Seeing Farther to Act Now: Long-Horizon Future Modeling for Robot Manipulation

World Action Models (WAMs) provide predictive context for robot manipulation by conditioning action generation on imagined future visual states. However, existing WAMs commonly couple the visual prediction horizon to the action horizon, restricting future information to the immediate interaction and potentially obscuring longer-term task consequences. We show that prediction horizon introduces a fundamental trade-off: short-horizon futures preserve fine-grained motion and interaction details, whereas longer-horizon futures capture broader task progression but with greater visual uncertainty, making the most useful temporal scale task dependent. Building on this insight, we propose Seeing Farther to Act Now (SFAN), a multi-timescale world-action model that decouples visual prediction from the executable action horizon and assigns complementary roles to different temporal scales. SFAN uses sparsely sampled long-horizon futures to provide broader task context and generate the primary action prediction, while a complementary short-horizon pathway densely samples nearby futures to predict a residual correction conditioned on the primary action. This asymmetric design allows longer-range futures to guide interaction selection while preserving the fine-grained local information needed to refine how the selected interaction is executed. Extensive experiments on simulated and real-world robot manipulation demonstrate that SFAN consistently outperforms existing baselines, with particularly strong gains on tasks that benefit from longer-horizon future context.