AlphaEvolve

Large language models (LLMs) are remarkably versatile. They can summarize documents, generate code or even brainstorm new ideas. And now we’ve expanded these capabilities to target fundamental and highly complex problems in mathematics and modern computing. Today, we’re announcing AlphaEvolve , an evolutionary coding agent powered by large language models for general-purpose algorithm discovery and optimization. AlphaEvolve pairs the creative problem-solving capabilities of our Gemini models with automated evaluators that verify answers, and uses an evolutionary framework to improve upon the most promising ideas. AlphaEvolve enhanced the efficiency of Google's data centers, chip design and AI training processes — including training the large language models underlying AlphaEvolve itself. It has also helped design faster matrix multiplication algorithms and find new solutions to open mathematical problems, showing incredible promise for application across many areas. In 2023, we showed for the first time that large language models can generate functions written in computer code to help discover new and provably correct knowledge on an open scientific problem. AlphaEvolve is an agent that can go beyond single function discovery to evolve entire codebases and develop much more complex algorithms. AlphaEvolve leverages an ensemble of state-of-the-art large language models: our fastest and most efficient model, Gemini Flash , maximizes the breadth of ideas explored, while our most powerful model, Gemini Pro , provides critical depth with insightful suggestions. Together, these models propose computer programs that implement algorithmic solutions as code. Diagram showing how the prompt sampler first assembles a prompt for the language models, which then generate new programs. These programs are evaluated by evaluators and stored in the programs database. This database implements an evolutionary algorithm that determines which programs will be used for future prompts. AlphaEvolve verifies, runs and scores the proposed programs using automated evaluation metrics. These metrics provide an objective, quantifiable assessment of each solution’s accuracy and quality. This makes AlphaEvolve particularly helpful in a broad range of domains where progress can be clearly and systematically measured, like in math and computer science. Over the past year, we’ve deployed algorithms discovered by AlphaEvolve across Google’s computing ecosystem, including our data centers, hardware and software. The impact of each of these improvements is multiplied across our AI and computing infrastructure to build a more powerful and sustainable digital ecosystem for all our users. Diagram showing how AlphaEvolve helps Google deliver a more efficient digital ecosystem, from data center scheduling and hardware design to AI model training. AlphaEvolve discovered a simple yet remarkably effective heuristic to help Borg orchestrate Google's vast data centers more efficiently. This solution, now in production for over a year, continuously recovers, on average, 0.7% of Google’s worldwide compute resources. This sustained efficiency gain means that at any given moment, more tasks can be completed on the same computational footprint. AlphaEvolve's solution not only leads to strong performance but also offers significant operational advantages of human-readable code: interpretability, debuggability, predictability and ease of deployment. AlphaEvolve proposed a Verilog rewrite that removed unnecessary bits in a key, highly optimized arithmetic circuit for matrix multiplication. Crucially, the proposal must pass robust verification methods to confirm that the modified circuit maintains functional correctness. This proposal was integrated into an upcoming Tensor Processing Unit (TPU), Google’s custom AI accelerator. By suggesting modifications in the standard language of chip designers, AlphaEvolve promotes a collaborative approach between AI and hardware engineer