WM-V2X: Cooperative World Models via V2X Communication for Consistent Occupancy Prediction

World models have demonstrated promising capability for 4D occupancy prediction and motion planning in autonomous driving. However, existing methods are designed for individual vehicles and are inherently limited by occlusions and restricted sensing ranges, which leads to inconsistent scene predictions across agents. In this paper, we propose WM-V2X, a cooperative world model framework that addresses this challenge by leveraging Vehicle-to-Everything (V2X) communication to share sensing information across connected agents for 4D forecasting on real multi-agent data. First, we design a cooperative scene tokenizer that fuses multi-agent sensory data into compact latent representations via early or intermediate fusion pipelines to facilitate 4D occupancy prediction. To further improve prediction performance, we introduce satellite imagery as an auxiliary conditioning signal that provides static scene context and visual trajectory information to a spatio-temporal transformer, which auto-regressively predicts future occupancy scenes and ego-trajectories. To evaluate cooperative world models, we reprocess the V2X-Real dataset to generate 3D voxel occupancy ground truth and introduce new cross-agent consistency metrics based on connected component analysis. Extensive experiments demonstrate that WM-V2X achieves the best 4D occupancy prediction with average mIoU of $46.04\%$ and $39.53\%$ under early and intermediate cooperation, and the lowest position error of $0.304$ m with a $0.174\%$ collision rate in motion planning. Cooperation further enhances cross-agent prediction consistency, raising the mean detection rate for dynamic objects by up to $41.56\%$ over the single-agent baseline.