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VC-Tooler achieves unprecedented adaptability in visual tool use, outperforming existing models by 60% in agentic reasoning tasks.
Bridging the gap between synthetic and real-world motion prediction, this framework achieves superior performance by leveraging objectness priors to refine motion labels.
VLMs reason better when shown visually similar examples with synonymous questions, enabling a new self-training approach that beats state-of-the-art visual reasoning datasets.
