# Selective Future Imagination

World Action Models (WAMs) predict future visual states and actions together, but generating a visual future at every replanning step is expensive and sometimes unnecessary. Bind-WAM uses whether the roles named in a language instruction are stably bound to objects in the current view as an interpretable signal: an offline pipeline labels reference mismatch, invisibility and identity drift from complete demonstrations, and a lightweight router of about 6.65M parameters switches a shared WAM between direct action prediction and joint future-action denoising. On LIBERO, Bind-WAM reaches 98.60% mean success while invoking the joint path at only 40.58% of replanning states, cutting average completion time from 15.73 s for Fast-WAM-Joint to 11.71 s.

World Action Models (WAMs) predict future visual states and actions together, but generating a visual future at every replanning step is expensive and sometimes unnecessary. Bind-WAM uses whether the roles named in a language instruction are stably bound to objects in the current view as an interpretable signal: an offline pipeline labels reference mismatch, invisibility and identity drift from complete demonstrations, and a lightweight router of about 6.65M parameters switches a shared WAM between direct action prediction and joint future-action denoising. On LIBERO, Bind-WAM reaches 98.60% mean success while invoking the joint path at only 40.58% of replanning states, cutting average completion time from 15.73 s for Fast-WAM-Joint to 11.71 s.