World models can solve tasks without learning accurate representations of environment dynamics—task performance alone doesn't guarantee the model understands how the world actually works.
This paper challenges the assumption that world models learn accurate internal representations of environment dynamics. The authors introduce a formal definition of 'faithful' world models based on equivariance and propose a metric called latent rollout consistency to measure whether learned latent dynamics actually match the environment's true dynamics.