AlphaEvolve Sets New Record on Matrix Multiplication Exponent
Google DeepMind researchers report applying AlphaEvolve to refine the matrix multiplication exponent ω.
Matej Balog posted that the team used AlphaEvolve on an ML pipeline for the time complexity of matrix multiplication and improved the prior best result. Pushmeet Kohli described matrix multiplication as a core operation in computing and called the exact value of ω a long-standing open problem in complexity theory. Abigail See added that the gain came from pairing larger-scale optimization with AlphaEvolve. The posts treat the outcome as a modest advance on ω itself but a milestone for the AI method. Independent replies in the thread repeat the same claim of a new record.
We applied AlphaEvolve's autoresearch powers to an ML pipeline tackling one of the most famous problems in CS: time complexity of matrix multiplication (ω). We improved the SOTA! A small step for ω (similar to recent works), but a nice milestone for AI