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FLOWREADER uses min-cost flow for multimodal long document Q&A

By PulseAugur Editorial · [3 sources] ·

Researchers have developed FLOWREADER, a novel method for question answering over long, multimodal documents. This approach reframes evidence assembly as a min-cost flow problem, enabling better handling of fragmented information across text, tables, and slides. FLOWREADER outperforms traditional top-k retrieval methods on specific subsets of the VisDoMBench benchmark, demonstrating its effectiveness in complex evidence assembly scenarios. AI

IMPACT Introduces a novel approach to multimodal Q&A, potentially improving performance on complex documents.

RANK_REASON The cluster contains a research paper detailing a new method for question answering.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

FLOWREADER uses min-cost flow for multimodal long document Q&A

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The cluster contains a research paper detailing a new method for question answering.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Ambuj Mehrish, Sebatiano Vascon ·

    FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

    arXiv:2606.07235v1 Announce Type: cross Abstract: Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken across cells in a long table, spread over multiple slides, or split between a figure …

  2. arXiv cs.LG TIER_1 English(EN) · Sebatiano Vascon ·

    FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

    Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken across cells in a long table, spread over multiple slides, or split between a figure and its discussion. Top-$k$ chunk retrieval treats…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sebastiano Vascon ·

    FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

    Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken across cells in a long table, spread over multiple slides, or split between a figure and its discussion. Top-$k$ chunk retrieval treats…

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