{"slug": "synthesizing-feature-extractors-an-agentic-approach-for-algorithm-selection", "title": "Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection", "summary": "Researchers at arXiv (2608.17170v1) introduced an automated approach using Large Language Models (LLMs) in an agentic check-fix-verify loop to synthesize executable Python feature extractors for constraint satisfaction problems, outperforming expert-curated features by up to 8.3 percentage points on the FLECC test set. The method, evaluated on vehicle routing, car sequencing, and fixed-length error-correcting codes with five solvers, consistently beat both mzn2feat and transformer-based trans2feat variants while remaining interpretable.", "body_md": "arXiv:2608.17170v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/synthesizing-feature-extractors-an-agentic-approach-for-algorithm-selection", "canonical_source": "https://arxiv.org/abs/2608.17170", "published_at": "2026-08-19 04:00:00+00:00", "updated_at": "2026-08-19 04:14:23.949128+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "machine-learning"], "entities": ["arXiv", "MiniZinc", "mzn2feat", "trans2feat", "FLECC"], "alternates": {"html": "https://wpnews.pro/news/synthesizing-feature-extractors-an-agentic-approach-for-algorithm-selection", "markdown": "https://wpnews.pro/news/synthesizing-feature-extractors-an-agentic-approach-for-algorithm-selection.md", "text": "https://wpnews.pro/news/synthesizing-feature-extractors-an-agentic-approach-for-algorithm-selection.txt", "jsonld": "https://wpnews.pro/news/synthesizing-feature-extractors-an-agentic-approach-for-algorithm-selection.jsonld"}}