AlphaProof Nexus can autonomously tackle select math problems, seeking proofs and using verification to ensure the resulting solutions are logically sound, researchers report in Science.

📄: scim.ag/4y7Rlyx
#SciencePerspective: scim.ag/4s0Kqpx

Reading all those recent beautiful essays by Terry Tao, Kevin Buzzard and @wtgowers, I could not resist, but write up my deeply personal perspective on how I perceive and see things:

docs.google.com/document/d/e/2…

Introducing Gemini 4 Argon – our new frontier model.

It’s built for complex workflows across coding, enterprise knowledge work, and cybersecurity defense – rolling out today to a set of trusted testers through our Fairwind Program.

Without commenting on the correctness of this claim, I want to briefly say a few words about what it would mean in practice.

Chess is supportable as a hobby in part because one can pick up the rules of chess in an afternoon. While one can't quickly play at a high level, it is eciate the game, and have fun at it.

Research math is not like this. Over the last decades we have made incredible progress understanding basic concepts (number, shape, etc.) but the edifice we have built to do so requires an incredible time investment. Learning the ideas that go into proving, or understanding the proof of, basic statements like Fermat's last theorem requires years of institutional support.

It's possible that soon, AI will be able to do work at this level; despite recent successes, this isn't the case yet. But even if it can do this, why would it? Without people who care about these deep but fundamental ideas, who will ask it to? Perhaps it might on its own, or a person asking it to "do some cool math" might elicit a similarly deep idea, but who would understand it, if doing so requires years of unsupported work?

In practice, turning pure math into a hobby means the end of pure math, and the end of people who understand basic mathematical concepts at a deep level. Whether this is something we want is something we'll have to decide together.

The AMS’s statement is short and sweet:

ams.org/news?news_id=7…

"The news today of progress on resolving the Navier–Stokes problem, one of mathematics' great longstanding challenges concerning the equations that govern the flow of fluids, represents a milestone advance in human knowledge. This story began with Navier, Stokes, Leray, and Ladyzhenskaya and has culminated in the recent breakthroughs of Córdoba and Martínez-Zoroa, then — assisted by new technologies — Alpöge and Buckmaster, with the final steps taken by OpenAI mathematicians. The purpose of mathematics is human understanding, and this achievement, and the process that led to it, will bear fruit for a long time to come."
 
Ravi Vakil, President of the AMS
John Meier, CEO of the AMS

I did not expect this. Anthropic just published a Lean proof of Fermat's Last Theorem.
The proof is +13M LoC, more than 5 times the size of Mathlib.

Kevin Buzzard's post: xenaproject.wordpress.com/2026/09/04/flt…
The proof: github.com/anthropics/fer…
Anthropic's post: anthropic.com/research/forma…

4000 GB200s just arrived in Texas for the Horizon TACC cluster. I'm informed this is the largest academic supercomputer. Very excited to start training some open models with this bad-ass machine.
(photo courtesy of Adam Klivans, director of NSF IFML)

Introducing Gemini 3.8 Flash, another jump in Gemini's agentic + coding capabilities, and our 3rd updated Flash model in only 6 weeks...

This model has been a ton of fun to work with, excited to see what you all think!

Google Research and Google DeepMind researchers just used Teamwork in Antigravity to achieve breakthroughs in theoretical computer science, research mathematics, and systems engineering. Teamwork is a multi-agent framework where agents propose, stress-test, and build solutions or days. It uses a lot of tokens and is overkill for everyday tasks, but if you're tackling frontier, long-horizon challenges - give it a try!

I'm incredibly excited to lead the new NSF Center for Human and Robot Co-Adaptation!
It is one of three new $30M, 5yr Centers launched under the @NSF STC program, and is led by @UTAustin , with partners @IUBloomington , @MIT , @TuftsUniversity , @UUtah , and @Yale

We applied AlphaEvolve's autoresearch powers to an ML pipeline tackling one of the most famous problems in CS: time complexity of matrix multiplication (ω). We improved the SOTA! A small step for ω (similar to recent works), but a nice milestone for AI arxiv.org/abs/2608.16884…

Some exciting new AI-for-math results from my students @rah4927 and @ByAlanLi!

3.7 Flash: Fast, 50% cheaper, happened in ~3 weeks

3.7 Flash : )