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New SCOUT framework drastically cuts visual document search time

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SCOUT, a novel cascaded retrieval framework designed to enhance the efficiency of searching visually rich documents. This system employs a two-track approach, utilizing a lightweight retriever for initial corpus filtering followed by a stronger reranker on a smaller candidate pool. Simultaneously, a semantic track processes offline summaries to complement visual matching. The framework fuses these signals through cross-modal query refinement, significantly improving query-time latency by up to 80.7x while also boosting retrieval performance. AI

IMPACT This framework could significantly speed up information retrieval from complex visual documents, benefiting researchers and anyone working with large, data-rich texts.

RANK_REASON This is a research paper describing a new technical framework for information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SCOUT framework drastically cuts visual document search time

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This is a research paper describing a new technical framework for information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Diana Turmakhan, Aseem Srivastava, Vivek Gupta, Iryna Gurevych, Thamar Solorio ·

    Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search

    arXiv:2610.20367v1 Announce Type: new Abstract: Retrieving information from visually rich documents requires matching queries against pages that contain complex evidence in tables, figures, and layout-dependent structures. Current vision-language retrievers address this but incur…

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