Language-guided dynamic scene understanding requires identifying one physical entity across time, metric 3D space, and multiple viewpoints, a setting not captured by current multimodal reasoning or tracking paradigms. We introduce 4DVLT, an offline calibrated multi-view task that unifies grounding with metric 3D and synchronized 2D tracking through a worldline binding identity, motion, visibility, and observations. We construct Instruct-4D, with 129.4K QA pairs, 64.7K targets, 851 scenes, and 9 reasoning types, and propose 4DTrack for query-conditioned inference over a 4D state graph. 4DTrack reaches 55.18 WQS, exceeding the strongest adapted VLT baseline by 33.01 points while improving grounding and trajectory quality. The project page is available at https://github.com/mikubaka88/4DVLT.

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