上海AI Lab&华师大：AI智能编程新框架，节省一半时间就能“聪明”地写代码

Core Insights - The article discusses the limitations of existing large language models in machine learning engineering, particularly in tasks like AutoML and Kaggle competitions, where continuous iteration and high-performance tuning are essential [1][2] - AutoMLGen, developed by Shanghai Artificial Intelligence Laboratory and East China Normal University, is introduced as a new intelligent programming framework that integrates general large model reasoning with domain knowledge [1][2] Group 1: AutoMLGen Framework - AutoMLGen is designed to enhance the capabilities of large language models beyond code generation, enabling continuous optimization and experience reuse [4][6] - The framework consists of three main modules: a knowledge base, Monte Carlo Graph Search (MCGS), and a fine-grained operator library, which together create a self-evolving loop from experience guidance to intelligent exploration and solution refinement [6][8] Group 2: Knowledge Base - The knowledge base in AutoMLGen systematizes the experience of skilled machine learning engineers, covering model selection, feature processing, and strategy design [7] - During the task initiation phase, AutoMLGen autonomously decides whether to utilize domain knowledge, effectively alleviating the cold start problem while maintaining the independence of the intelligent agent's decisions [7] Group 3: Monte Carlo Graph Search (MCGS) - MCGS innovatively introduces a graph structure to the search process, allowing for dynamic fusion and sharing of nodes and trajectories across different branches, thus enhancing efficiency in complex tasks [8] - Four core mechanisms drive the continuous evolution of the intelligent agent: main expansion, intra-branch evolution, cross-branch reference, and multi-branch aggregation [8] Group 4: Fine-Grained Operator Library - The fine-grained operator library in AutoMLGen defines the evolution methods between different solutions, facilitating a coherent and efficient optimization process [9] - This mechanism allows the intelligent agent to transition from a code generator to an AI engineer capable of proactive reflection and improvement [9] Group 5: Performance Results - AutoMLGen achieved a 36.4% average medal rate and an 18.7% gold medal rate on the MLE-Bench leaderboard, outperforming existing systems while using only half the standard computation time (12 hours) [12][19] - In the MLE-Bench-Lite tests, AutoMLGen maintained a significant lead, demonstrating consistent performance and excellent generalization capabilities [12] Group 6: Future Prospects - The emergence of AutoMLGen signifies a shift in the capabilities of intelligent agents in complex engineering and algorithm design tasks, showcasing AI's potential for autonomous exploration and continuous improvement [19][20] - The framework's principles are expected to extend to broader intelligent system paradigms, paving the way for future developments in AI that can actively understand, improve, and innovate solutions [20]