Why AI Reasoners Double‑Down on Wrong Answers (Humans Don't)
Large reasoning models spend more tokens on wrong answers, unlike humans who quit when stuck. The study separates difficulty registration (time tracks problem hardness) from deliberation allocation (how long they linger on failures) and finds LRMs lack a self‑stop signal, implying current metareasoning misses a crucial control.