SOURCE-LINKED INTELLIGENCE
MAGIC: Marginal-Guided Compression with Optimal Transport for Efficient Visual Document Retrieval
Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained evidence matching but incur substantial index storage and MaxSim scoring overhead. Post-hoc merging offers a practical route to efficient VDR by reducing this cost without retraining the retriever, but its uniform reconstruction objectives are poorly aligned with the sparse, non-uniform patch usage induced by late-interaction retrieval. Under aggressive compression, this misalignment can preserve rarely used patches while concentrating retrieval acti
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-17T19:14:06.000Z
First collected: 2026-09-23T14:01:59.594Z. This is not the publication date.