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MINER module enhances multimodal document retrieval by fusing internal transformer signals

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have introduced MINER, a novel plug-in module designed to enhance the efficiency of visual document retrieval. MINER probes and fuses internal representations from transformer layers into a single compact embedding, addressing the trade-off between quality and efficiency in existing retrieval methods. This approach aims to improve retrieval accuracy without increasing storage or latency, outperforming current dense single-vector retrievers on several benchmarks. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT MINER could lead to more efficient and accurate visual document search systems, reducing costs for platforms that handle large volumes of visual data.

RANK_REASON This is a research paper detailing a new method for improving retrieval efficiency in visual documents.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Weien Li, Rui Song, Zeyu Li, Haochen Liu, Gonghao Zhang, Difan Jiao, Zhenwei Tang, Bowei He, Haolun Wu, Xue Liu, Ye Yuan ·

    MINER: Mining Multimodal Internal Representation for Efficient Retrieval

    arXiv:2605.06460v1 Announce Type: new Abstract: Visual document retrieval has become essential for accessing information in visually rich documents. Existing approaches fall into two camps. Late-interaction retrievers achieve strong quality through fine-grained token-level matchi…

  2. arXiv cs.LG TIER_1 · Ye Yuan ·

    MINER: Mining Multimodal Internal Representation for Efficient Retrieval

    Visual document retrieval has become essential for accessing information in visually rich documents. Existing approaches fall into two camps. Late-interaction retrievers achieve strong quality through fine-grained token-level matching but store hundreds of vectors per page, incur…

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