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Current MLLMs struggle with fine-grained spatial reasoning, achieving only 37.2 F1 on challenging tasks compared to human performance of 84.0 F1.
Generative training not only enhances a model's ability to manipulate objects in images, but also surprisingly strengthens its spatial reasoning skills.
A single model now rivals specialized vision-language models in understanding, while also generating and editing images, thanks to a unified discrete diffusion framework.
