Charmaine Barker

The authors introduce CLEAR, a lightweight, task-agnostic post-hoc method that uses held-out calibration data to characterize the group-conditioned geometry of the model's latent space, then at inference generates perturbation views directly in the latent space and measures their conflict relative to the calibrated geometry of the predicted group, selectively reducing evidential strength for latent-inconsistent inputs without retraining or altering the base prediction; on ImageNet→CUB it improves OOD and adversarial AUROC by 8.29 and 5.01 while running 17.4× faster than competing post-hoc methods, and preserves predictive performance across classification, regression, and object detection benchmarks.

The authors introduce CLEAR, a lightweight, task-agnostic post-hoc method that uses held-out calibration data to characterize the group-conditioned geometry of the model's latent space, then at inference generates perturbation views directly in the latent space and measures their conflict relative to the calibrated geometry of the predicted group, selectively reducing evidential strength for latent-inconsistent inputs without retraining or altering the base prediction; on ImageNet→CUB it improves OOD and adversarial AUROC by 8.29 and 5.01 while running 17.4× faster than competing post-hoc methods, and preserves predictive performance across classification, regression, and object detection benchmarks.