OpenAI announced the model breakthrough index time hypothesis

OpenAI recently announced that its AI model has cracked the Exponential Time Hypothesis, abbreviated as ETH, which has been a longstanding conjecture in computer science for over twenty years, and is not related to Ethereum. This hypothesis focuses on how difficult particularly hard problems are, using the Boolean Satisfiability Problem (SAT) as an example: given a set of logical conditions, is there an assignment that makes all conditions true simultaneously? As the problem size increases, the best-known methods essentially can only try possible answers one by one, with the time cost growing exponentially with the size. The Exponential Time Hypothesis further asserts that such problems cannot be significantly accelerated, and there are no truly sub-exponential time efficient algorithms.

Since its proposal in 1999, this hypothesis has been a crucial cornerstone of theoretical computer science, with numerous papers proving that certain problems are hard and that some algorithms are close to optimal based on it. If the Exponential Time Hypothesis were to be overturned, it would imply that many problems considered nearly impossible to solve faster might theoretically have better solutions. OpenAI claims that its model has found a deterministic algorithm that can accurately count the number of solutions to the k-SAT problem in sub-exponential time, and it has been formally verified using Lean. If this result withstands expert scrutiny, it will directly challenge the Exponential Time Hypothesis.