World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a useful abstraction, they are not sufficient to capture interaction-dependent dynamics. We therefore propose C-JEPA, a simple and flexible object-centric world model that extends masked joint embedding prediction from image patches to object-centric representations. By masking object-level latents and requiring each masked object state to be inferred from the surrounding context, C-JEPA imposes structured partial observability during training, creating counterfactual-like prediction queries that discourage shortcut solutions and make interaction-dependent prediction necessary under the learning objective. Empirically, C-JEPA leads to consistent gains in visual question answering, with an absolute improvement of about 20% in counterfactual reasoning over the same architecture without object-level masking. On agent control tasks, C-JEPA enables substantially more efficient planning by using only 1% of the total latent input features required by patch-based world models, while achieving comparable performance. Finally, we provide a formal analysis demonstrating that object-level masking induces useful inductive bias by controlling observability. Our code is available at https://github.com/galilai-group/cjepa.

翻译：世界模型需要稳健的关系理解能力以支持预测、推理与控制。尽管以对象为中心的表示提供了有用的抽象，但不足以捕捉基于交互的动态特性。为此，我们提出C-JEPA——一种简洁且灵活的以对象为中心的世界模型，将遮蔽联合嵌入预测从图像块扩展到对象为中心的表示。通过对对象级潜在变量进行遮蔽，并要求从周围上下文中推断每个被遮蔽的对象状态，C-JEPA在训练过程中引入了结构化的部分可观测性，从而产生类似反事实的预测查询，遏制捷径解，并使学习目标在交互依赖的预测中成为必要。实验表明，与未采用对象级遮蔽的相同架构相比，C-JEPA在视觉问答任务中实现了一致性增益，反事实推理的绝对性能提升约20%。在智能体控制任务中，C-JEPA仅需基于块的世界模型所需总潜输入特征的1%，即可实现可比性能，并显著提升规划效率。最后，我们通过形式化分析证明，对象级遮蔽通过控制可观测性引入了有效的归纳偏置。代码已开源至https://github.com/galilai-group/cjepa。