GST-Bench evaluates whether vision-language models can integrate long video streams into a globally consistent spatial representation. Its human-verified VQA questions are derived from 6,790 minutes of synthetic video and require reasoning from unseen viewpoints and mapping egocentric observations onto top-down images. Across 22 state-of-the-art VLMs, the strongest zero-shot result reported is 42.68, compared with a human score of 79.08. The authors also introduce GST-Bench-Local to distinguish local perception from global integration failures, plus GST-Train as a training resource for global spatial reasoning.
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