Our IMO journey continues: the yolo run model that we trained a week before #imo2025, despite all possible likelihood of failures, magically achieves SOTA across a wide range of reasoning tasks from maths, to coding, and challenging knowledge. I'm very excited that we have now delivered the IMO 🥇 system to the hands of mathematicians and a simplified version (results below) to all Google AI Ultra subscribers. One very important distinction compared to all other IMO results out there is that our model is production ready, not experimental 🙂 This advanced model together powered by Deep Think mode is quite creative in problem solving. Check out this video of what a mathematician thinks and see the blog post for more details blog.google/products/gemin…

Super thrilled to share our latest work, AlphaGeometry from @GoogleDeepMind , the first AI system ever approaching the IMO gold medalists in solving Olympiad geometry math problems. Published today at Nature, titled “Solving olympiad geometry without human demonstrations”, our work marks an important milestone towards advanced reasoning, which, I believe, is the key prerequisite for AGI.

As someone who was doing Olympiad Maths full time back in high school, I find the results really fascinating. #AlphaGeometry (trained on 100% synthetic data) was able to surpass the previous state-of-the-art by a large margin. It can solve all geometry problems of the years 2000 and 2015, judged correct by humans, equivalent to winning real Bronze medals those years!

A few key ideas of AlphaGeometry. (1) synthetic data generation at scale with 100M theorems and proofs, allowing AlphaGeometry to learn from scratch, without any human demonstrations. (2) a neuro-symbolic architecture that combines a neural language model (System 1, creative) with a symbolic deduction engine (System 2, reliable), allowing AlphaGeometry to reason efficiently and effectively.

Amazing collaborators: @thtrieu_, @Yuhu_ai_, @quocleix, @hhexiy.
Blog:
Paper:
Code: dpmd.ai/alphageometry
nature.com/articles/s4158…
github.com/google-deepmin… This is the neuro-symbolic architecture of #AlphaGeometry. Similar to System1 and System 2, in the book "Thinking, fast and slow", the symbolic engine will first take a crack at the problem mechanically; if it gets stuck it will ask the neural language model for suggestions of new points and lines (the auxiliary constructions that everyone used to do in high school!)