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Google DeepMind's AlphaEvolve agent improves algorithms and open maths bounds

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AlphaEvolve pairs Gemini models with automated evaluators to evolve code for problems whose answers can be machine-checked. DeepMind reported a 48-multiplication method for 4x4 complex matrices, a new kissing-number lower bound in 11 dimensions, better-than-known results on about 20% of 50+ open problems, and recovery of 0.7% of Google's fleet-wide compute via a scheduling heuristic.

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It showed LLM-driven search producing verifiable new results in mathematics and engineering, a template later used against Erdős problems.

SourceGoogle DeepMind Checked against the primary source. Independently fact-checked on 7 Oct 2026.
Google DeepMind

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