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OpenAI says its models disproved the Exponential Time Hypothesis, among three new results it handed to outside experts

If the computer-science claim holds, hundreds of papers built on that assumption are affected; Engadget explains the three results and how OpenAI is releasing them.

OpenAI announced three results it says its models produced, in harmonic analysis, complexity theory and quantum field theory, and released them differently, Engadget reports. Two manuscripts are on GitHub: one proves Sogge's local smoothing conjecture for waves in every spatial dimension, and one claims the Exponential Time Hypothesis (ETH) is false. The preprint describes a deterministic algorithm that counts the satisfying assignments of any k-CNF formula in subexponential time.

ETH, a widely believed assumption that some problems can't be solved quickly, underpins hundreds of papers; if it is false, better algorithms could exist in fields from databases to computational biology. OpenAI asked outside experts, including the scientists who proposed ETH, to examine and explain the computer-science result; the physics result (on interacting quantum field theories in four dimensions) has been left to a group of physicists that has not yet published. OpenAI says it won't seek academic credit. TechCrunch reports that Anthropic released its own refutation of ETH hours later, after Anthropic mathematician Levent Alpöge suggested OpenAI's solution drew on pre-existing work. The claims have not been peer reviewed; MIT's Ryan Williams, organizing the field's response with ETH's proposers, told TechCrunch parts of the proof look "alien."

Hacker News commenters said the headline "buries the lede," asked whether this shifts the odds on P versus NP, and one questioned the size of OpenAI's donation to academia.

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What hidden risk could pull against this, even if the news is good?

MIT's Ryan Williams says parts of OpenAI's proof are routine but "two or three alien-looking parts" need the whole community to work through them, and Anthropic's Levent Alpöge suggested the solution drew on pre-existing work. A model's output doesn't come with an account of where its ideas came from, so people have to reconstruct both whether a proof is right and how new it is. Until that checking is done, these are claims, not results.

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