One Correction, Every Case: When LLMs Actually Update the Rule
TL;DR for operators An AI system receives one signal that an operating rule has changed. The important test is not whether its average performance eventually recovers, but whether it immediately applies the revised rule to cases it has not yet revisited. Many models fail this test quietly. They correct each stimulus only after encountering it again, producing gradual recovery without inferring that one hidden rule changed for every stimulus at once. For teams deploying agents, that distinction matters whenever a policy change, workflow update, or exception rule must propagate across related cases. ...
