Abstract: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.
| Comments: | 18 pages, 5 figures, preprint |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.19309 [cs.CL] |
| (or arXiv:2605.19309v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19309 arXiv-issued DOI via DataCite |
From: Yue Chen [view email]
[v1]
Tue, 19 May 2026 03:44:09 UTC (2,917 KB)
[v2]
Tue, 26 May 2026 13:13:58 UTC (2,369 KB)
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