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Binjiang Institute of Zhejiang University
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Dynamo achieves a remarkable 5.6% average accuracy boost in visual reasoning tasks without retraining, revolutionizing how VLMs adapt in real-time.
VLMs and VGMs reveal a surprising complementarity in spatial intelligence tasks, with a simple fusion of their features outperforming either model alone.
MLLMs still fail at complex visual workflows: even the best models struggle to navigate multi-step reasoning chains involving visual conditions, achieving only 53.33 Path F1 on the new MM-CondChain benchmark.
