Automated AI theorem proving has moved the frontier: The holy grail is not an AI that can produce an endless pile of true theorems. It’s an AI that can discover mathematics, and build each discovery into the foundation for the next. Check out our new paper on Learning to Discover *Interesting* Mathematics.

Interested in World Modeling with a lens on Physics? We are organizing the World Modeling for Physics workshop at the wonderful Aspen Center 2/28-3/5/27. Apply for presentations (deadline Oct 9) or participation (by 9/30)! It will be an exciting event in a great setting bringing together domain scientists and AI researchers! With @ylecun @randall_balestr @cosmo

Congratulations to Tristan Buckmaster and Levent Alpöge for developing the AI assisted techniques for solving an important math problem. Congratulations to OpenAI for not letting any good customer transcripts go unmined.

I am excited to share that I am joining @amilabs as Director of Research, Paris, working with @ylecun and an exceptional founding team. Further progress in machine intelligence will require not only scaling foundation models and large-scale engineering, but also new ideas and breakthroughs. This is what makes AmiLabs such a unique and exciting place to build. Its focus on world modeling — systems that can learn richer representations of the real world, reason, plan, and learn from interaction — is a long-term research direction I am deeply excited about. I am particularly looking forward to working with extraordinary co-founders @sainingxie, @lxbrun, @michaelrabbat, @pascalefung, @mavenlin, @laurentsolly, and the amazingly talented AmiLabs team, to helping build the research organization and to the journey ahead.