MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery
- Published
- Source
- arXiv
- Paper number
- 335
- Field
- AI / General
- arXiv ID
- 2606.06473
Key points
- It uses Progressive MCGS to enable information flow across branches, solving the isolation problem of existing tree search.
- Retrospective Memory jointly uses domain knowledge and task experience.
- An adaptive coding mode that separates strategy planning from code generation improves stability in long iterative loops.
- It achieves state-of-the-art mean medal rate and valid submission rate on MLE-Bench with a 12-hour budget, which is half the standard budget.
- It outperforms AlphaEvolve on mathematical algorithm optimization and shows strong cross-domain generalization.
Paper links
External research summaries. These are not HDATF publications or measured product results.