How Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence

Title:How Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence

Abstract:Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answering, and related applications. 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 drops than area-matched erasure. These results shift DLA robustness evaluation from footprint-based measurement toward structure-aware vulnerability auditing.

23 pages, 7 figures. Accepted to EMNLP 2026 Main Conference. Code: this https URL

Computation and Language (cs.CL)

arXiv:2605.19309 [cs.CL]

(or arXiv:2605.19309v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.19309 Focus to learn more arXiv-issued DOI via DataCite

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