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Alphaevolve A Gemini Powered Coding Agent For Designing Advanced Algorithms
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This DeepMind white paper is relevant to AI safety discussions around increasingly autonomous AI research agents, recursive self-improvement (the system accelerated its own training), and the pace of capabilities advancement driven by LLM-based automated discovery systems.
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Summary
AlphaEvolve is an evolutionary coding agent from Google DeepMind that combines LLMs with evolutionary computation to automatically discover and optimize algorithms. It achieved notable results including improving Strassen's matrix multiplication algorithm for the first time in 56 years and optimizing Google's data center scheduling and hardware accelerator designs. The system represents a significant advance in automated scientific and algorithmic discovery.
Key Points
- •Uses an evolutionary pipeline of LLMs to iteratively improve algorithms via code changes, with automatic evaluators providing grounding feedback to avoid LLM hallucinations.
- •Achieved the first improvement over Strassen's matrix multiplication algorithm in 56 years, multiplying 4x4 complex matrices with 48 instead of 49 scalar multiplications.
- •Applied successfully to optimize Google's production systems: data center scheduling, hardware accelerator circuit design, and LLM training speed.
- •Demonstrates broad applicability across mathematics, computer science, and engineering problems where candidates can be automatically evaluated.
- •Represents an important capability jump in AI-assisted scientific discovery, raising questions about recursive self-improvement and autonomous research agents.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Self-Improvement and Recursive Enhancement | Capability | 69.0 |
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# AlphaEvolve: A coding agent for scientific and algorithmic discovery
Alexander Novikov\*, Ngân Vu˜\*, Marvin Eisenberger\*, Emilien Dupont\*, Po-Sen Huang\*, Adam Zsolt Wagner\*, Sergey Shirobokov\*, Borislav Kozlovskii\*, Francisco J. R. Ruiz, Abbas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushmeet Kohli and Matej Balog\* Google DeepMind1
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 $4 \\times 4$ 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.
# 1\. Introduction
Discovering new high-value knowledge, such as making a novel scientific discovery or developing a commercially valuable algorithm, generally requires a prolonged process of ideation, exploration, backtracking on unpromising hypotheses, experimentation, and validation. There has been much recent interest in using large language models (LLMs) to automate significant parts of this process. Hopes of success here are driven by the breathtaking power of recent LLMs \[32, 76\], which can enhance their capabilities using test-time compute, and the rise of agents that combine language generation and action \[88, 114\]. These advances have improved performance across a range of established benchmarks and accelerated discoveryoriented tasks like hypothesis generation \[34\] and experiment design \[7, 43\]. Ho
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