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MLLMs often struggle with reasoning due to a failure in dynamic cross-modal coordination, but DyCo-RL fixes this by optimizing attention shifts for better performance.
VLMs confidently hallucinate answers to spatial reasoning questions even when visual evidence is occluded or misleading, achieving near-random performance in identifying viewpoints that could resolve the ambiguity.
Pixel-space diffusion models get a serious boost: V-Co reveals a simple recipe for visual co-denoising that outperforms existing methods on ImageNet-256 with fewer training epochs.
