Causal-JEPA: Learning World Models through Object-Level Latent Interventions
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 applying object-level masking that requires an object's state to be inferred from other objects, C-JEPA induces latent interventions with counterfactual-like effects and prevents shortcut solutions, making interaction reasoning essential. Empirically, C-JEPA leads to consistent gains in visual question answering, with an absolute improvement of about 20\% in counterfactual reasoning compared to 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 a causal inductive bias via latent interventions. Our code is available at https://github.com/galilai-group/cjepa.
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Heejeong Nam
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2026-02-18T13:45:40.276Z
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Heejeong Nam
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Quentin Le Lidec
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2026-02-18T13:45:46.221Z
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Quentin Le Lidec
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Lucas Maes
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2026-03-17T08:21:35.078Z
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Lucas Maes
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Yann LeCun
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2026-02-18T13:46:02.392Z
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Yann LeCun
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Randall Balestriero
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2026-02-18T13:45:55.816Z
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Randall Balestriero
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RandallBalestriero
Causal-JEPA: Learning World Models through Object-Level Latent Interventions
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 applying object-level masking that requires an object's state to be inferred from other objects, C-JEPA induces latent interventions with counterfactual-like effects and prevents shortcut solutions, making interaction reasoning essential. Empirically, C-JEPA leads to consistent gains in visual question answering, with an absolute improvement of about 20\% in counterfactual reasoning compared to 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 a causal inductive bias via latent interventions. Our code is available at https://github.com/galilai-group/cjepa.
C-JEPA extends masked joint embedding prediction to object-centric representations, enabling robust relational understanding through object-level masking that induces causal inductive biases and improves reasoning and control tasks.
object-centric representations masked joint embedding prediction counterfactual reasoning latent interventions causal inductive bias visual question answering agent control tasks