AdaMerge: Tuning-Free Patch Compression for Multi-Vector Visual Document Retrieval

arXiv:2609.22562 · cs.CV, cs.IR · Submitted 2026-09-18 · Read on arXiv

cs.CV, cs.IR

Submitted: 2026-09-18

Updated: 2026-09-18

Terminology

• Pruning — 15× in this paper · explained in 4 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Clustering — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Ablation — 3× in this paper · explained in 1 episode
• Redundancy — 3× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes

Sources

• ColPali: Efficient Document Retrieval with Vision Language Models
• Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings
• ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval
• MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
• Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework
• DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning

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