[Submitted on 28 Sep 2026]
Title:One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models
View a PDF of the paper titled One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models, by Aditya Sharma and 1 other authors
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Abstract:Contrastive vision-language models learn shared embedding spaces by aligning matched image-text pairs, yet their representations remain separated by a modality gap. Prior work reports divergent effects of modifying this gap: reducing it can improve zero-shot classification and cross-modal alignment, whereas removing gap-related structure can degrade image-text retrieval. In this paper, we provide a unified geometric explanation for these task-dependent effects. Across CLIP and SigLIP encoders, we find that a single dominant direction captures 94.4-99.9% of the squared norm of the image-text mean separation, revealing that the mean-separation component is approximately rank-one. A decomposition of the similarity score then identifies three task-specific roles. In zero-shot classification, query-side fixed gap-offset subtraction is exactly equivalent to an additive class bias. In standard cross-modal retrieval, projecting out the gap direction and renormalising residuals discards candidate-specific norm information, inducing a multiplicative ranking distortion; a geometry-derived exponent tracks the grid-search optimum (Spearman rho = 0.93) and restores performance in some settings, although the gains transfer unevenly. In mixed-modal retrieval, the gap direction sorts candidates by modality; its removal can improve cross-modal ranking, unlike random or non-gap controls. Residual semantic structure after removal defines the limits of the rank-one account. Together, these results explain why gap modification can improve, degrade, or restore performance across downstream settings. By clarifying when and why gap modification changes model behavior, this account provides a principled basis for selecting gap interventions in similarity-based vision-language systems across evaluated downstream tasks.
Comments: 9 pages, 3 figures, 3 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2609.36101 [cs.CV]
(or arXiv:2609.36101v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.36101
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aditya Sharma [view email] [v1] Mon, 28 Sep 2026 18:40:27 UTC (312 KB)
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