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Posted: 2025-04-26 04:45:35 UTC
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
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2025-04-26 04:45:54 UTC
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Rollup News
This paper introduces Executable Functional Abstractions (EFAs), a method for inferring generative programs that capture the underlying structure of math problems. By feeding an LLM a math problem, it extracts a parameterized program capable of generating unlimited valid variants, improving the model's ability to solve the original problem and probe reasoning limitations.
Inference of Executable Functional Abstractions (EFAs) for math problems
Data augmentation using EFAs leads to consistent gains across math benchmarks
EFAs can find problem variants that expose limitations in GPT-4o
Crafting adversarial examples by intuition alone
Systematically probing reasoning limitations of models