CounterCount: Diagnosing Knowledge Bias in How Vision-Language Models Count Attributes

arXiv:2605.17826v2 Announce Type: replace-cross Abstract: Do Vision-Language Models (VLMs) count what they see, or what they know about the world? We introduce CounterCount, a diagnostic dataset of paired factual and counterfactual (CF) images, in which the CF image contains an atypical count of a subject's attribute (e.g., a rabbit with four ears), together with annotations localizing the counted attributes. Humans answer both types of images nearly perfectly ($98.9\%$ and $97.5\%$), but across 16 VLMs (14 open-weight and 2 closed-source), average accuracy drops from $91.6\%$ on factual to $4

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