Yilei Man, Yang Chen, Jingcai Guo · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28483 · open-access PDF
在现有的家庭中,联合嵌入预测架构(JEPAs)提供了一个自然的框架,通过直接在隐形空间中模拟过渡,而不是在像素层面。然而,在基于JEPA的世界模型中,仍然存在两个问题:(i)物理地化缺乏明确的时间背景聚合和(ii)行动规划总是过度依赖于通用采样分布。
Machine translation (Meta M2M100); the English original follows.
Learning latent dynamics is fundamental to building action-conditioned world models for planning. Among existing families, Joint Embedding Predictive Architectures (JEPAs) offer a natural framework by modeling transitions directly in latent space rather than at the pixel level. However, two issues remain in JEPA-based world models: (i) physical grounding lacks explicit aggregation of temporal context and (ii) action planning always overly relies on generic sampling distributions. To this end, we propose $\mathcal{G}$-JEPA, which incorporates temporal context into physical grounding for latent
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