OpenAI released a manuscript titled “The Exponential Time Hypothesis is False,” reportedly generated by an unreleased internal model. Lean, a proof-checking assistant, has verified a version of its main theorem, while an independent group led by ETH’s original proposers Russell Impagliazzo and Ramamohan Paturi, alongside Ryan Williams, reviews the work. Their participation is not an endorsement, so the claim remains unconfirmed. The explainer introduces the issue through 3-SAT: given Boolean variables and rules involving up to three variables, determine whether any assignment satisfies all the rules. Checking an assignment is easy, but exhaustive search takes exponentially many trials; the best known algorithms improve the growth rate without eliminating exponential growth.
Proposed in 1999, ETH asserts that no algorithm can solve 3-SAT in subexponential time - roughly, that the hardest instances require work growing at least as 2 to a fixed positive fraction of the number of variables. It is stronger than P ≠ NP: a subexponential algorithm would refute ETH but would not necessarily make 3-SAT polynomial-time solvable or establish P = NP. ETH matters because reductions connect 3-SAT to problems such as graph colouring, clique, and Hamiltonian cycle, allowing researchers to derive conditional lower bounds for their algorithms. If ETH fails, many such bounds may need reconsideration, but the excerpt provides no details of OpenAI’s proof or its eventual review verdict.
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