Utility-WAM: Correcting Future-Induced Action Changes

World Action Models (WAMs) can condition robot actions on generated visual futures, but this additional context is neither free nor uniformly beneficial. We study what a predicted future actually changes: the action delta $\Delta a$ between the future-conditioned and future-free paths of the same model. Under a shared WAM checkpoint, this delta barely moves average success ($+0.8$ points on LIBERO, $-0.8$ on RoboTwin 2.0), while generating the future increases inference latency by 1.7–1.9$\times$ on every query. The averages hide a concentrated failure mode that we call future-induced negative transfer: the delta helps on long-horizon tasks ($+2.4$ points on LIBERO-Long), is negligible on most (26 of 50 RoboTwin 2.0 tasks change by at most one point), and fails severely on a few. Press Stapler and Place Can Basket lose 54.0 and 34.0 points and are also among the three most harmed tasks for both future-generating FAST-WAM variants. We introduce Utility-WAM for in-place correction of WAMs that retain future-conditioned inference. Rather than improving a current-only student overall, it targets concentrated failures without discarding the long-horizon benefit of generated futures. Its main learning component is a residual adapter that repairs future-induced action changes while keeping the shared WAM frozen. It aligns induced velocity deltas with beneficial ones induced by ground-truth futures and penalizes local error beyond the future-free reference. As intended, the adapter acts selectively: the two severe-task losses shrink to 6.0 and 14.0 points, while the remaining 48 RoboTwin tasks improve by 0.35 points on average and the long-horizon gain is retained. Because this correction is soft and still pays the video cost on every query, a lightweight gate reuses the adapter's paired $\Delta a$ outcomes to skip futures that correction does not make useful. It generates video on only 24.5% of RoboTwin queries, reduces latency by 38%, and improves over always-on correction (93.3% versus 92.8%; 98.9% versus 98.7% on LIBERO). On three real-world bimanual tasks, Utility-WAM succeeds in 53 of 60 trials, compared with 43 for FAST-WAM.