The article audits 18 chapter‑specific reviews of ten mathematical results announced by OpenAI, examining review standards, Lean formalizations, subsequent research, and references. No confirmed substantive errors remain in the principal results, though review depth varies and some dependencies are only partially checked. Chapter 8 raises the strongest reservation, requiring major revision of compressed analytic arguments, while Chapter 6’s polarity error was corrected after a PDF extraction issue.
By Miko{\l}aj Sienicki, Krzysztof Sienicki
arXiv:2610.17090v1 Announce Type: cross
Abstract: On 6 October 2026 OpenAI published an initial 722 AI-written mathematical manuscripts, organized into 372 problem families, in the public repository...
By Ying Xie
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
arXiv:2611.06962v1 Announce Type: new
Abstract: Proof assistants such as Lean check mathematical proofs down to the axioms, and AI agents have recently made writing such proofs cheap. We describe Aut...
By Christian Clason, Tapio Helin, Medet Nursultanov, Lauri Oksanen
arXiv:2606. 29687v1 Announce Type: cross Abstract: We report a machine-verified resolution of a problem open for over a decade in quantum optimization: the Farhi, Goldstone and Gutmann (FGG) conjecture that depth-$p$ Quantum Approximate Optimization Algorithm (QAOA) on the ring of disagrees attains approximation ratio $(2p+1)/(2p+2)$ exactly.
By Uri Kol, Maor Ben-Shahar, Kfir Sulimany, Dirk Englund
arXiv:2608.29401v1 Announce Type: cross
Abstract: As several mathematical conjectures have recently been settled using large language models (LLMs), the mathematical community has formulated norms an...
By Axel Boldt
FormalFlow is a system that coordinates AI proving agents under human supervision to tackle long‑horizon formalizations, using a shared blueprint for nested planning, proving, and review loops. The team used it to produce a machine‑checked Lean 4 proof of the quantum soundness of the classical low‑individual‑degree test, a core theorem underlying MIP* = RE, in 63 days. The resulting library contains 126,367 lines of Lean code, all generated by agents, and corrects side conditions while preserving the published error bound under corrected assumptions.
By Sirui Lu, Ruixuan Deng, Yanqiao Zhu, Zhengfeng Ji
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:2606. 02484v1 Announce Type: new Abstract: Recent advances in large language models and agentic AI systems have enabled significant progress in mathematical discovery, from solving competition problems to tackling research-level conjectures.
By Leheng Chen, Zihao Liu, Wanyi He, Bin Dong
The Ramanujan Challenge benchmark tests AI on research‑level number theory problems, including unpublished results and open conjectures. Analysis of submissions shows that AI models, CAS, and formal tools can produce correct solutions that differ conceptually from official ones, even proving a published open conjecture. To reliably judge these complex proofs at scale, the authors propose an AI‑assisted evaluation pipeline that reduces false positives in natural language proofs from 23% to 6% and improves code judgment accuracy from 46% to 58%.
Bolzano is a multi‑agent, open‑source system that employs parallel prover agents and a verifier agent to conduct proof search while maintaining a human‑readable research state. In initial manual trials on expert‑selected problems, it produced eight proofs verified by domain experts. When deployed without problem‑specific guidance on roughly 3,800 open problems from four paper sets, Bolzano solved about 200 of them, including four questions from papers accepted to the STOC 2026 conference, which were confirmed by the authors.
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}
The growing use of AI in mathematics raises a question: can we detect AI involvement in proofs, separately from AI assistance with writing? Historical arXiv papers, recent papers disclosing substantiv...