arXiv:2605.19309v2 Announce Type: replace 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 pathw
The paper identifies systemic vulnerabilities in Document Layout Analysis (DLA) pipelines, which are critical components of current AI document intelligence systems leveraging generation and retrieval.
A strategic reader should care because vulnerabilities in core DLA impact the reliability and trustworthiness of AI systems used for critical tasks like legal document processing or financial analysis.
This introduces a standardized framework for auditing DLA robustness, potentially leading to more secure and reliable AI document intelligence applications.
- · AI robustness researchers
- · Enterprises reliant on document intelligence
- · Developers of robust DLA solutions
- · Developers of fragile DLA pipelines
- · AI systems with un-audited DLA components
Improved reliability and trust in AI systems using DLA for document processing and retrieval.
Increased pressure on AI developers to integrate robust auditing frameworks early in their development cycles.
The emergence of specialized 'AI auditors' focused on structural vulnerabilities in complex AI pipelines.
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Read at arXiv cs.CL