Large language models can write essays, solve complex math problems, and generate code at impressive levels. Yet they frequently fail at surprisingly simple variations of the same tasks.

Melanie Mitchell describes this uneven performance as “jagged intelligence” — a landscape where models excel in certain areas while remaining brittle and unpredictable in closely related ones. Slight rewording of a problem, added irrelevant details, or minor changes in context can cause dramatic drops in performance, even on tasks where the model previously succeeded.