Researchers introduced RoTA-Bench, a benchmark that evaluates vision-language models on interactive 3D spatial reasoning by requiring them to turn 2D observations into 3D understanding, infer object geometry, anticipate the effects of rotation, and refine actions through visual feedback. The benchmark targets the ability to support precise action, a capability the authors frame as central to whether such models can operate reliably in 3D tasks.
Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipating the effects of rotation, and refining actions through visual feedback. We introduce RoTA-Bench to evaluate these abilities through interactive 3D-
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