SAVE-WAM: Speculative Action Verification with Environment Feedback for Asynchronous World-Action Models

Large generative robot policies often adopt asynchronous inference to reduce execution latency, but this introduces generation discrepancy: actions are generated from observations obtained before all previously predicted actions have been executed. Although existing methods attempt to mitigate this discrepancy through calibration during action generation, there lacks feedback from newer observations affected by ongoing execution. As a result, action generation remains open-looped, allowing accumulated prediction errors and eventually causing the trajectory to deviate from the synchronous inference. Therefore, we aim to explicitly model and correct the discrepancy introduced by asynchronous action prediction in a closed-looped manner. Inspired by speculative decoding in language models, we introduce $\text{SAVE-WAM}$, a training-free post-generation feedback mechanism for asynchronous World-Action Models. SAVE-WAM treats asynchronously generated actions as speculative drafts and verifies them by measuring their deviation from the corresponding synchronous trajectory. Using updated observations during execution, the verifier detects excessively deviated trajectory, and triggers the replanning. As a plug-in mechanism, SAVE-WAM can be integrated with existing asynchronous inference methods to correct generation discrepancies with modest overhead, thereby improving the accuracy-efficiency trade-off. On LingBot-VA with FDM, SAVE-WAM improves success over unverified asynchronous execution from 2% to 42% on $\text{Hanging Mug}$ and from 82.76% to 92.76% overall, while retaining 73.8% latency overlap. Moreover, verification enables the use of larger action chunks without degrading task success, providing an additional dimension for improving generation efficiency. With a larger chunk size, SAVE-WAM achieves up to 1.97$\times$ higher action-generation throughput while maintaining a comparable success rate.