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Visual identity discrimination is the missing link in Universal Multimodal Embeddings, and this work provides a robust framework to integrate it seamlessly.
VC-Tooler achieves unprecedented adaptability in visual tool use, outperforming existing models by 60% in agentic reasoning tasks.
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.
