OpenAI Publishes 722 AI-Generated Math Manuscripts, With Verification Still Uneven

OpenAI’s internal mathematics model now has a public body of work to inspect: 722 manuscripts, grouped into 372 related families. Published on October 6, 2026, the GitHub collection pairs papers with supporting proof artifacts. But its contents are at different stages of verification, and the model that produced most of them remains unreleased.

Papers and computer-checkable proofs

Many manuscripts have accompanying formalizations in Lean, a programming language that lets computers check mathematical proofs. Those files provide a different way to examine an argument than reading the paper alone. OpenAI says it will add more formalizations as it obtains them; not every manuscript currently has one.

The repository explicitly warns that some unformalized results could have issues and says OpenAI will endeavor to fix them quickly. The release therefore comes with a verification distinction: it offers manuscripts and, for many, formal proof artifacts, rather than presenting the entire collection as equally checked.

Nor does each paper represent a separate problem solved. A family can collect a principal result, companion arguments, consequences or alternative proofs. Families are classified by mathematical discipline, giving readers a subject-level route into the catalogue before they examine individual manuscripts.

722Manuscripts

Individual papers in the current catalogue.

372Families

Groups of related papers, including companion arguments and alternative proofs.

For readers looking beyond the headline count, the repository supplies several entry points into the papers and their supporting materials:

• The overview describes the families; the manuscript map points to individual papers and supporting materials.
• The preprints directory contains PDFs, source files, and manuscript-specific citation and build instructions.
• The Lean library and formalization catalogue identify available formal proofs, associated papers and verification configurations.

An evaluation moved into open research problems

OpenAI says it expanded its evaluations on open research problems after performance on existing mathematics evaluations saturated. Over the evaluation, the model received approximately 4,000 problems. Outputs were grouped into families and manuscripts, with an appropriate level of significance required for inclusion in the catalogue.

The vast majority of results followed the same procedure using the unreleased internal model. OpenAI estimates that the average result consumed compute equivalent to roughly three hours of ChatGPT Pro thinking. That is the company’s comparison for computing effort, not an announcement that ChatGPT Pro users can access this model.

Alongside the papers, OpenAI released 10 abridged summaries of the model’s reasoning. The selected subjects range from the irrationality exponent of π to a three-dimensional relativistic Vlasov–Maxwell system. These are summaries of selected results, not reasoning accounts accompanying all 722 manuscripts.

A publication record built to retain corrections

Corrections and revisions will appear as new versions, while earlier releases remain accessible. Each manuscript directory supplies a BibTeX citation block. That approach preserves a record of what was published even as papers or supporting artifacts change.

In its release announcement, OpenAI says it consulted the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study. The company drew on the group’s advice and public recommendations and is exploring community-hosted alternatives for the collection.

OpenAI also promises improvements to citations, mathematical exposition and presentation in future releases. It plans to fund workshops, conferences and special programs to help researchers understand major AI-produced results. Separately, it says it is working toward responsibly releasing the model itself; this publication does not launch it.