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Spatial-Interactor: Learning Spatial Reasoning through Interaction with the Observable Physical World

The paper introduces Spatial-Interactor, a framework for training vision-language models (VLMs) to model physical-world state transitions through interaction. It uses a three-level curriculum and a dataset of simulated and real interaction trajectories to improve spatial reasoning in dynamic environments.

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PublishedSeptember 19, 2026Kaixiang Yao, Xu Wang, Miao Pan
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WHY IT MAY MATTER

This work addresses the challenge of improving spatial reasoning in vision-language models by leveraging interaction data and a structured curriculum, which could enhance their ability to understand and act in dynamic environments.

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