Pair a frontier coding model with an evolutionary loop and a machine-checkable evaluator. Let the loop run long enough. It discovers a 4x4 complex-matrix multiplication procedure that uses 48 scalar multiplications — the first improvement over Strassen in 56 years. It also finds a Google-wide Borg scheduling heuristic that recovers ~0.7% of cluster compute in production. The architecture is boring on purpose. The wins come from the evaluator's rigor.
Large language models can write code. Evolutionary algorithms can search over code. Both have been tried separately for decades; both hit ceilings. The LLM ceiling is confabulation: the model writes plausible code that does not do what it claims. The evolutionary ceiling is search cost: random mutations over syntax rarely produce compilable programs, let alone better ones.
AlphaEvolve (Novikov et al., DeepMind, arXiv:2506.13131, June 2025) combines them. The LLM proposes targeted edits to a program database; an automatic evaluator scores each variant; high-scoring variants become parents for future generations. The LLM handles the expensive step of writing plausible code; the evaluator catches the confabulations. The loop runs for hours to weeks.
Results reported: 48-scalar-multiplication 4x4 complex matrix multiplication (Strassen's 1969 bound was 49), a Borg scheduling heuristic in Google production, a 32.5% FlashAttention kernel speedup, Gemini training throughput improvements.
The architecture works because the evaluator is machine-checkable. It does not work where the evaluator isn't. That asymmetry is the lesson.
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