AlphaEvolve: pointing a model at problems that come with an objective scoring function
Google DeepMind's coding-agent-driven evolutionary search: Gemini Flash proposes candidates in volume and Gemini Pro in quality, automatic evaluators score them, and high scorers stay in the population as next-round context, i.e. evolutionary search with an LLM as the mutation operator; it applies only where an automatic evaluator exists, hence the math and agents filing. An evolved scheduling heuristic has run in production in Google data centres (Borg) for over a year, recovering 0.7% of Google's global compute (Google's compute, not all compute on earth), the card reading and the only result validated by long-running production. An evolved matmul kernel is 23% faster at specific sizes, and about 20% of 50+ open maths problems improved, including 4x4 complex matrix multiplication in 48 multiplications against Strassen's 1969 record of 49. Verification differs from the prover line: a Lean check is mathematical correctness, "23% faster" is an empirical reading on specific hardware. No public weights, Early Access is a waitlist, and the precondition is an evaluator you write yourself. Graded B (contested): a year of production behind the Borg result, nothing reproduced.
