Paper page - World Action Models: A Survey

Source: https://huggingface.co/papers/2606.20781

Abstract

World Action Models are predictive-action systems that generate future states for decision-making, with designs balancing representational richness against computational constraints.

World Action Models(WAMs) areembodied predictive-action modelsthat make a forecast of the future available to action. Recent WAMs repurpose largevideo generation models, and a parallel line relies on language orvision-language backboneswithout a video-generation core. This rapid expansion has blurred the boundary among broad world models,video generation models, action-grounded video world models, Vision-Language-Action policies, and WAMs. This survey gives the field a common account. It first clarifies these boundaries, then organizes existing works through two complementary views. The first view asks what each method is required to generate, spanning rendered futures, latent futures, and video-generation-free action reasoning. The second view decomposes each method bypredictive substrate, backbone,action coupling, anddeployment regime. This anatomy supports a unified discussion ofinteractability,causality, persistence,physical plausibility, andgeneralization, followed by data, evaluation, and open challenges. Across these axes, a consistent design pattern emerges: WAMs are not simply video generators with action heads, but predictive-action methods whose design choices trade representational richness against compute, memory, latency, and action-label cost. The field is moving toward methods that generate less of the future while preserving what control requires. The survey homepage is available at https://world-action-models.github.io/.

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