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OpenAI released hundreds of AI-generated solutions to hard math problems, but they fall short of the Advisory Group on Mathematics and Artificial Intelligence's standards, with gaps in formalization, chain-of-thought disclosure, and human understanding, as highlighted by a Cambridge and King's College paper on discrepancies between natural language proofs and Lean code.
OpenAI has formed an independent advisory group of elite mathematicians to guide the company on handling mathematical research and AI interactions, aiming to avoid future reputational crises.
The author shares personal reminders for conducting AI research, emphasizing curiosity over publication pressure, insights over benchmarks, and thorough investigation over rushed papers.
The article discusses the requirements for making an uploader-run refusal table in AI models independently reproducible, highlighting the need for detailed replication packets including raw generations, decoding settings, per-prompt labels, and scoring code.