Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection

2026-08-17

AI Summary

本文提出一种使用大型语言模型自动生成问题特定特征提取器的方法，以解决手动设计特征提取器的瓶颈问题，提高算法选择性能。

Abstract

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. We evaluate our approach on three combinatorial problems (vehicle routing, car sequencing, fixed-length error-correcting codes) with a portfolio of five state-of-the-art solvers. The synthesized extractors yield algorithm selectors that consistently outperform both expert-curated mzn2feat features (up to $8.3$ percentage points (pp) test-set accuracy on FLECC) and the best transformer-based trans2feat variants. In the meanwhile, the synthesized feature extractors remain inspectable.

Problem

• algorithm selection
• constraint satisfaction problems
• feature extractors
• domain expertise

Innovation

• Large Language Models (LLMs)
• check--fix--verify loop
• feature extractors
• typed graph representation
• algorithm selection