OpenAI’s New Reasoning Model Challenges an 80-Year-Old Conjecture in Geometry
An anonymous reader cites a TechCrunch report revealing a groundbreaking claim by OpenAI. The AI research company asserts that its latest reasoning model has successfully produced an original mathematical proof that disproves a well-known unsolved conjecture in geometry. This conjecture, first introduced by the renowned mathematician Paul Erdos in 1946, has puzzled mathematicians for decades.
OpenAI’s Previous Claims and Missteps
If this declaration sounds familiar, it might be due to OpenAI’s earlier bold claims. Seven months ago, Kevin Weil, the former vice president of OpenAI, tweeted about GPT-5’s achievements. He claimed that the model had found solutions to 10 previously unsolved Erdos problems and made progress on 11 others. However, it later emerged that GPT-5 had merely rediscovered solutions already existing in the mathematical literature. This led to criticism from notable figures in the field, such as Yann LeCun and Google DeepMind CEO Demis Hassabis, prompting Weil to delete his post.
A More Thorough Approach This Time
Unlike the previous incident, OpenAI seems to have taken a more cautious and thorough approach this time. Alongside the announcement, the company released additional remarks in a PDF, supported by endorsements from respected mathematicians such as Noga Alon, Melanie Wood, and Thomas Bloom. Bloom, the operator of the Erdos Problems website, had previously referred to Weil’s post as a “dramatic misrepresentation,” highlighting the importance of credibility and accuracy in such claims.
Significance of the New Model
The proof, according to OpenAI, was generated by a new general-purpose reasoning model. This model was not specifically designed to tackle math problems or even this particular problem, which underscores its versatility. According to OpenAI, the ability of AI systems to maintain lengthy and complex chains of reasoning and to connect ideas across different fields is a significant milestone. This capability could have far-reaching implications across various domains, including biology, physics, engineering, and medicine.
As AI continues to advance, the potential for these systems to aid in solving longstanding scientific and mathematical challenges grows. The integration of AI into research and problem-solving may lead to unprecedented discoveries and innovations.
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