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The paper introduces S^2VOPD, a self-supervised method that improves vision-language models by distilling from original images into strongly augmented student views, achieving performance surpassing GPT-5.4 on benchmarks.
Introduces iVGR, a reinforcement learning framework that internalizes visual localization into textual reasoning for multimodal language models, eliminating the need for explicit visual grounding during inference while improving fine-grained perception performance.