[2606.02742] Consistent Yet Wrong: Evidence Insensitivity in Spatial Vision-Language Models
[Submitted on 1 Jun 2026]
Title:Consistent Yet Wrong: Evidence Insensitivity in Spatial Vision-Language Models
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Abstract:Spatial reasoning is fundamental to robotics, autonomy, and embodied AI, yet modern vision-language models (VLMs) remain unreliable on metric distance queries. A common assumption is that consistent predictions across viewpoints reflect geometric grounding. We test this assumption and find the opposite: leading VLMs often produce view-invariant and consistent answers even when those answers are incorrect, indicating weak coupling between predictions and viewpoint-specific visual evidence.
We introduce \textbf{ViewDiag}, a controlled multi-view evaluation protocol built from Hypersim, ScanNet, and KITTI360, comprising 176 object-pair tracks across 80 scenes with 2--10 views per track. The protocol evaluates models along three axes: metric accuracy, distributional concentration, and a latent feature probe for internal collapse that distinguishes decision collapse from representation collapse. Across diverse models, we observe a consistent pattern of high prediction stability paired with substantial error, clustering in a regime characterized by strong consistency but low accuracy.
\noindent These results challenge the common use of cross-view consistency as a proxy for geometric understanding. Instead, we show that stable predictions may reflect prior-driven collapse rather than evidence-sensitive reasoning. ViewDiag provides a controlled benchmark and diagnostic framework for evaluating spatial VLMs beyond accuracy alone. The code and data can be found \href{this https URL}{here}
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.02742 [cs.CV]
(or arXiv:2606.02742v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.02742
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: S Divakar Bhat [view email] [v1] Mon, 1 Jun 2026 18:06:08 UTC (5,364 KB)
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