MIST tests whether models use valid context without trusting misleading context
MIST evaluates 1,000 reasoning items under clean, misleading, correct-context, and irrelevant-context conditions. Its SC2W metric counts how often misleading context turns a clean-correct answer wrong, while SCOPE trains on those matched failures.
Why it matters: A model that ignores every retrieved source can appear resistant to poisoning while remaining useless whenever external evidence is accurate and necessary.