Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?

arXiv:2605.30557v2 Announce Type: replace-cross Abstract: Spatial reasoning benchmarks typically evaluate whether vision-language models can derive the correct answer from a visual observation. Yet in real 3D environments, the observation itself may be unreliable: occlusion can remove task-relevant evidence, while perspective can make visible geometry misleading. Reliable spatial reasoning therefore requires more than answering a question correctly. A model must also assess whether its current observation provides sufficient and trustworthy evidence for that answer. We introduce SPATIALUNCERTA

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