AI RESEARCH
CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding
arXiv CS.CL
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ArXi:2601.21262v3 Announce Type: replace Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality multi-vector embeddings, the substantial storage overhead caused by representing a page with thousands of visual tokens limits their practicality in real-world applications. To address this challenge, we propose an auto-regressive generation approach, CausalEmbed, for constructing multi-vector embeddings. By incorporating iterative margin loss during contrastive.