Go Further, Be Lighter: Recurrent Predictive Futures for World-Action Models

World-action models (WAMs) repeatedly compute predictive futures for control, incurring substantial inference cost. Asynchronous execution reduces waiting, but the overhead of temporal correction can limit effective action-generation throughput. Crucially, the predictive state conditioning the executing action already anticipates the outcome of that chunk before the robot reaches the next control boundary. Our *key insight* is that even imperfect predictions capture chunk-end changes in task-relevant regions. This creates an opportunity to advance the predictive future into a further future for subsequent control. To this end, we introduce the **Further-Future Propagator (FFP)**, which treats the predictive state as a recurrent state and learns a future-to-further-future transition that advances the predictive state across control rounds guided by factual evidence. Action relevance shapes how information important for action generation is preserved and updated, while a recurrent manipulation context modulates how the predictive state evolves across control rounds. The composed state conditions the frozen native action pathway and becomes the next recurrent input. Before each transition, a Confidence module estimates the marginal value of obtaining a fresh native prediction from the WAM, enabling adaptive refresh instead of a fixed refresh schedule. Across three architecturally distinct WAMs on CALVIN and RoboTwin 2.0, FFP improves control–compute trade-offs. On Faster-WAM/RoboTwin 2.0 Easy, FFP achieves $7.4\times$ the effective action-generation throughput of Real-Time Chunking (RTC) while improving success rate from 76.1% to 92.2%. Across four real-world tasks with two WAMs, task-averaged completion time decreases by 34.3–39.3% relative to asynchronous baselines, with mean success rate maintained or improved.