🧮OpenAI Models Crack Three Long-Standing Math Problems, Challenge Exponential Time Hypothesis

Some decades-old complexity papers may need rewriting

OpenAI just published solutions to three math problems that researchers have been stuck on for years. One of them knocks out the Exponential Time Hypothesis, or ETH, a core assumption in computer science that defines what's hard to compute. If it holds up, that's a big deal. Why you should care: ETH says the fastest algorithms for a certain class of tricky logic puzzles need exponentially more time as variables grow. OpenAI's models produced algorithms that solve those puzzles in sub-exponential time. Anyone who has cited ETH in a paper, and that's a lot of papers over the past decade, now has homework. The catch? Faster algorithms here could also threaten cryptographic systems that depend on certain key sizes. OpenAI says its claims don't break any crypto, which is a relief. The release process is the messy part. Mathematicians got the work dropped on GitHub. Computer scientists formed a working group and asked for public proofs plus OpenAI funding for a conference. Physicists are still deciding how their result gets published. Spoiler: it's not that simple, and this is a live experiment in how AI-driven discovery gets shared.