BoundaryWorld: Decision-Regime-Sufficient World Models for Reliable Long-Horizon Planning

World models are often optimized for state or observation fidelity, yet a planner does not need every detail of a predicted future. For a fixed downstream decision problem, the consequential distinctions are those that can change action rankings or feasibility. We formalize this requirement with Decision-Regime Trajectory Sufficiency (DRTS), which measures whether a learned model preserves the action-conditioned joint distribution of future decision regimes induced by that problem. A regime-abstraction residual measures the utility loss induced by representing a trajectory through its regime sequence and yields a continuous-plan regret bound that separates abstraction error from predictive error. BOUNDARYWORLD adds a rollout-conditioned joint regime-trajectory objective to a standard latent world model while retaining the same CEM search mechanics. An analytic diagnostic illustrates the distinction; on Safety-Gymnasium and MetaDrive, BoundaryWorld reduces \(H=20\) planning regret by 19.3% and 21.7%, respectively, relative to the strongest controlled alternative in each domain.