Calibrated Uncertainty for Long-Horizon Rollouts in Latent World Models

Latent world models can imagine long rollouts, but planners need to know when their predictions become unreliable. We show that common uncertainty measures, including ensemble disagreement, latent divergence, and predictive variance, are Lipschitz functions of compact latent states and therefore have horizon-independent ceilings, while rollout error need not. Across two world-model families, ensemble disagreement grows more slowly than reward error and becomes anti-correlated with it on several tasks. We address this mismatch without retraining or ensembles. A linear probe maps a frozen latent state to rollout error and is calibrated by split conformal prediction at each horizon. The resulting intervals achieve finite-sample coverage and track per-rollout error better than standard conformal baselines on nearly every DreamerV3 and TD-MPC2 task. Where error compounds, they are substantially sharper than ensemble-scaled intervals, while matching them when error remains flat. A single-checkpoint probe matches an ensemble-trained probe at $1/K$ of the training cost with a $K$-member ensemble. For trusted-horizon decisions, our intervals accept as many imagined steps as ensemble disagreement while exceeding the error tolerance less often. Coverage can degrade under substantial behavior-policy shifts, requiring recalibration.