A survey paper on World Action Models.
WAMs are moving robotics from reacting to the present toward predicting consequences before acting.
A model only counts as a WAM when its predicted future directly helps produce， score， verify， or train the action.
The trend is "dream less， act more"： full video generation is often too slow and memory-heavy for real control loops.
Many newer systems skip rendered video and use latent features， geometry， affordance maps， or motion representations instead.
Photorealistic futures are not necessarily the most useful； flow， masks， tactile signals， and physically grounded latents may constrain action better.
There is no single winning architecture because every design trades predictive richness against latency， memory， action-label cost， and physical reliability.
The biggest open question is whether robots can spend heavy predictive compute only when uncertainty， contact， or irreversible error makes it necessary.
- arxiv. org/abs/2606.20781
Title： "World Action Models： A Survey"