WorldAgen: Unified State-Action Prediction with Test-Time World Model Training
How can vision-language-action (VLA) models adapt to new environments where world dynamics shift? While recent research has combined world modeling and action prediction to improve VLA performance, existing methods largely rely on pretraining on static datasets, without mechanisms for active adaptation at deployment time. As a result, these models often fail to generalize when deployed in unseen scenarios with novel object configurations or dynamics. We present WorldAgen, a unified framework that jointly learns world modeling and action prediction while enabling Test-Time Training (TTT) to adapt to new environments. WorldAgen employs a shared Transformer backbone with two heads: (1) a wor...
| Authors | Chi Wan, Kangrui Wang, Yuan Si et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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