This paper develops a framework to evaluate spatial cognition in image-generation models by asking them to draw or mark answers in a visual space, rather than relying on text-based inputs. Practitioners might care about this research to better understand how image-generation models think spatially and to improve their performance on tasks that require spatial reasoning.
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