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.
Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C
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IA-JEPA applies interaction-aware masking to JEPA, raising causal reasoning accuracy on CLEVRER from 3.22% to 14.26% while producing a higher-entropy latent space that better aligns with physical energy.
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 interaction-aware masking to JEPA, raising causal reasoning accuracy on CLEVRER from 3.22% to 14.26% while producing a higher-entropy latent space that better aligns with physical energy.