# Learn then Test (LTT)

In streaming deployment a fixed policy's loss can drift with observed history, so certifying a fixed population risk no longer describes the risk actually encountered. StreamLTT certifies average predictable risk, the conditional risk of each decision given the information available before its outcome, averaged along the stream, and conditional centering turns empirical deviations into a martingale sum. Across seven forecasting datasets and seven deployment risks, its variance-adaptive version pushes the certified frontier close to the risk target while empirical family-wise error stays near the prescribed level.

In streaming deployment a fixed policy's loss can drift with observed history, so certifying a fixed population risk no longer describes the risk actually encountered. StreamLTT certifies average predictable risk, the conditional risk of each decision given the information available before its outcome, averaged along the stream, and conditional centering turns empirical deviations into a martingale sum. Across seven forecasting datasets and seven deployment risks, its variance-adaptive version pushes the certified frontier close to the risk target while empirical family-wise error stays near the prescribed level.

In safety-of-the-intended-functionality analysis under ISO 21448, an uncertainty threshold that keeps a LiDAR detector within a performance target under one condition had no procedure to check whether it still does under another. The threshold is transferred unchanged into the condition of use, and a statistical test decides at a stated error probability whether the target is applicable, not applicable or not determined, together with a lower bound on the independent units needed to show the target is met. On PointPillars ensembles over KITTI and nuScenes, counted in frames, the threshold transferred from nuScenes to KITTI was not applicable on all five splits; counted in recorded sequences, neither dataset held enough units to show any target limit met.

In safety-of-the-intended-functionality analysis under ISO 21448, an uncertainty threshold that keeps a LiDAR detector within a performance target under one condition had no procedure to check whether it still does under another. The threshold is transferred unchanged into the condition of use, and a statistical test decides at a stated error probability whether the target is applicable, not applicable or not determined, together with a lower bound on the independent units needed to show the target is met. On PointPillars ensembles over KITTI and nuScenes, counted in frames, the threshold transferred from nuScenes to KITTI was not applicable on all five splits; counted in recorded sequences, neither dataset held enough units to show any target limit met.