# Online Calibration

The work replaces one-shot quantile estimation with online feedback control (Adaptive Conformal Inference with tuning-free, locally-adaptive, and group-conditional variants) to address non-exchangeability of candidates during reinforcement-learning search, tracking requested coverage to within about 10^-3 across three architecture families and both single-step and sequential search (three seeds) while pruning 25-50% of evaluations at no measured accuracy cost, whereas static calibration loses coverage control and a Gaussian-process baseline stays conservative.

The work replaces one-shot quantile estimation with online feedback control (Adaptive Conformal Inference with tuning-free, locally-adaptive, and group-conditional variants) to address non-exchangeability of candidates during reinforcement-learning search, tracking requested coverage to within about 10^-3 across three architecture families and both single-step and sequential search (three seeds) while pruning 25-50% of evaluations at no measured accuracy cost, whereas static calibration loses coverage control and a Gaussian-process baseline stays conservative.

The work proposes a model-agnostic online martingale PAC-Bayesian framework that replaces independent-sample concentration with martingale concentration to yield finite-sample certificates under temporal dependence and distribution shift, and uses the certificate as a surrogate regularizer for online calibration by training a gated residual Bayesian head on top of a fixed forecasting backbone under a predict-then-update protocol, improving stability and accuracy under covariate and concept shift across convolutional, attention-based, and large language model-based forecasters.

The work proposes a model-agnostic online martingale PAC-Bayesian framework that replaces independent-sample concentration with martingale concentration to yield finite-sample certificates under temporal dependence and distribution shift, and uses the certificate as a surrogate regularizer for online calibration by training a gated residual Bayesian head on top of a fixed forecasting backbone under a predict-then-update protocol, improving stability and accuracy under covariate and concept shift across convolutional, attention-based, and large language model-based forecasters.