Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection

The paper introduces an automated method that employs Large Language Models in a check–fix–verify loop to generate executable Python scripts for extracting problem-specific features from constraint satisfaction problems. Given a MiniZinc model and instance, the LLM agent produces code that builds a typed graph representation and computes structural properties such as graph density, variable clustering, and constraint tightness. Evaluated on vehicle routing, car sequencing, and fixed‑length error‑correcting codes, the synthesized extractors enable algorithm selectors that outperform expert‑curated mzn2feat features and transformer‑based trans2feat variants, while remaining interpretable.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• agents
• benchmarks