LeWAM: Latent evolving World Action Model
World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained Video Diffusion Models (VDMs). In VDM-based WAMs, observations are first encoded by a VAE, and the resulting compressed latents are then processed by large video diffusion backbones to extract effective features for action generation. However, this paradigm ties WAM performance and training cost to large-scale video generation pretraining, limiting WAM efficiency and scalability. In this paper, we theoretically and empirically investigate how visual representations affect action generation in WAMs. Our results show that predictive embeddings from Joint-Embedding Predictiv...
| Authors | Xueji Fang, Boqiang Duan, Hua Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 September 2026 |
| Last updated at source | 10 October 2026 |
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