Sang Hyun Kim

This is a remarkable milestone in which our agent can work on a research problem for a very long time, then come back and tell us if it has succeeded or failed! We visualize the inference cost Aletheia decided to spend on each candidate solution (as a multiple of the inference cost of for solving Erdős-1051, see our previous work teapawt.net/lmthang/status/2018354…). P7 is extremely interesting. It has been an open problem for several years, and nobody else came close to solving it in the FirstProof contest per @tonylfeng. We initially thought Aletheia had no chance; turned out it was right! Aletheia spent most compute on P7, 16x amount we used for Erdős-1051. Remarkably, per @kimshmath, "This was the first case that I have ever seen that an AI applies several deep mathematical results (by Cartan/Leray/Borel/Atiyah/Quillen/Novikov/Kasparov...) flawlessly. It is a very unique instance."

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. 👇

Yes, we provided 3 things for AI-assisted math: * Human-AI interaction (HAI) card (photo), inspired by model cards * Full transcripts github.com/google-deepmind/s… * A label for novelty-autonomy, inspired by SAE Levels of autonomy, see #Aletheia paper arxiv.org/abs/2602.10177

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.

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.