MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework
arXiv:2606.07654v1 Announce Type: cross Abstract: Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language Models (VLMs), but this design makes deployment expensive in both storage and computational overhead. Existing efficiency techniques usually optimize only part of this budget, leaving multimodal retrievers without a unified way to trade accuracy for both vector width and encoder depth. Therefore, we propose MM-Matryoshka, a 2D Matryoshka training framework for budget-elastic Visual Document Retri
The proliferation of advanced deep Vision-Language Models (VLMs) and multi-vector retrieval systems necessitates more efficient deployment solutions as computational and storage costs become significant barriers.
This development allows for more budget-elastic and efficient deployment of powerful visual document retrieval systems, making advanced AI capabilities more accessible and scalable.
The ability to dynamically adjust the trade-off between retrieval accuracy and resource consumption based on available budget changes how multi-vector visual document retrievers can be deployed and utilized.
- · AI developers
- · Cloud providers
- · Enterprises with large document stores
- · SaaS companies leveraging visual search
- · Companies relying on inefficient VLM deployment
- · Legacy retrieval systems
Reduced operational costs and increased accessibility for VLM-based visual document retrieval.
Accelerated adoption of advanced visual search capabilities across industries due to improved cost-efficiency.
Potential for new business models built on highly scalable and cost-effective visual AI, blurring lines between data storage and intelligent retrieval.
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Read at arXiv cs.AI