aiqbench analysis
This research demonstrates how agentic Large Language Models can autonomously synthesize interpretable feature extractors, significantly outperforming both expert-designed and transformer-based baselines in algorithm selection for constraint satisfaction problems. By automating the creation of Python scripts that compute structural properties like graph density and variable clustering, this approach removes a major bottleneck requiring deep domain expertise when facing new problem classes. The method maintains inspectability while delivering up to 8.3 percentage points higher accuracy on test sets compared to existing state-of-the-art solutions.
Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection
arXiv:2608.17170v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2608.17170 [cs.AI]
(or arXiv:2608.17170v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.17170 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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