OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
Savi++: Towards end-to-end object-centric learning from real-world videos
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IA-JEPA applies motion-centric masking in JEPA to focus on entity interactions, reporting 14.26% causal reasoning accuracy on CLEVRER versus 3.22% for standard baselines plus higher latent entropy and R²=0.43 energy linearization.
citing papers explorer
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OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
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Entity-Centric World Models: Interaction-Aware Masking for Causal Video Prediction
IA-JEPA applies motion-centric masking in JEPA to focus on entity interactions, reporting 14.26% causal reasoning accuracy on CLEVRER versus 3.22% for standard baselines plus higher latent entropy and R²=0.43 energy linearization.