A paper titled "Spatial Harness: Grounding Spatial Reasoning in Executable World States with Physics Engines" proposes grounding vision-language models' spatial reasoning in executable world states built with physics engines rather than model-internal inference. The work targets a known weakness: VLMs typically perform both scene interpretation and viewpoint-change reasoning internally, forming an implicit scene interpretation from partial views. The paper's authors and institution are not identified in the available text.
Spatial reasoning requires understanding how objects and observers are arranged in a scene and how their relations change with viewpoint or movement. Vision-language models (VLMs) typically perform both steps through model-internal reasoning, forming an implicit scene interpretation from partial vie
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