We shared⁠(opens in a new window) our proof attempts on Saturday, February 14, 2026 at 12:00 AM PT. Based on feedback from experts, we believe at least five of the model’s proof attempts (problems 4, 5, 6, 9, and 10) have a high chance of being correct, and several others remain under review. We initially believed our attempt for problem 2 was likely correct. Based on the official First Proof commentary and further community analysis, we now believe it is incorrect. We’re grateful for the engagement and look forward to continued review. Our full set of proof attempts can be found here⁠(opens in a new window). The preprint includes all ten proof attempts, plus a newly added appendix with prompt patterns and examples that aim to simulate our manual interactions with the models during the process.

We believe novel frontier research is perhaps the most important way to evaluate capabilities of next generation AI models. Benchmarks are useful, but they can miss some of the hardest parts of research: sustaining long chains of reasoning, choosing the right abstractions, handling ambiguity in problem statements, and producing arguments that survive expert scrutiny. Frontier challenges like First Proof help us stress-test those capabilities in settings where correctness is nontrivial to verify and the failure modes are informative.

“We’re currently training a new model for which a primary focus is increasing the level of rigor in its thinking, with the goal that the model can think continuously for many hours and remain highly confident in its conclusions. When the First Proof problems were announced, it seemed like the perfect testbed, so over the weekend I tried it out. Already it was able to solve two of the problems (#9 and #10). As it trained, it became increasingly capable, eventually solving–in our estimation–at least three more. We were particularly pleased when it solved #6 and then, two days later, #4, as those problems were from fields familiar to many of us. It’s pretty incredible to watch a model get tangibly smarter day by day.”

We ran the model with limited human supervision. When prompting versions of the model along training, we sometimes suggested retrying strategies that appeared fruitful in earlier attempts. For some attempts, we asked the model to expand or clarify parts of a proof after receiving expert feedback, to make the reasoning easier to verify. We also facilitated a back-and-forth between this model and ChatGPT for verification, formatting, and style. For some problems, we present the best of a few attempts, selected by human judgment. This was a fast sprint, and our process was not as clean as we would like in a properly controlled evaluation. We look forward to discussions with the First Proof organizers about a more rigorous experiment and evaluation framework for future iterations.