The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs
Despite strong performance on many multimodal tasks, vision-language models (VLMs) still struggle with basic object counting. We investigate whether this reflects missing internal knowledge or a gap between internal representations and verbalized outputs. Training simple probes on activations from four VLMs across five counting datasets reveals that nonlinear probes can reliably detect counting errors, suggesting that VLMs often encode the correct count even when they output the wrong answer. SVCCA analysis shows that probes trained on ground-truth counts and probes trained on model outputs occupy a partially shared activation subspace but read out along misaligned directions. We further ...
| Authors | Ahmed Oumar El-Shangiti, Abzal Nurgazy, Hilal AlQuabeh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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