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Current video generation models struggle with visual reasoning, with the best achieving only 51% accuracy on a new benchmark designed to probe their capabilities.
Current video understanding benchmarks and post-training datasets are riddled with linguistic biases, meaning VLMs might be acing tests without actually "watching" the video.
