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This paper presents an automated agentic approach using Large Language Models to synthesize interpretable Python feature extractors for algorithm selection in constraint satisfaction problems, outperforming expert-curated methods.
A discussion about whether anyone is operating a fully automated 'dark factory' for code, where specifications are input and pull requests are output, likely involving AI-driven development pipelines.