Machine learning and evolutionary optimization can solve previously intractable mathematical optimization problems, enabling incremental progress on long-standing theoretical computer science questions.
Researchers improved the best-known upper bound on the matrix multiplication exponent (a fundamental constant in computer science) from 2.371339 to 2.371177 by reformulating the underlying optimization problem, applying modern machine learning techniques, and using an evolutionary algorithm called AlphaEvolve to refine the solution.