The guidelines were published on Sept. 29, but OpenAI chose to announce proofs for the 722 problems anyway.
OpenAI proceeded with the release format the guidelines warned against (a headline drop generated by a closed model), but structured the drop itself to comply with many of the documentation and formalization standards laid out in those guidelines. https://agmai.org//
About the guidelines:
AI laboratories have begun using powerful artificial intelligence models to solve complex mathematical problems. However, this is creating a dilemma: AI systems can produce advanced proofs that humans do not yet understand, cannot easily verify, or cannot take responsibility for.
Historically, mathematics relies on human understanding, peer review, and open collaboration. To protect this foundation, a group from the mathematical community—backed by feedback from over 600 mathematicians—has issued guidelines on how AI labs should responsibly handle and share mathematical discoveries.
Core Principles.
* Prioritize Human Understanding: The ultimate goal of mathematics is human insight. AI discoveries shouldn’t remain black boxes.
* Lab Responsibility: If an AI lab releases a major math discovery that humans don’t yet understand, the lab is responsible for funding and supporting the effort to help human mathematicians understand it.
* Community Leadership: The math community—not corporate AI labs—must lead and direct the research process.
Key Recommendations for AI Labs.
1. Improve Proof Clarity and Attribution: Before publishing, labs should use models to cite existing literature properly and clean up messy, wordy AI text into clear, standard mathematical language.
2. Avoid Hype and Release Transparently: Labs should deposit results in independent academic repositories rather than using breakthroughs purely as promotional marketing. They should disclose the prompts used, computing time, costs, and how many failed attempts occurred.
3. Formalize Proofs: Where possible, AI-generated proofs should be translated into machine-verifiable code to confirm their accuracy.
4. Fund Educational Efforts: AI labs should fund independent non-profits to host workshops, conferences, and research grants so mathematicians can analyze, digest, and write guides on complex AI outputs.
5. Ensure Broad and Fair Access: Frontiers in mathematics shouldn’t be locked behind closed doors. Labs are urged to give the global mathematical community broad, equitable access to their models to prevent a two-tier system where wealthy tech companies outpace the rest of the world.
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The recommendations below are for labs that have AI mathematical output that is not understood by the people who prompted the AI systems. They are split into two parts. The first part is a set of proposed technical norms for the release of AI-generated mathematics. The second part is a recommendation that AI labs provide support for the additional mathematical activities that are needed for humans to be able to understand and assimilate their AI-generated mathematical output and identify possible applications of it.
Step I: Initial release
1. With the help of LLMs, it is easy to make substantial improvements to the initial written version of an AI-generated result. The following actions should be carried out by the AI labs rather than left to mathematicians afterwards.