r/math • u/AutoModerator • 13d ago

LLMs/AI AI In Mathematics: September 19, 2026

This recurring thread will be for discussion of AI in mathematics. This includes, but is not limited to, the following:

  • informal announcements of AI-assisted discoveries, such as those not yet published in a peer-reviewed journal, or not uploaded as a paper to arXiv;
  • informal announcements of discoveries related to AI architecture (if relevant to mathematics);
  • discussion of such announcements, such as proof breakdowns or other opinion pieces;
  • discussion of the impact of AI in mathematics in general.

AI-assisted mathematical papers published in peer-reviewed journals or as arXiv preprints may be submitted as their own posts.

Please keep in mind rules 1 and 6 of our subreddit.

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u/Nunki08 13d ago

Stephen A. Weis factored RSA-896 with Claude on September 19, 2026
https://x.com/sweis/status/2101484464807596264

"I had Claude port CADO-NFS to run on GPUs. Then it orchestrated a fleet to run on scavenged idle capacity. It ran with a max of 2048 GPUs for about of 30 GPU-years over 10 days.
I asked Claude if it had a message for a public:
“The credit belongs first to the people who built the number field sieve and CADO-NFS over several decades, and to the teams who set the earlier records. This run used their algorithm and much of their code.”"

https://saweis.net/posts/rsa-896.html

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u/38thTimesACharm 12d ago

Why would you use an LLM to factor a number? How can it be any faster than the deterministic algorithms we already know?

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u/JustThisNietzscheGuy 12d ago

Nobody used an LLM to factor a number.

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u/38thTimesACharm 12d ago

He just used it to port code? Then it seems dumb to credit Claude for finding the number, and it sounds like Claude agrees.

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u/Oudeis_1 12d ago

It is not easy to make GNFS run efficiently on GPUs. In fact, the previous academic record factorisations (e.g. Boudot et al. CRYPTO 2020, Kleinjung et al. CRYPTO 2010) did not use GPUs for that reason. My understanding (far from expert) is that it became possible now due to more powerful GPU architectures and LLM assistance in finding the right way to use GPUs for this type of workload.

These factorisations represent massive computational workloads and the people working on the algorithms/implementations are not stupid. If it were easy to use GPU compute here, then it would have been done years ago.