Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control
World generative models are typically used through what they produce: a rendered future, a video-conditioned action, or latent context computed by a costly generative branch. We argue that their more reusable asset is the computation that constructs a future. As a generator transforms a corrupted future into a coherent trajectory, its intermediate states organize appearance, spatial layout, and interaction across levels of abstraction. Can this future-generative computation be internalized in a representation inferred from the present alone? We present Enfold, which transfers this computation into a representation predicted from the current visual context and language instruction. During ...
| Authors | Weili Zeng, Yitong Xing, Fulong Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 July 2026 |
| Last updated at source | 6 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.