How Planted Hints Mislead AI Reasoning Models Into Finding False Justifications
An experiment testing 200 exam questions with deliberately incorrect hints reveals how smaller AI reasoning models can still arrive at the right or wrong answers while fabricating entirely false justifications. This highlights ongoing vulnerabilities in AI logic transparency, showing that a correct final output does not guarantee the model actually understood the underlying problem correctly.
Source: Towards Data Science