CVT-Bench: Probing Spatial-State Integrity through Counterfactual Viewpoint Transformations
Multimodal large language models (MLLMs) perform strongly on isolated spatial tasks, but whether their predictions remain persistent and mutually coherent across viewpoints and competing scenes is unclear. We formalize this behavioral property as spatial-state integrity and introduce CVT-Bench, a factorial diagnostic suite spanning two domains (CVT-Synthetic, CVT-Real), four context regimes (CVT-Isolated, irrelevant filler, attribute filler, CVT-Competing), three representations (Image, Text/BBox, Scene Graph), and ten viewpoint conditions (nine azimuths from $0^\circ$ to $360^\circ$ at $45^\circ$ increments, plus Top) with target views withheld. Across 200 scenes and 11,833 relational qu...
| Authors | Shanmukha Vellamcheti, Uday Kiran Kothapalli, Disharee Bhowmick et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 March 2026 |
| Last updated at source | 14 August 2026 |
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