I have read the reaction of @marcusgnt and @ValerioCapraro to @ElliotGlazer astounding announcement. I strongly disagree with them and for practical reasons.
I’m working on understanding and rewriting the OpenAI H10 paper: very far from done. But bringing Glazer’s work into the mix was actually helpful. It has shed light on some of the rewritings that a collaborator and I are trying to make. I’m still digesting things, and I’m not claiming to have verified his proposed proof, though I definitely hope eventually to understand it as well.
I disagree with the idea that “slop on slop” is somehow additive. The effect need not be cumulative in the negative direction. There can be a nonlinearity here, where an attempt at a harder result can shed light on an earlier undigested argument. That is actually exactly what I’m seeing empirically in this case. Making more raw material available can actually help.
It is the responsibility of the mathematical leadership to design new incentives that promote understanding. Asking people not to produce more raw material seems just not empirically useful and not an adequate response from academic leadership.
Regarding Capraro's outrage. "The argument looks plausible, and plausibility takes the place of verification." Who said that this is so? The mathematical community will have to digest and verify these arguments of course. However the plausibility here is valuable for this process: this is more raw data that has a strong chance to be correct and be actually profitably used by the humans thinking about the proof. And I am not talking in generalities: this is exactly what is happening here.
Timo Keller and I have posted our resolution of questions of Venkatesh, Bost–Charles, Frey–Kani–Völklein, and going all the way back to Ihara: arxiv.org/abs/2610.06461. We construct Golod–Shafarevich towers in dimension 2, i.e. infinite (geometric) unramified towers of arithmetic surfaces.
The general vibe was that such surfaces should not exist. Timo and I laid out our program to construct them back in 2022: that is more or less the first half of the proof. This summer, helped by a little army of AI agents, we managed to finish the climb: we used GPT 5.6 Sol, Fable and Aletheia internally at Google DeepMind. The experience is described in some detail in Section 1.4 on page 5: it has been by far the most exciting human-AI collaboration that I have personally taken part to, so far.
Simultaneously and independently, Pablo Chenal has also settled the questions of Bost–Charles, and consequently Frey–Kani–Völklein: arxiv.org/abs/2610.05972. Pablo gives a much shorter and more elegant existence proof, while our approach is heavier but makes the surfaces explicit. Pablo, Timo and I are currently working towards a unified perspective and merging the two works into one.
I have been dreaming of finding such a surface since 2022, and it is truly exciting to be able to finally share it!
Here is Timo's formalization in Lean of our proof (modulo standard facts on étale cohomology): github.com/TimoKellerMath/Go…
Mark Shusterman and I have posted our resolution of the probabilistic version of Shafarevich's conjecture, posed by Liu--Wood and Sawin--Wood: arxiv.org/abs/2609.30200
The main idea of the proof was discovered independently by GPT5.5 pro and Aletheia, an internal agent at Google DeepMind. While digesting the proof we discovered several new applications. Among these, we settled a conjecture of Nikolov on the existence of universal perfect groups. This latter result came from an interesting experiment in letting the agent pattern-match problems to methods in reverse.
I wanted to see the random group question settled since I studied Melanie and Yuan's paper back in 2021. Very happy this is finally done!
Classical constructions in explicit class field theory generate abelian extensions using division points on algebraic groups. A conjecture of Andrews–Petsche predicts that this is the only way to generate infinite abelian extensions by repeatedly taking preimages of points under polynomial maps.
Today, two independent papers settle this conjecture: one by Zhuchao Ji, Jiarui Song and Junyi Xie, and one by Andrea Ferraguti and me!
arxiv.org/abs/2609.24701
arxiv.org/abs/2609.24938
In a parallel project, appearing also today, Andrea Ferraguti, Patrick Ingram and I investigate the global function-field analogue. We formulate a precise conjecture incorporating new phenomena from Drinfeld modules and we prove it in degree smaller than the characteristic arxiv.org/abs/2609.24920.
Still a lot to explore in the function field case!
AI played a decisive role in the project with Andrea, while almost no role in the project with Andrea and Patrick.
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Peter Koymans, Mark Shusterman and I have just posted our resolution of the probabilistic version of Chowla’s non-vanishing conjecture for imaginary quadratic characters over Fq(T), with q ≡ 1 mod 8:
arxiv.org/abs/2609.11855
(Over function fields, one cannot expect deterministic non-vanishing: Wanlin Li constructed infinitely many examples of vanishing)
We have been working on this for seven years, and it is very exciting to finally be able to share it!
Carlo Pagano retweeted
AI and (Research) Mathematics. A thought experiment to break past the shallowness of hot takes and pre-IPO marketing. 1/
Carlo Pagano retweeted
A few months ago I bumped into Anand Patel, who had been my algebraic geometry TA in college, visiting Google DeepMind. He agreed to try out an agent I was building called Aletheia.
Fast forward: Anand prompted Aletheia to solve a problem about simplicity of the Hodge bundle on M_g that had been floating around (a part of) the algebraic geometry community for at least ten years. Check out his paper at arxiv.org/pdf/2603.19052
Carlo Pagano retweeted
“Mathematics is undergoing the biggest change in its history.”
Glad to see our math agent #Aletheia featured by @newscientist on its results at the FirstProof inaugural challenge, alongside other interesting milestones in AI for Math research! Implications worth thinking about. Link in thread.
Carlo Pagano retweeted
Thrilled to share: #Aletheia, our math research agent, just solved 6/10 notoriously hard FirstProof problems autonomously, the best result in the inaugural challenge! To me, this is even bigger than our historic IMO-gold achievement last year; these problems challenge even top mathematicians. We share our results transparently, see paper and full thoughts in the thread. 👇
Carlo Pagano retweeted
Our blog post describes how Deep Think agents are making research progress in pure mathematics, physics, and computer science.
deepmind.google/blog/acceler…
Carlo Pagano retweeted
6 months in, after the IMO-gold achievement, I’m very excited to share another important milestone: AI can help accelerate knowledge discovery in mathematics, physics, and computer science! We’re sharing Two new papers from @GoogleDeepMind and @GoogleResearch that explore how Gemini #DeepThink together with agentic workflows can empower mathematicians and scientists to tackle professional research problems. Some highlights:
The first paper built a research agent #Aletheia, powered by an advanced version of Gemini Deep Think, that can autonomously produce publishable math research and crack open Erdős problems.
The second paper, built on similar agentic reasoning ideas, helped resolve bottlenecks in 18 research problems, across algorithms, ML and combinatorial optimization, information theory and economics.
See the thread for details about the two papers and the joint blog post.
Very excited to share that an advanced version of Gemini Deep Think is the first to have achieved gold-medal level in the International Mathematical Olympiad! 🏆, solving five out of six problems perfectly, as verified by the IMO organizers! It’s been a wild run to lead this effort and I am grateful to everyone in the team for such an amazing achievement! Blog post in the thread and more to share soon!
Carlo Pagano retweeted
This paper describes our Aletheia system that solved multiple open Erdős problems. It also contributed intermediate propositions on two research papers, collaborated with a human author on a third paper, and produced a standalone fourth paper on its own. github.com/google-deepmind/s…
More work in collaboration with Aletheia:
Replying to @kimshmath
* Strongly Polynomial Time Complexity of Policy Iteration for L∞ Robust MDPs
arxiv.org/pdf/2601.23229
Human found a theoretical theorem in the complexity theory, and AI produced a generalization. (6/6)
Carlo Pagano retweeted
Six very recent AI-math papers from our team:
* Irrationality of rapidly converging series: a problem of Erdős and Graham
arxiv.org/abs/2601.21442
An Erdos-1051 problem was solved and generalized together by an AI and human authors. It required a *lot* of human efforts, especially in the generalization part. (1/6)
Mathematicians 🤝AI researchers arxiv.org/abs/2601.22401. Our take on AI solving Erdos problems:
* Many "Open" problems are actually just obscure: many cases the AI didn't find something new, only rediscovered solutions buried in the literature. We present our systematic approach to reporting AI results on Erdos.
* The real bottleneck is still human labor, e.g. we spent lots of time filtering out technically correct but meaningless solutions (AI missed Erdos’s original intent).
* Acceleration in solving low-hanging fruits is real, but we also need to highlight the many more misses that require human auditing. Clear research directions ahead though, and we feel optimistic about drastically increasing the signal-to-noise ratio.
More to come!
Replying to @lmthang
Here's the paper link to our scaled effort for tackling Erdős problems. We started with 700 problems marked ‘Open’ in the database. Our agent #Aletheia identified potential solutions to 200 problems. Initial human grading revealed 63 correct answers, followed by deep expert evaluation and discussion to eventually arrive at meaningful proofs to 13 Erdős problems. arxiv.org/abs/2601.22401
Excited to share our latest work: "Semi-Autonomous Mathematics Discovery with Gemini." We used Gemini to systematically evaluate 700 "open" conjectures in the Erdős Problems database.
The result? We addressed 13 problems marked as open—finding 5 novel autonomous solutions and identifying 8 existing solutions missed by previous literature.
Read the full case study here: arxiv.org/abs/2601.22401
Carlo Pagano retweeted
This is the second paper from our series “Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erdős Problems”. Our agent #aletheia powered by #DeepThink cracked 13 ‘Open’ Erdős problems: 5 novel autonomous solutions, and 8 through existing literature. It was a lot of fun as the world went crazy on Erdős problems while we patiently scanned over 700 problems :) Paper link and more details below.
There has been so much noise on AI for Math research. We have been working on research-level math for over a year (in parallel with our IMO Olympiad math effort) and obtained many results including solving Erdős problems (and beyond!). We haven't shared much in the past yet as we want to do things responsibly with respect to the math community. We're almost done! Yesterday, we released the first paper in our series: Solving a generalized version of Erdős-1051 problem! More to come! #DeepThink
Carlo Pagano retweeted
Some mathematics can simply never be known. quantamagazine.org/new-proof…























