When the Change Carries No Energy: A Detectable Regime Change That No Learned Monitor Detects

Latent world models need reliable regime detection, yet existing monitors track only residual energy-shifts in the mean or variance of prediction error. When an environment switches between noise laws of identical variance, every prediction error keeps its size and only its shape changes, so an energy-based monitor, sensitive only to the second moment, is provably blind to the shift, a claim we verify-along with our other guarantees-in a machine-checked proof. We introduce a generative world model that pairs a joint-embedding predictive architecture with a conditional flow-matching predictor over future latent trajectories (F-JEPA-RD), which scores the full predictive density rather than its energy. An analytic Gaussian base coupled with a learned residual yields exact trajectory density ratios while preserving the representation structure the joint-embedding design protects. An audit of sequential detection benchmarks reveals a pervasive validity failure: standard protocols mistake a detection statistic's natural drift during calm periods for genuine detection, creating an illusion of success across existing methods. Under a drift-robust protocol with null controls, an oracle given the true noise law detects variance-matched shifts almost perfectly, while every learned monitor we test-including our own model's readouts-collapses to chance, on both synthetic benchmarks and a four-year real-world recording. A diagnostic locates the loss: the encoder preserves and even sharpens the shape signature, which is then diluted by the multi-lag residual the readouts consume. Applying the same shape core to a single-step innovation residual instead recovers part of the lost power in a single-seed pilot, indicating the information is still present in the representation. When regime changes instead alter the underlying dynamics, F-JEPA-RD's density ratio retains strong power while staying silent under null controls, exactly as the theory requires. We release the benchmark, proofs, and scoring code for independent re-scoring.