Multi-agent LLM systems can achieve better long-horizon optimization by combining cross-branch information sharing, persistent memory of past solutions, and adaptive planning strategies—enabling faster algorithm discovery with less compute.
MLEvolve is an AI system that uses multiple language model agents working together to automatically discover and improve machine learning algorithms. It combines tree search with memory systems to learn from past attempts, enabling it to solve complex optimization tasks more efficiently than existing approaches.