SOURCE-LINKED INTELLIGENCE
Latent Action as Intention Enables Efficient Future Imagination for World Action Models
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives, especially with scarce robot demonstrations and in out-of-distribution scenarios. To bridge this gap, we introduce **LAWA**, a WAM architecture that uses compact latent actions as an operational representation of future intentions, enabling efficient test-time future imagination
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T17:59:03.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.