Feasible Actions, Misleading Rollouts: Diagnosing Latent World Model Planning

Prediction error, goal-cost disagreement, and actuator imprecision require different evidence in latent world-model planning. We study frozen planners by executing fixed action banks and comparing predicted cost, actual-outcome cost, and physical error. On ten V-JEPA2-AC/RoboCasa sources and ten LeWM/PushT tasks, predicted selection incurs mean range-normalized actual-cost regrets of 19.2% and 6.5%, respectively, while outperforming within-bank uniform selection. Seven LeWM banks select the actual-cost minimum; the distinct banks do not support a model ranking. We then test intermediate known-state feedback in DINO-WM. After a common actuator finish, feedback reduces composite object error relative to native planning, endpoint-only correction, and first-patch consistency control by 0.132, 0.090, and 0.098 on 32 generated tasks. Coarse-success non-inferiority remains unestablished. A finite-history analysis characterizes the learned state-error paths omitted from a shared comparison bound, without guaranteeing smaller realized errors. Separately, a 16-task LeWM terminal-constraint test does not confirm an object benefit. Together, the results show why candidate-level cost regret, physical outcomes, and actuator precision must be evaluated separately.