Same Failure, Different Evidence: Validity Domains For VLA Monitaring

Vision-language-action (VLA) policies enable robots to follow language instructions across diverse tasks, making reliable failure monitoring essential. Yet strong aggregate detection performance does not reveal which failures a monitor can recognize or which consequences its evidence can support. A shared failure label can hide opposite evidence: a VLA may choose the wrong action or execute the right action incorrectly. We establish that monitor validity is jointly determined by fault factor, sensor access, prediction target, and onset. These coordinates change sensor rankings, accepted executions, and alarm timing. VISTA turns this insight into a learning rule: encode policy and execution prefixes separately, then jointly train outcome prediction and training-only coordinate heads. A study of 2,640 trajectories from 360 sources yields terminal AURC 0.061 on 840 held-out ranking runs, versus 0.102 for a matched temporal model and 0.081 without coordinate supervision. Gains persist on reset-only goals, native referent errors, and independent actuator faults. Simultaneous certification validates 16 of 30 domain cells under common non-alarm risk and coverage requirements. Joint sensing certifies all five completion strata but only two safety strata.