OpenAI Blog

Sharing AI progress in mathematics

OpenAI has released new results on open problems in mathematics derived from its internal frontier model. The company is sharing Lean proof formalizations and detailed research findings on GitHub. This effort makes advanced mathematical progress publicly accessible and encourages collaboration within the research community.

OpenAI Blog
Sep 21

Advisory Group on Mathematics and Artificial Intelligence

OpenAI is collaborating with an independent Advisory Group on Mathematics and Artificial Intelligence. The group’s role is to guide the review and communication of emerging AI results. This partnership aims to ensure rigorous oversight and clear dissemination of new developments in AI.

arXiv Computation and Language
Sep 23

FrontierMath Erd\H{o}s

arXiv:2609. 25050v1 Announce Type: new Abstract: We introduce FrontierMath Erd\H{o}s (FME), a benchmark of 68 Erd\H{o}s problems that are open as of August 2026.

By Tom Adamczewski (Epoch AI), Thomas F. Bloom (University of Manchester)
arXiv Computation and Language
Oct 8

From Expert-Guided Proof Search to Automated Open-Problem Solving

arXiv:2610.09769v1 Announce Type: cross Abstract: Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental imp...

By Adri\'an Z\'ame\v{c}n\'ik, Mat\v{e}j Kripner, Martin Kouteck\'y, Martin Balko, Jan Greb\'ik, Pavel Hub\'a\v{c}ek, Robert \v{S}\'amal, V\'aclav Rozho\v{n}
arXiv AI
Oct 9

OpenProblemBench: Benchmarking AI on Open Problems in the Foundational Theoretical Sciences

OpenProblemBench is a new benchmark that presents 82 unresolved problems from mathematics and theoretical physics, each with detailed context, assumptions, and prior progress. The benchmark evaluates AI submissions on correctness, completeness, and progress using four independent evaluator models, without providing reference solutions. GPT‑6‑Astra achieved the highest mean judged solve rate of 14.0%, outperforming other evaluated models, and the study links stronger outcomes to improved problem representation, general reasoning, and proof steps.

By Zhiyi Li, Sihan Hu, Tianning Xiao, Xiansheng Cai, Xiaojun Tan, Youjin Deng, Kun Chen
arXiv AI
Jun 16

SorryDB: Can AI Provers Complete Real-World Lean Theorems?

arXiv:2603. 02668v2 Announce Type: replace Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub.

By Austin Letson, Leopoldo Sarra, Auguste Poiroux, Oliver Dressler, Paul Lezeau, Dhyan Aranha, Frederick Pu, Aaron Hill, Miguel Corredera Hidalgo, Julian Berman, George Tsoukalas, Lenny Taelman
arXiv AI
Jun 9

Advancing Mathematics Research with AI-Driven Formal Proof Search

arXiv:2605. 22763v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research.

By George Tsoukalas, Anton Kovsharov, Sergey Shirobokov, Anja Surina, Moritz Firsching, Gergely B\'erczi, Francisco J. R. Ruiz, Arun Suggala, Adam Zsolt Wagner, Eric Wieser, Lei Yu, Aja Huang, Mikl\'os Z. Horv\'ath, Andrew Ferraiuolo, Henryk Michalewski, Edward Lockhart, Codrut Grosu, Thomas Hubert, Matej Balog, Pushmeet Kohli, Swarat Chaudhuri