Unveiling the Visual Counting Bottleneck in Vision-Language Models
While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visual counting. In this work, we investigate this extrapolation bottleneck by deconstructing visual counting into three cognitive stages: visual individuation, magnitude awareness, and symbolic mapping. Using synthetic Go boards and linear probes, we demonstrate that visual backbones maintain robust, linearly separable representations of quantity well into the extrapolation regime, ruling out perceptual failure. Furthermore, models retain latent magnitude awareness, successfully performing comparative reasoning on quantities they fail to enumer...
| Authors | Xingzhou Pang, Yifan Hou, Junling Wang et al. |
|---|---|
| Primary category | cs.MM |
| Published | 28 May 2026 |
| Last updated at source | 8 July 2026 |
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