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Imitation learning enables automated theorem provers to solve 46% more problems while drastically reducing proof steps compared to traditional methods.
LLMs can now automatically verify imperative code at scale, achieving state-of-the-art results on challenging verification benchmarks and paving the way for large-scale verified code datasets.
