Can Internal States Signal Trouble and Guide Recovery in VLAs?

Frozen vision-language-action (VLA) policies can keep acting after progress has stalled. We introduce NAR, a test-time harness around a frozen mixture-of-experts VLA, driven by the expert routing already computed during action generation. On LIBERO-10, HiMoE-VLA failures exhibit stronger routing recurrence despite similar numbers of experts used per inference. NAR measures recurrence relative to the episode’s opening and detects deviations from successful source executions using nearest-neighbour scoring, temporal smoothing, and episode-level conformal calibration. Alarms trigger a fixed release-and-lift primitive before control returns to the same policy, requiring no parameter optimization or additional policy forward passes. Under a shared monitoring pipeline, routing achieves higher recall than the evaluated state and action readouts at their calibrated operating points. In a retrospective cross-suite evaluation, a profile fitted on native and perturbed LIBERO-Goal successes detects 97.2% of failures at a 1.32% episode-level false-alarm rate across 4,320 episodes from the other three suites, without target-task reference trajectories. With a separate pooled profile, paired replay of 1,608 completed alarms yields 175 net additional successes: 10.88 percentage points per alarm and 0.40 points when normalized by the recorded corpus. A separate timing study finds the lowest observed harm at the alarm, while later interventions lose opportunities to act. Transfer depends on source-success coverage, with specificity degrading on CALVIN. These results show how a policy’s existing routing trace can support a test-time harness that connects execution monitoring with the measured benefits and harms of intervention.