CausalWM: Causal Chain-of-Thought Reasoning for Embodied World Model
Embodied world models learn to predict future physical dynamics from visual observations and control signals, where physical knowledge is implicitly entangled within latent representations. We introduce CausalWM, a 16B embodied world model that performs explicit causal chain-of-thought reasoning before future video prediction. CausalWM organizes useful variables into a reasoning trajectory, allowing the model to progressively capture causal dependencies underlying physical evolution. To train CausalWM, we collect 31K hours embodied data and develop a three-stage paradigm consisting of large-scale video pre-training, causal CoT mid-training, and multi-objective RL post-training. Despite us...
| Authors | Ziming Xu, Shuang Liang, Ruobing Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 September 2026 |
| Last updated at source | 22 September 2026 |
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