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PointWAM introduces a 3D world action model that decomposes the world into scene and hands, jointly forecasting both as 3D point trajectories to enable dexterous robotic manipulation. Pre-training on human demonstration videos boosts DexJoCo success by 56.9 points, surpassing prior state-of-the-art by 11.7 points and outperforming strong VLAs on a real robot.