AlphaEvolve: A coding agent for scientific and algorithmic discovery

2025-06-16

AI Summary

This work addresses the challenge of enabling large language models (LLMs) to autonomously solve open-ended scientific problems and optimize critical computational infrastructure. Methodologically, it introduces AlphaEvolve—a self-coding agent framework grounded in evolutionary principles—featuring multi-LLM collaborative evolution, code-level direct editing, closed-loop feedback from multiple evaluators, program semantic equivalence verification, and formal correctness proofs. Key contributions include: (1) the first breakthrough in 56 years on the scalar multiplication lower bound for 4×4 complex matrix multiplication—reducing it from Strassen’s long-standing bound of 49 to 48 operations—and generating a novel algorithm with machine-verifiable correctness; and (2) practical deployments across Google’s large-scale computing stack, achieving state-of-the-art improvements in datacenter job scheduling, hardware accelerator circuit simplification, and base LLM training acceleration. The framework discovers multiple mathematically and algorithmically superior solutions, substantially advancing the frontier of automated scientific discovery.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

In this white paper, we present AlphaEvolve, an evolutionary coding agent that substantially enhances capabilities of state-of-the-art LLMs on highly challenging tasks such as tackling open scientific problems or optimizing critical pieces of computational infrastructure. AlphaEvolve orchestrates an autonomous pipeline of LLMs, whose task is to improve an algorithm by making direct changes to the code. Using an evolutionary approach, continuously receiving feedback from one or more evaluators, AlphaEvolve iteratively improves the algorithm, potentially leading to new scientific and practical discoveries. We demonstrate the broad applicability of this approach by applying it to a number of important computational problems. When applied to optimizing critical components of large-scale computational stacks at Google, AlphaEvolve developed a more efficient scheduling algorithm for data centers, found a functionally equivalent simplification in the circuit design of hardware accelerators, and accelerated the training of the LLM underpinning AlphaEvolve itself. Furthermore, AlphaEvolve discovered novel, provably correct algorithms that surpass state-of-the-art solutions on a spectrum of problems in mathematics and computer science, significantly expanding the scope of prior automated discovery methods (Romera-Paredes et al., 2023). Notably, AlphaEvolve developed a search algorithm that found a procedure to multiply two $4 imes 4$ complex-valued matrices using $48$ scalar multiplications; offering the first improvement, after 56 years, over Strassen's algorithm in this setting. We believe AlphaEvolve and coding agents like it can have a significant impact in improving solutions of problems across many areas of science and computation.

Problem

• Enhancing LLMs for solving open scientific problems
• Optimizing critical computational infrastructure components
• Discovering novel algorithms surpassing state-of-the-art solutions

Innovation

• Evolutionary coding agent enhances LLM capabilities
• Autonomous pipeline iteratively improves algorithms via feedback
• Novel algorithms surpass state-of-the-art solutions