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MLLMs struggle with visual prompts, showing a 17.8-point accuracy drop when questions are embedded in images, revealing a critical semantic gap in multimodal reasoning.
Continuous latent visual representations can drastically improve multimodal reasoning efficiency and performance, outpacing traditional discrete output methods.
