Tag
Black-Mamba introduces a test-time adaptive forecasting architecture that uses accumulated surprisal to selectively update memory only upon evidence of distribution drift, achieving efficient adaptation on non-stationary time series.
ASK-NN is an asymmetric nearest-neighbor test for detecting distribution drifts between reference and query samples, with applications to LLM hallucination detection and artificial-text detection. It is computationally efficient, has theoretical guarantees, and performs competitively against baselines on synthetic and real-world benchmarks.
Proposes a Wasserstein-GAN approach for unsupervised calibration of sensor-induced distribution drifts, validated on tracking detector toy models and simulated calorimeter data with aging effects.