It is argued that confidence should combine formal checking, human reconstruction, independent mathematical use, and a public record supporting correction of both proofs and reviews, to combine formal checking, human reconstruction, and a public record supporting correction of both proofs and reviews.
Abstract
We assess 18 chapter-specific reviews of the ten mathematical results announced by OpenAI on 1 August 2026, alongside review standards, Lean formalizations, subsequent research, and mathematical references. The article audits this review record without claiming a complete reconstruction of all ten proofs. No confirmed substantive mathematical error in a principal result remains in the examined assessments, although review depth varies and some dependencies remain partly checked. Chapter 8 presents the strongest reservation: a specialist review requests major revision of compressed analytic arguments. In Chapter 6, an apparent polarity error was withdrawn after an overbar lost during PDF extraction was recovered from the typeset source. Subsequent research independently reuses the Chapter 3 proof mechanism and confirms that Connes's rigidity conjecture is false, without independently reproducing Chapter 4's stronger infinite-family result. Among the cited follow-ups, Chapter 7 receives the strongest direct theorem-level corroboration through a stronger hardness theorem. Related equality results in Chapter 8 do not verify the analytic inequality proof. Some follow-ups disclose material AI assistance. We argue that confidence should combine formal checking, human reconstruction, independent mathematical use, and a public record supporting correction of both proofs and reviews.
It is argued that machine checking produces verification abundance while leaving adjudication scarce, and proposes a six-category taxonomy of representational mismatch, a disclosure schema for machine-generated mathematical claims, and implications for software, cryptography, and regulated decision systems.
We audit the exact-arithmetic certificate in arXiv:2310.19781v2, its 2025 \emph{Communications in Mathematical Physics} version, and their shared MATLAB archive. CMP corrects none of the audited items; we therefore treat both versions together and use the openly accessible arXiv source for exact provenance. We found 11...
ProofJudge is introduced, an agentic LLM-as-judge system that scores formal proof quality along five dimensions beyond correctness: library leverage, automation fit, structural clarity, statement quality, and Mathlib conventions.
This work completed a machine-checked Lean 4 proof of the quantum soundness of the classical low individual-degree test, a core theorem underlying MIP* = RE, and provides a verified foundation for quantum complexity.
Si-Rui Lu, Rui-Xuan Deng, David Zhu et al.· 1 citation
This work introduces MathAdv, a diagnostic benchmark spanning 13 domains across undergraduate- and graduate-level mathematics, and shows how component-wise evaluation can reveal model capabilities and failure modes that aggregate theorem-proving accuracy obscures.
Jiajie Yuan, C. Lockhart, Xiao-Yun Liu et al.· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
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