Latent Video Prediction Learns Better World Models
A recent study explores the capabilities of self-supervised video models as world models. The research evaluates four video foundation models across various robustness axes, revealing that latent-prediction models exhibit distinct advantages. These models demonstrate improved performance in scenarios involving pixel corruption and occlusion, suggesting their potential for robust world modeling.
- ▪The study analyzes four matched-capacity frontier video foundation models: V-JEPA 2.1, V-JEPA 2, VideoPrism, and VideoMAEv2.
- ▪Latent-prediction models degrade more gracefully under pixel corruption and preserve usable class structure under occlusion.
- ▪The results indicate that latent prediction can outperform fully fine-tuned models in terms of corruption and occlusion robustness.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15618 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.15618 (cs) [Submitted on 15 May 2026] Title:Latent Video Prediction Learns Better World Models Authors:Ali J Alrasheed, Aryan Yazdan Parast, Basim Azam, James Bailey, Naveed Akhtar View a PDF of the paper titled Latent Video Prediction Learns Better World Models, by Ali J Alrasheed and 4 other authors View PDF HTML (experimental) Abstract:Self-supervised video models are increasingly framed as world models, yet their evaluation remains largely confined to a single top-1 accuracy score on clean benchmarks. This leaves a major gap in comprehending their potential as world models.
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