LLMs achieve strong initial accuracy on code output prediction but frequently alter their answers under semantics-preserving mutations, with drops up to 70% and flawed reasoning detected in 10-50% of correct cases via human review.
So, the function returns `1` when called with `n = 1` and `target = 1`
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Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?
LLMs achieve strong initial accuracy on code output prediction but frequently alter their answers under semantics-preserving mutations, with drops up to 70% and flawed reasoning detected in 10-50% of correct cases via human review.