# TASER: Task-Aware Stein Regularisation for Geometry-Driven Robustness Source: [TASER: Task-Aware Stein Regularisation for Geometry-Driven Robustness]() Collection index: [llms.txt]() Data updated: 2026-06-02T05:00:05.014Z Authors: Micha{\\l} Kozyra, Gesine Reinert Published: 2026-05-28T21:49:04.000Z ## Abstract Modern deep networks remain fragile under distribution shift and adversarial perturbations, often due to excessive or poorly structured input sensitivity. We introduce TASER (Task-Aware Stein Regularisation), a training-time regularisation framework derived from Langevin Stein operators. By penalising pointwise Stein residuals under the training distribution, TASER encourages geometric compatibility between predictors and data density, inducing anisotropic, data-aware smoothness. We provide theoretical links between Stein regularisation and reduced first-order shift sensitivity, develop scalable implementation variants compatible with modern architectures, and demonstrate improved robustness and stability across regression and vision benchmarks. Across CIFAR-10 experiments, TASER consistently improves the adversarial robustness of established training methods without incurring statistically significant clean-accuracy degradation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-10 (test) (Accuracy and Overhead Profile)](): Image Classification; CIFAR-10 (test); Clean Accuracy: 77.88. ## Original sources and implementation [Original paper]() [PDF]()