The loop found a better matrix multiply

AlphaEvolve is not a chatbot with good ideas. It is a loop. Gemini proposes programs. An evaluator scores them. An evolutionary database keeps the winners and feeds them back into the next prompt. Flash explores. Pro deepens. The grader is the governor.

The loop found a 4×4 complex matrix multiply that uses 48 scalar multiplications, beating Strassen's 49 from 1969. It also produced a Borg scheduling heuristic now recovering about 0.7% of Google's fleet, a Verilog tweak headed for a TPU, and a 23% speedup on a Gemini matmul kernel. On fifty-plus open math problems it rediscovered the state of the art most of the time and beat it on about 20%.

This is TextGrad's cousin with a compiler in the loss. If you can score a candidate automatically, you do not need the model to be right on the first try. You need a fence around the search. The algorithm is the artifact. The model is the mutation operator.