The article reports an independent human audit of a proof from OpenAI’s "Ten Advances in Mathematics and Theoretical Computer Science." An initial review flagged a polarity error in a greedy conditioning lemma, but this was traced to a PDF text‑extraction issue that removed an overbar from a symbol. After correcting the rendering, no mathematical error was found, and the audit underscores that errors in AI‑generated mathematics can stem from document‑processing problems as well as reasoning mistakes.
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:2608.28997v1 Announce Type: new
Abstract: In May 2026 an OpenAI model produced a counterexample to the Erd\H{o}s unit distance conjecture. Five mathematicians published a human-verified version...
By Maher Kallel, Mohamed El Louadi
arXiv:2608. 15432v1 Announce Type: new Abstract: In formal verification, both the autoformalization of statements and automated proof search have been studied extensively.
By Tadd Mao, Tianjun Zhong, Dhruva Arekar, Yuming Feng, One An, Jiani Huang, Xujie Si, Ziyang Li
Automatically constructing well-specified and valuable mathematical conjectures remains a central challenge in AI-assisted mathematical discovery. Many existing open problems and conjectures are often too broad, underspecified, or difficult to connect to plausible proof or refutation strategies.
The paper introduces OpenConjecture, a dataset of mathematical conjectures from arXiv papers since February 2026, and evaluates frontier GPT models on 200 randomly sampled conjectures. The models generate candidate solutions for about 60% of the conjectures, with counterexamples occurring roughly 1.3 times more often than proofs. The study examines solution novelty, citation practices, and fidelity to the original statements, finding that some solutions deviate from authors’ intent and that many proofs closely follow existing results, though some exhibit moderate novelty.
By Helen Jenne, Davis Brown, Jesse He, Henry Kvinge
The Ramanujan Challenge benchmark tests AI on research‑level number theory problems, including unpublished results and open conjectures.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%.
arXiv:2606. 10799v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs.
By Yifeng Sun
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
The paper reports on the Ramanujan Challenge, a benchmark of research‑level number theory problems, and evaluates submissions from AI models, CAS, and formal tools. It highlights that some correct solutions differ conceptually from official ones and even proves a published open conjecture. The authors introduce an AI‑assisted evaluation pipeline that significantly lowers false positives in natural language proofs and improves accuracy for supporting code, offering a scalable method for assessing high‑level mathematical work.
By Alan Sorani, Ashvni Narayanan, Michael Shalyt, Tali Monderer, Rotem Kalisch, Hila Barkan, Elyasheev Leibtag, Shachar Weinbaum, Carsten Schneider, John Campbell, Ido Kaminer
arXiv:2607. 01223v1 Announce Type: new Abstract: When should an AI system's answer be trusted?
By Ben Slivinski, Michael Saldivar
The paper introduces HorizonMath, a benchmark of 113 largely unsolved mathematical problems across eight domains, paired with an open-source framework for automated verification. It focuses on the generator‑verifier gap, targeting problems that are hard to discover but easy to verify computationally, thereby avoiding costly formal proof verification or manual review. Using this framework, the authors found six novel solutions—three each from GPT‑5.4 Pro and GPT‑5.6 Sol—demonstrating that current models can contribute to mathematical research, while most state‑of‑the‑art models score below 10%.
By Erik Y. Wang, Sumeet R. Motwani, James V. Roggeveen, Eliot Hodges, Dulhan Jayalath, Charles London, Kalyan Ramakrishnan, Jakob Foerster, Cheng Zhang, Flaviu Cipcigan, Philip Torr, Alessandro Abate