What Must Quantization Preserve in Latent World Models?

What must quantization preserve in latent world models to retain their planning ability? Existing work has primarily characterized sensitivity to precision configuration. To our knowledge, this is the first study to investigate latent world-model quantization from the perspective of representation preservation and calibration-target design. We analyze how quantization-induced representation errors propagate to goal-distance estimates and candidate rankings, and argue that calibration should preserve reusable latent representations rather than merely match scalar planning costs. We derive a bound relating terminal relative-representation error to the second moments of local encoder and predictor reconstruction errors, providing a theoretical basis for Module-MSE. Across LeWM, DINO-WM, and PLDM on four control tasks under symmetric per-channel W4 quantization, Module-MSE reaches 79.33% average success compared with 79.83% for full precision, while achieving the strongest overall planning-decision fidelity. It also maintains strong performance across the tested learning rates and calibration-set sizes. These results show that severe degradation under naive W4 quantization is not inevitable, establish representation preservation as a practical calibration principle, and support Module-MSE as a default choice for latent world-model quantization.