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Giraffe introduces a novel mapping architecture that translates hidden text representations to visual embeddings using a single token per image, enabling efficient graphic design generation in multimodal large language models.
This paper introduces Lexical Consensus, a framework for studying how artificial agents acquire and stabilize grounded word meanings using visual embeddings and lexical learners, finding that perceptual distance rather than semantic relatedness drives acquisition success.
Deployment of Meta's DINOv3 ViT-L/16 distilled model on Replicate, providing efficient visual embeddings for tasks like similarity search and retrieval.