MLEvolve: Self-Evolving Multi-Agent LLM Framework for Automated ML Algorithm Discovery

MLEvolve is a self-evolving multi-agent LLM framework for automated machine learning algorithm discovery. It introduces Progressive Monte Carlo Graph Search (MCGS) with cross-branch information flow, Retrospective Memory (cold-start knowledge base plus dynamic task-specific memory), and hierarchical planning that decouples strategy from code generation. On MLE-Bench, it achieves state-of-the-art medal rate within a 12-hour budget — half the standard runtime — and outperforms AlphaEvolve on mathematical algorithm optimization tasks. Open-source code is available on GitHub.