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New benchmark VL-DocIR targets vision-based long document retrieval challenges

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced VL-DocIR, a new benchmark designed to improve vision-based long document information retrieval. This benchmark addresses the limitations of current systems that focus on short documents by simulating realistic scenarios where evidence may span multiple pages or documents. VL-DocIR comprises over 388,000 page images from sources like Wikipedia, arXiv, and PubMed, along with more than 270,000 queries across various domains and evidence structures. Initial evaluations show that while leading visual retrieval systems perform well on single-page queries, retrieving information that spans multiple pages or documents remains a significant challenge. AI

IMPACT This benchmark aims to push the boundaries of information retrieval systems, potentially leading to more effective tools for accessing and synthesizing information from extensive visual document collections.

RANK_REASON The item describes a new benchmark dataset and evaluation for a specific AI research problem (vision-based long document retrieval). [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New benchmark VL-DocIR targets vision-based long document retrieval challenges

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The item describes a new benchmark dataset and evaluation for a specific AI research problem (vision-based long document retrieval). [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Simon Ott, Yufan Chen, Ruiping Liu, Junwei Zheng, Jiale Wei, Di Wen, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen ·

    VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval

    arXiv:2610.17057v1 Announce Type: new Abstract: Vision-based document retrieval has improved rapidly, but current evaluation settings still emphasize short documents and single relevant pages. This leaves an important gap between leaderboard performance and realistic long-documen…

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