Researchers have introduced VLA-Scope, a two-stage framework for predicting failure in vision-language-action models under out-of-distribution conditions. The framework combines input-shift characterization with execution history to improve reliability in robotic action models. Initial results show improved OOD detection and failure prediction compared to baseline models.