Google DeepMind improves matrix multiplication exponent with AlphaEvolve

Matrix multiplication is one of those foundational operations that silently runs the modern world. Every time a neural network trains, a physics simulation renders, or a search engine ranks pages, matrices are being multiplied somewhere underneath the hood. And for over half a century, mathematicians have been trying to figure out the theoretical speed limit for doing it. Google DeepMind just nudged that limit a little lower. Using AlphaEvolve, an evolutionary coding agent built on top of its Gemini architecture, DeepMind’s research team refined the matrix multiplication exponent, known in the literature simply as omega (or just “w”), from 2.371339 down to 2.371177. The improvement was detailed in an arXiv paper. The difference looks tiny. It is not. Why a decimal shift matters more than it looks To understand why shaving off a few digits in the fourth decimal place of an exponent is a big deal, you need a quick detour into what omega actually represents. When you multiply two n-by-n matrices using the straightforward method you learned in school, the number of operations scales as n cubed, meaning omega equals 3. In 1969, Volker Strassen showed you could do better, proving that om...