World models succeed when their latent states are built to meet task-specific sufficiency constraints rather than preserving the maximum amount of information.
Causal- jepa: Learning world models through object-level latent interventions
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CausalVAE plug-in for world models preserves factual prediction and boosts counterfactual retrieval, with large gains on physics benchmarks and recovered physical interaction trends.
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Latent State Design for World Models under Sufficiency Constraints
World models succeed when their latent states are built to meet task-specific sufficiency constraints rather than preserving the maximum amount of information.
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CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics
CausalVAE plug-in for world models preserves factual prediction and boosts counterfactual retrieval, with large gains on physics benchmarks and recovered physical interaction trends.
- LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels