Plausible Future, Wrong Choice: Steering World-Action Models toward Intended Future

World-Action Models (WAMs) have emerged as a promising paradigm for embodied generalist policies by jointly modeling future world evolution and robot actions across diverse manipulation tasks. However, representing diverse futures is only the first step: a capable WAM must also generate futures that match the requirements of the current task and execution progress. In long-horizon manipulation where similar observation-action segments occur across different tasks and stages, existing approaches may generate locally plausible yet misdirected rollouts to drift toward wrong task stage, i.e., a phenomenon we term $\text{Stage Confusion}$. To address this problem, we propose $\text{TaskWAM}$, a closed-loop, stage-aware framework that represents fine-grained task requirements with $\text{Mode Tokens}$ and tracks execution progress with $\text{State Tokens}$, forming a structured task state to steer world–action generation toward intended future. We further introduce $\text{StageBench}$, a controlled multi-stage manipulation benchmark with parameterized stage variations and automated evaluation. Extensive experiments on both simulation benchmarks and real-world tasks demonstrate that TaskWAM effectively reduces confusion and achieves competitive performance with state-of-the-art methods. Moreover, TaskWAM provides strong plug-and-play transfer across heterogeneous WAM architectures and robot embodiments, including single-arm robots, dual-arm grippers, dexterous hands, and humanoid robots.