One Fact, Many Places: Redundant Evidence in Long Visual Documents Breaks Single-Gold Evaluation of Retrieval, Citation and Attribution

Xiaokang Yuan, Binhui Yao, Luwei Yang, Ruimin Chen, Zeng Wang
10/19/2026
cs.CV

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Abstract

Document understanding is increasingly judged on where an answer comes from, scored against annotated gold locations as if each fact lived in one place. In long visual documents, a fact often lives in several places: a chart value is restated in the text, a key figure reappears in the summary. We ask how often such copies occur in standard benchmarks and what they do to evaluation. We annotate 420 questions from MMLongBench-Doc and MMDocRAG with the locations that independently establish each required fact, checked by two independent reviewers. Copies are common and often cross-modal: 19.5% of MMLongBench-Doc test questions have a strict copy outside the gold, and in 28% of MMDocRAG questions a non-gold candidate alone supplies a required fact. Single-gold scoring then fails in three predictable ways. A retriever that finds a copy is scored as a miss: page recall@1 of text and visual retrievers is underestimated by 13-24 points on questions with a cross-page copy. A model that cites a copy is scored as citing wrongly: crediting equivalent evidence reverses 11% of pairwise orderings among 65 competitive MMDocRAG systems. Masking the gold region leaves the fact available elsewhere: with a copy in the input, 41-56% of correct answers across four models survive it yet fail once every annotated instance is masked. Set-level masking with automatically found copies closes 78% of the gap to masking every annotated instance at matched granularity (three models); an unverified key-string rule lowers survival at least as far but masks three times the area. We release the annotations and redundancy-aware metrics.

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Cite this paper

@article{yuan2026fact,
  title  = {One Fact, Many Places: Redundant Evidence in Long Visual Documents Breaks Single-Gold Evaluation of Retrieval, Citation and Attribution},
  author = {Xiaokang Yuan and Binhui Yao and Luwei Yang and Ruimin Chen and Zeng Wang},
  year   = {2026},
  eprint = {2610.26401},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.26401v1}
}

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