Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection
An automated approach that uses Large Language Models in an agentic check--fix--verify loop to synthesize executable Python scripts that act as interpretable, problem-specific feature extractors that consistently outperform both expert-curated mzn2feat features and the best transformer-based trans2feat variants.