Mathematical exploration and discovery at scale
Bogdan Georgiev, Javier G\'omez-Serrano, Terence Tao, Adam Zsolt Wagner

TL;DR
AlphaEvolve leverages large language models and evolutionary algorithms to autonomously discover, improve, and generalize mathematical solutions across various fields, demonstrating significant potential for advancing mathematical research.
Contribution
The paper introduces AlphaEvolve, a novel AI system that combines LLMs with evolutionary search to autonomously discover and refine mathematical solutions at scale.
Findings
Successfully rediscovered known solutions in most problems
Discovered improved solutions for several problems
Generalized finite solutions into formulas valid for all inputs
Abstract
AlphaEvolve (Novikov et al., 2025) is a generic evolutionary coding agent that combines the generative capabilities of LLMs with automated evaluation in an iterative evolutionary framework that proposes, tests, and refines algorithmic solutions to challenging scientific and practical problems. In this paper we showcase AlphaEvolve as a tool for autonomously discovering novel mathematical constructions and advancing our understanding of long-standing open problems. To demonstrate its breadth, we considered a list of 67 problems spanning mathematical analysis, combinatorics, geometry, and number theory. The system rediscovered the best known solutions in most of the cases and discovered improved solutions in several. In some instances, AlphaEvolve is also able to generalize results for a finite number of input values into a formula valid for all input values. Furthermore, we are able to…
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Artificial Intelligence in Games · Mathematics, Computing, and Information Processing
