Consistent Yet Wrong: Evidence Insensitivity in Spatial Vision-Language Models
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 ...
| Authors | S Divakar Bhat, Toshihiko Yamasaki |
|---|---|
| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 29 June 2026 |
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