Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection
Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing feature extractors demands deep domain expertise and quickly becomes a bottleneck when new problem classes appear. We present an automated approach that uses Large Language Models (LLMs) in an agentic check--fix--verify loop to synthesize executable Python scripts that act as interpretable, problem-specific feature extractors. Given a high-level MiniZinc model and an instance, the LLM agent generates code that constructs a typed graph representation and computes structural properties such as graph density, variable clustering, and constraint tightness. W...
| Authors | Hai Xia, Carlos Ansótegui, Stefan Szeider |
|---|---|
| Primary category | cs.AI |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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