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arXiv · 2606.06714

Anchored, Not Graded: Vision-Language Models Fail at Slant-from-Texture Perception

Qian Zhang·Michal Golovanevsky·Fulvio Domini·James Tompkin
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Abstract

Human perception of surface slant from texture exhibits systematic, graded biases that emerge reliably in psychophysical experiments. Prior work showed that unsupervised CNNs reproduce several human-like biases, while supervised CNNs do not. Do Vision-Language Models (VLMs) exhibit similar competences? Across multiple VLM families and model scales, zero-shot and in-context prompting both produce distinctive failures: slant is predicted at only a small set of anchors (e.g., 0\degree, $\pm$25\degree, $\pm$45\degree) with little dependence on stimulus field of view, optical slant, or surface curvature. Supervised fine-tuning partially remediates the failure, but residual anchoring persists. While success in high-level vision-language benchmarks might not require sensitivity to low-level geometric cues, we interpret anchoring as a failure at the representation-to-output language interface: not necessarily an absence of geometric encoding, but a failure to express it in a graded form.

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Qian Zhang, Michal Golovanevsky, Fulvio Domini, James Tompkin. 2026-06-04. Anchored, Not Graded: Vision-Language Models Fail at Slant-from-Texture Perception. https://arxiv.org/abs/2606.06714

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