TurboSens: An Interactive Turbofan Environment for Probing World Model Representations against Hidden Engine State

World models are expected to learn compact representations that capture the underlying state of dynamical systems, but evaluating whether these representations preserve meaningful latent state remains difficult under partial observability. We introduce TurboSens, a controlled environment for measuring latent state preservation in world models. TurboSens provides a configurable turbofan simulation with paired latent state and sensor observations, allowing representation quality to be evaluated against known underlying system state rather than only through prediction or reconstruction performance. The environment is designed to simulate issues that could arise when relevant state is only partially observable and must be inferred from temporal context, actions, and changing operating conditions. We evaluate representations from several world model families using frozen encoders and inverse probes that measure latent state recovery, recovery as a function of observability, robustness to distribution shift, and responses to maintenance interventions. Across controlled changes in observability and degradation context, we find that self supervised representations preserve far less of the state than the observations demonstrably contain, with recoverability depending on the pretraining objective and on memory aligned with maintenance events rather than on context length. These results highlight a gap between what the observations contain and what learned representations preserve, and demonstrate how controlled simulation can provide a direct measurement of whether world model representations retain the underlying state required for prediction and decision making under partial observability.