Don't Look at the Numbers: Visual Anchoring Bias and Layer-wise Representation in VLMs

M. Shalankin

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arXiv · 2605.11218

Don't Look at the Numbers: Visual Anchoring Bias and Layer-wise Representation in VLMs

M. Shalankin

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Abstract

Embedded numeric anchors on images systematically bias Vision-Language Model quality judgments across six VLMs from five architectural families (ANOVA eta^2 = 0.18-0.77, all p < 0.001). Anchor effects are 2.5x larger than severe image quality degradation, confirming bias is not reducible to visual changes. Layer-wise probing reveals consistent dissociation: layers where anchor classification saturates (L12-L34) are suboptimal for quality prediction, with optimal layers deeper (R^2 = 0.69-0.91). Fusion analysis identifies architecture-dependent integration -- instant fusion at L1-L2 in two models versus partial or no fusion in three others. These results establish a causal account of visual anchoring bias, linking behavioral susceptibility to representation dynamics.

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M. Shalankin. 2026-05-11. Don't Look at the Numbers: Visual Anchoring Bias and Layer-wise Representation in VLMs. https://arxiv.org/abs/2605.11218

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