Beyond Ego Privilege: Queryable Multi-Agent 4D Occupancy World Model

Ego-conditioned occupancy world models typically tie both motion conditioning and prediction windows to ego. In cooperative driving, comparing plans shared by multiple vehicles requires assessing their joint effects in the same road region, but not in a window that changes with each ego plan. We present BEP-World (Beyond Ego Privilege), a queryable 4D occupancy world model with a shared plan interface for ego and surrounding vehicles and separate spatial queries. Besides, we propose Query-Aligned Memory Aggregation and Grounded Plan Fusion to organize historical evidence and vehicle plans in the requested coordinates before scene dynamics, enabling direct forecasting without post-prediction warping. On OpenOccupancy, BEP-World supports forecasting, querying, multi-agent conditioning, and their combination. It predicts occupancy beyond the future ego window and benefits from surrounding-vehicle plans under non-ego queries. Furthermore, we construct BEP-Pairs, a simulation-based paired-intervention development set for diagnosing responses beyond controlled vehicles. Extensive experiments and ablations identify spatial fields as the main source of plan utilization and reveal trade-offs between forecasting accuracy, query coverage, and conditioning gains. Code are available at https://anonymous.4open.science/r/BEP-World.