Core Insights - The article discusses the limitations of existing large language models in machine learning engineering, particularly in optimizing code and algorithms, despite their ability to generate correct code [1][2] - It introduces AutoMLGen, a new intelligent programming framework that combines general large model inference with domain knowledge to enhance machine learning tasks [3][6] Group 1: AutoMLGen Framework - AutoMLGen features a self-developed Monte Carlo Graph Search (MCGS) that allows for dynamic fusion of branches and nodes, breaking the isolation of traditional Monte Carlo Tree Search (MCTS) [4][13] - The framework consists of three main modules: a domain knowledge base, Monte Carlo Graph Search, and a fine-grained operator library, creating a self-evolving loop from experience guidance to intelligent exploration and solution refinement [10][12] Group 2: Performance Metrics - AutoMLGen achieved a 36.4% average medal rate and an 18.7% gold medal rate on the MLE-Bench leaderboard, using only half the standard computation time (12 hours), showcasing its efficiency and effectiveness [21][22] - In the MLE-Bench-Lite test, AutoMLGen maintained a significant performance advantage over existing methods, demonstrating consistent performance and excellent generalization capabilities [21][23] Group 3: Mechanisms of Improvement - The framework's domain knowledge base allows the intelligent agent to quickly transition from "zero experience" to a more knowledgeable state, enhancing decision-making in model selection and feature processing [11][12] - MCGS promotes continuous evolution of the intelligent agent through mechanisms such as intra-branch evolution, cross-branch reference, and multi-branch aggregation, leading to more efficient and robust search processes [14][16][24] Group 4: Future Prospects - The emergence of AutoMLGen signifies a shift in AI capabilities, enabling autonomous exploration and continuous improvement in complex engineering and algorithm design tasks [31] - The integration of memory and collaboration mechanisms is expected to evolve AutoMLGen into an "AI engineering partner," laying the groundwork for higher levels of intelligence and self-improvement in AI systems [31]