Your World Model Is Not Blind, Your Detector Is: Native Prediction Errors for Regime Detection

A world model that has learned how a system evolves should be the first to notice when that system starts to evolve differently. Yet learned representations are routinely judged blind to regime change: a predictive representation that recovers the hidden state of a dynamical system still lets standard sequential change-point detectors miss most changes. We show that this blindness belongs to the detector, not the representation. While standard detectors assume latent states follow a linear process, we instead query the world model directly-scoring each transition by its one-step predictive error normalized by covariance fitted on nominal (calm) data. This native-residual detector requires no retraining and elevates detection rates from a small minority of shifts to near the exact-likelihood ceiling. We then prove why this gap exists. The standard detector’s variance channel can only gather evidence above a threshold fixed by its alternative hypothesis grid, which no grid refinement resolves. Furthermore, we prove an absorption principle: a flexible predictor trained on calm data systematically explains away the component of a regime shift aligned with its learned dynamics, while a learned variance head re-weights the residual back toward nominal behavior. This reveals why standardizing by model uncertainty is inherently deceptive. Across convolutional, transformer, and period-folding backbones, native-residual detection alarms roughly three times faster than classical baselines, fails predictably only when an encoder discards one-step temporal order, and outperforms recent detector families, calm-fitted forecasters, and a zero-shot foundation model.