# StreamLTT

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.