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LLMs that ace standard coding benchmarks spectacularly fail at esoteric languages, revealing a reliance on memorization rather than true reasoning.
Coding agents can pinpoint performance bottlenecks in real-world inference codebases, but struggle to generate functional optimization patches, revealing a critical gap between identifying problems and implementing solutions.
LLMs can achieve 85% grammatical accuracy in a language with minimal training data, like Tulu, using only structured prompting and clever constraints.
