CoWAM给双臂机器人策略把关，只在有把握时改动。模拟中成功率比最强基线高9.6个百分点，坏干预不足1%。
CoWAM是为World Action Models设计的选择性干预层，把同步、角色兼容和碰撞收敛定义为协调合约。它只在某替代动作满足所有义务且带来低风险改进时替换默认策略，否则保留原动作或执行弃权回退。在8个模拟双臂任务上，CoWAM的协调有效选择率比仅用合约的变体高16.7个百分点，闭环成功率比最强选择性基线高9.6个百分点，有害干预低于1%。实验结果说明协调合约可以成为利用世界动作预测做保守策略干预的有效接口。
CoWAM: Coordination Contracts for Selective Policy Intervention with WAMs
World Action Models (WAMs) augment robot policies with action-conditioned predicted futures, but a plausible future alone does not justify changing the action that a bimanual policy would execute. We present CoWAM, a selective intervention layer that expresses synchronization, role compatibility, and collision convergence as coordination contracts. Each contract combines typed admissibility checks with event-conditioned verification and calibrated intervention gates. CoWAM preserves the nominal action unless an alternative satisfies every active obligation and provides a clear, low-risk improvement; when the nominal action is also inadmissible, it invokes a predefined abstention fallback. To separate selector quality from proposal quality, all methods operate on identical candidate pools and commit their decisions before shared oracle labeling. Across eight simulated bimanual tasks, CoWAM improves coordination-valid selection by 16.7 percentage points over the contract-only variant and raises closed-loop success by 9.6 percentage points over the strongest selective baseline, while keeping harmful interventions below 1%. Together, these results establish coordination contracts as an effective interface for conservative policy intervention with predicted world-action evidence across coordination-rich bimanual tasks.