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Nanyang Technological University
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A single generalist model outperforms specialized systems, achieving over 35% improvement in real-world robotic task success.
Visual Evidence Pre-Alignment boosts MLLM performance by ensuring models effectively utilize fine-grained visual evidence, rather than just relying on coarse captions.
