How Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910260186316800 |
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| author | Chen, Yue Wang, Yihao Tang, Ziyi Zheng, Yongsen Wang, Keze |
| author_facet | Chen, Yue Wang, Yihao Tang, Ziyi Zheng, Yongsen Wang, Keze |
| contents | Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answering, and other document intelligence systems, yet their robustness evaluation remains largely area-centric. We identify this Footprint Bias and propose ProSA, a lightweight output-level auditing framework that decouples controlled probing, policy-driven targeting, and structure-aware diagnosis. ProSA combines Block-level Structural Loss Rate (B-SLR), granularity-aware exposure descriptors, and pathway attribution to analyze where structural identity is lost, at what exposure granularity failures emerge, and how failures propagate. Across MinerU and PP-StructureV3 on 1,000 pages, affected area weakly tracks perturbation-induced OCR instability (R^2=0.384/0.110), whereas B-SLR aligns much more closely with it (R^2=0.727/0.916). Exposure descriptors further separate occlusion- and topology-dominant pathways, while matched-footprint structural probes cause much larger downstream QA/retrieval degradation compared to area-matched erasure. These results shift DLA robustness evaluation from footprint-based stress testing toward structure-aware vulnerability auditing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19309 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence Chen, Yue Wang, Yihao Tang, Ziyi Zheng, Yongsen Wang, Keze Computation and Language Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answering, and other document intelligence systems, yet their robustness evaluation remains largely area-centric. We identify this Footprint Bias and propose ProSA, a lightweight output-level auditing framework that decouples controlled probing, policy-driven targeting, and structure-aware diagnosis. ProSA combines Block-level Structural Loss Rate (B-SLR), granularity-aware exposure descriptors, and pathway attribution to analyze where structural identity is lost, at what exposure granularity failures emerge, and how failures propagate. Across MinerU and PP-StructureV3 on 1,000 pages, affected area weakly tracks perturbation-induced OCR instability (R^2=0.384/0.110), whereas B-SLR aligns much more closely with it (R^2=0.727/0.916). Exposure descriptors further separate occlusion- and topology-dominant pathways, while matched-footprint structural probes cause much larger downstream QA/retrieval degradation compared to area-matched erasure. These results shift DLA robustness evaluation from footprint-based stress testing toward structure-aware vulnerability auditing. |
| title | How Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.19309 |