Mirage Probes: How Vision Models Fake Visual Understanding
Vision-language models (VLMs) confidently answer image-based questions even without images, inflating benchmark scores without reflecting visual grounding. This study classifies mirages into two regimes: textual biases (relying on language priors) and spurious images (constructing false visual content in latent space). Using a contrastive probing framework and a Prior Harnessing Index (PHI), the authors show these regimes are distinct and require different mitigation strategies—text cleaning addresses only the first, while faithful grounding needs representational interventions.
[2606.13870] Mirage Probes: How Vision Models Fake Visual Understanding
[Submitted on 11 Jun 2026]
Title:Mirage Probes: How Vision Models Fake Visual Understanding
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Abstract:Vision-language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided. This mirage behavior inflates benchmark scores without reflecting visual grounding. Prior work treats this as a single failure mode. We argue it is two. Using Mirage Probes, a contrastive probing framework that pairs paraphrased question variants with matched mirage and non-mirage labels on the same image, we show that mirage behavior is linearly decodable from internal activations across residual stream, MLP, post-attention, and attention-head sites in two open-source VLMs. We demonstrate that a Naive Bayes text baseline cannot recover this signal, ruling out surface lexical confounds. Cross-benchmark separability patterns, together with a novel Prior Harnessing Index (PHI) measuring how much a model can answer from text alone, expose two distinct regimes: textual biases, where the model answers from language priors without engaging visual representations, and spurious images, where it constructs false visual content in latent space and answers as if grounded. The distinction has direct mitigation consequences: text-distribution cleaning can address the first regime but cannot reach the second, since spurious-image mirages live in the model's visual representations rather than its text. Faithful visual grounding will require interventions at the representational level.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.13870 [cs.CV]
(or arXiv:2606.13870v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.13870
arXiv-issued DOI via DataCite (pending registration)
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From: Daniel Ben-Levi [view email] [v1] Thu, 11 Jun 2026 19:51:44 UTC (629 KB)
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