Mathematicians' AGMAI publishes norms for AI labs releasing AI-generated results; Simons Institute TCS group issues 12 actions
On Sept 29, 2026 the Advisory Group on Mathematics and Artificial Intelligence (AGMAI; Tao, Gowers, Hairer, Witten and others) published general recommendations for AI companies releasing mathematical results. They ask labs to release significant results promptly, disclose models, prompts, compute and costs, formalize proofs, report failure rates, and fund human efforts to understand the results. A Simons Institute working group separately published "AI and TCS: The Next Six Months" with 12 near-term actions for theoretical computer science.
Key facts
- AGMAI recommendations informed by 600+ responses from the mathematical community
- For results nobody yet understands: cite prior work even if rediscovered; improve exposition; disclose model names, prompts, chain-of-thought summaries, compute time and cost; formalize proofs to community standards; explain AI use and report failure rates on comparable problems
- Labs should fund conferences, expository material and postdocs to build human understanding, and give broad, equitable access to public models
- Simons report (dated Sept 28): from a Sept 9–10 working group (organizers Tony Feng, Zeph Landau, Amit Sahai, Nikhil Srivastava) and a 134-response survey
- Its 12 actions include a standardized 'AI methodology' section in papers, tracing the lineage of AI-generated ideas, arXiv posting and video talks with conference submissions, extra postdocs, training and evaluation without AI assistance, and grants covering reasonable LLM costs
- Terence Tao highlighted both on his blog on Sept 30 ('Two reports')
What happened
AGMAI formed on Sept 21 after OpenAI said an internal model had resolved 100+ open problems and asked mathematicians how to release them. Its first public output is a general code of conduct for any AI company in that position. The Simons report covers the parallel adaptation of theoretical computer science.
Why it matters
These are the first detailed, community-backed norms for how AI-generated mathematics should be disclosed, verified and absorbed. They set expectations labs will be judged against when they release batches of machine-found results.
Changelog
- 2026-09-30: created (evening sweep run, via Tao's blog)
Related events
- OpenAI says an internal model resolved 100+ long-standing open problems in 24 days of training; no list released ★★★
- Caltech 'Mathathon' is reworked into 'Old Problems, New Proofs' after an open letter from mathematicians ★★
- Summer 2026 flood: dozens of named conjectures settled on arXiv with disclosed AI help (July–September catalogue) ★★★★
Sources (4)
- officialAGMAI: General recommendations to AI companies (Sept 29)
- paperSimons Institute: AI and TCS, The Next Six Months (PDF)
- discussionTerence Tao: Two reports
- discussionTerence Tao: Announcing AGMAI (Sept 21)
id: 2026-09-29-agmai-recommendations-ai-math-results · updated 2026-09-30 · open in the interactive timeline