Recovered or Memorized? Investigating Dynamical Consistency and Faithfulness in World Model Latents

World models develop internal simulators that can learn the structure and dynamics of an environment. It has been demonstrated that their internal representations are both meaningful and causal, but are these representations faithful to the governing dynamics of the environment they are learning? We introduce Anchored Recovery Certification (ARC), a procedure that utilizes a discrete Fréchet distance calculation with ground truth to evaluate dynamical consistency on candidate world models up to an affine gauge. We use the procedure on TD-MPC2 (decoder-free world model) that has learned the pendulum swing on an observation lift under varying hyperparameters. The model is then steered within a PCA-derived intervention subspace and compared against a ground-truth simulator using relative error to investigate dynamical faithfulness. Although TD-MPC2 was ARC-sufficient, the relative error between the simulator and the causal latent is substantial. Further research could be conducted with decoder world models such as DreamerV3 (reconstruction loss) to investigate whether the results differ.