CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding

Abstract: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 training, CausalEmbed encourages the embedding models to learn compact and well-structured representations. Our method enables efficient VDR tasks using only dozens of visual tokens, achieving a 30-155x reduction in token count while maintaining highly competitive performance across various backbones and benchmarks. Theoretical analysis and empirical results demonstrate the unique advantages of auto-regressive embedding generation in terms of training efficiency and scalability at test time. As a result, CausalEmbed introduces a flexible test-time scaling strategy for multi-vector VDR representations and sheds light on the generative paradigm within multimodal document retrieval. Our code is available at this https URL.

Computation and Language (cs.CL)

arXiv:2601.21262 [cs.CL]

(or arXiv:2601.21262v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.21262 Focus to learn more arXiv-issued DOI via DataCite

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