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The Visual Insensitivity Gap: Diagnosing When Vision-Language Models Fail to Use Visual Evidence
Vision-language models are evaluated by aggregate accuracy on multimodal benchmarks, a practice that implicitly assumes the model uses its visual input. We show this assumption fails on 40%--97% of samples across six VLMs and three perceptual benchmarks: blurring the question-relevant visual region
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