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VLMs are prone to critical failures that vary significantly across cultures, exposing the inadequacy of Western-centric safety benchmarks.
VLMs can be effectively adapted, even under data and compute constraints, to create a unified evaluator for video world models that rivals task-specific models and aligns well with human judgment.
