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AI systems are making rapid progress in mathematics, but this progress is hard to measure. Benchmarks of solved problems saturate, and in benchmarks of open problems each problem can be resolved for the first time only once. We present SqueezeBench, a benchmark that is resilient to saturation: a success raises the bar...
An automated approach that uses Large Language Models in an agentic check--fix--verify loop to synthesize executable Python scripts that act as interpretable, problem-specific feature extractors that consistently outperform both expert-curated mzn2feat features and the best transformer-based trans2feat variants.
An automatic pipeline that is problem-agnostic to all problems in the MiniZinc format is built, finding that algorithm selection achieves a 39.6% average problem-weighted win rate against a one-shot Gurobi baseline, more than doubling the best single configuration (19.3%).
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