EventCone-WM: Counterfactual Event Cones for Reliable Action-Conditioned Latent Planning

Action-conditioned world models can predict plausible futures while propagating an action's effects to entities it cannot physically reach. We introduce EventCone-WM, which contrasts candidate and matched-control latent rollouts, decodes entity-event relations, and propagates them into a finite-horizon event cone. Outside-cone invariance, inside-cone effect separation, paired-action ranking, and a planning penalty discourage unsupported changes while preserving goal-relevant interactions. The evaluation spans three manipulation environments under visual, physical, and occlusion shifts, jointly examining closed-loop success, cone false-positive rate (FPR), collisions, and latency. EventCone-WM is shown at 75.9% Physics-OOD success versus 57.8% for JEPA-WM; across the full comparison, cone FPR is 11.1% versus 24.5%, collision rate is 8.7% versus 14.9%, and per-action planning latency is 83.6 versus 68.4 ms. These results demonstrate that EventCone-WM substantially reduces physically implausible interactions with only a modest computational overhead, maintaining real-time viability for closed-loop control.