A Low-Dimensional Deterministic Dynamics Core for Controllable Long-Horizon Latent World Models

Interactive world models must generate plausible observations while preserving the consequences of actions over long autoregressive rollouts for game agents, model-based planning, and embodied control. Yet commonly used one-step and teacher-forced metrics can make flat and factorized transition models appear similar under matched capacity and exposure-bias noise, even when their free-running behavior differs. We study this discrepancy in frozen pretrained latent spaces and introduce WorldBridge, a factorized transition model that predicts a compact dynamics code before rendering the remaining appearance coordinates deterministically. Both components retain access to the previous latent and the action, so the compact code guides prediction without imposing a strict information bottleneck. Under capacity-, training-budget-, and exposure-noise-matched comparisons, the factorized model has comparable short-range fidelity but produces more controllable and radially stable long-horizon rollouts than a flat predictor, with the clearest separation at the single-evaluation operating point. Comparisons with stronger memory-based predictors further show that the training objective and the factorization contribute distinct parts of the observed improvement, and that the advantage is not explained by generative stochasticity alone. A larger-scale validation retains the controllability advantage on temporally held-out trajectories and improves action identification. Finally, component-wise distillation yields a real-time student using only a few network evaluations per frame. These results support evaluating latent world models by their closed-loop behavior, not by short-range fidelity alone.