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The Causal Semantic World Action Model (CSWAM) is presented, which augments FastWAM with a causal semantic expert built on V-JEPA 2.1, which provides temporally grounded representations of semantic state changes and motion with less dependence on appearance-specific details.
DSWAM is introduced, a Dual-System World Action Foundation Model for fine-grained robot manipulation and built and evaluated under the DeMaVLA real-world deformable manipulation setting with matched robot platform, pretraining data, post-training data, and evaluation criteria.
MECo-WAM is proposed, a Multi-Expert Co-Training World Action Model that injects action-relevant 4D geometric priors into video-action representations while preserving the original lightweight inference graph.
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