OpenAI claims AI solved an 80-year-old math problem with complex reasoning breakthrough

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OpenAI announced its new reasoning model has produced an original mathematical proof disproving a famous geometry problem posed by Paul Erdős in 1946. This comes after the company faced criticism seven months ago when GPT-5 allegedly solved Erdős problems but had only rediscovered existing solutions. This time, OpenAI backed its claim with support from respected mathematicians.

OpenAI Announces Breakthrough in Mathematical Problem-Solving

OpenAI has announced that its latest AI reasoning models successfully disproved an unsolved mathematical conjecture that has puzzled researchers since 1946. The 80-year-old math problem, originally posed by legendary mathematician Paul Erdős, centered on a geometry problem where mathematicians believed the best solutions resembled square grid patterns

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. The company's general-purpose reasoning model discovered an entirely new family of constructions that performs better, marking what OpenAI calls "the first time AI has autonomously solved a prominent open problem central to a field of mathematics"

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Source: Digit

Source: Digit

This achievement demonstrates a significant leap in complex reasoning capabilities, as the AI solves math problem through autonomous discovery rather than pattern matching from existing literature. The proof came from a model not specifically designed to tackle mathematical challenges, suggesting broader implications for how AI systems handle difficult chains of reasoning across multiple scientific fields

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Learning from Past Mistakes

The announcement carries extra weight given OpenAI's previous misstep in this arena. Seven months ago, former OpenAI VP Kevin Weil posted on X that GPT-5 had found solutions to 10 previously unsolved Erdős problems and made progress on 11 others. However, it turned out GPT-5 didn't actually solve those problems—it merely rediscovered existing solutions already present in mathematical literature

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That premature claim drew sharp criticism from AI luminaries including Yann LeCun and Google DeepMind CEO Demis Hassabis, prompting Weil to delete his post. Thomas Bloom, who maintains the Erdos Problems website, called Weil's earlier post "a dramatic misrepresentation"

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. This time, OpenAI appears to have taken a more cautious approach before going public.

Mathematicians Validate AI's Original Proof

To support its claim, OpenAI published companion remarks from respected mathematicians including Noga Alon, Melanie Wood, and Thomas Bloom

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. Their endorsements lend credibility to what could represent a watershed moment for AI in mathematics. Bloom, who previously criticized OpenAI's earlier claims, now offers a more optimistic perspective: "AI is helping us to more fully explore the cathedral of mathematics we have built over the centuries. What other unseen wonders are waiting in the wings?"

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The geometry problem disproved a long-held belief that had persisted for nearly 80 years among mathematicians. By discovering constructions that outperform the traditional square grid approach, the AI demonstrated an ability to think beyond established patterns and explore novel solution spaces that human researchers may not have previously considered

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Source: TechCrunch

Source: TechCrunch

Implications Beyond Mathematics

OpenAI emphasizes that this breakthrough matters because it shows AI systems can now hold together long, difficult chains of reasoning and connect ideas across fields in ways researchers may not have explored. The company suggests this capability extends far beyond mathematics, with potential applications in biology, physics, engineering, and medicine

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. If AI can autonomously discover solutions to problems that have stumped experts for decades, it could accelerate scientific discovery across multiple domains.

The use of a general-purpose reasoning model rather than a specialized mathematical system is particularly noteworthy. This suggests the underlying reasoning capabilities are transferable, potentially enabling breakthroughs in other scientific fields where complex problem-solving and novel approaches are needed. For researchers watching this space, the key question becomes: what other long-standing problems might yield to AI's ability to explore solution spaces without human preconceptions?

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