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WALL-SS achieves unprecedented long-horizon visual simulation for robots, improving action following and trajectory accuracy while maintaining coherence over extended interactions.
By redefining action learning around semantic events, WALL-WM achieves unprecedented generalization across tasks and environments, outperforming traditional models.
