WeVisDoc is a two‑stage data‑centric framework designed to improve end‑to‑end document parsing. Stage I expands coverage by adding heterogeneous data and applying structure‑preserving degradation synthesis, while Stage II evaluates residual errors with a held‑out probe and uses those diagnostics to target data construction and token budget reallocation. The resulting WeVisDoc‑4B model achieves an overall score of 95.38 on OmniDocBench v1.6 and outperforms competing parsers across all evaluated settings, with Stage II delivering notable gains on degraded tracks.
By Hao Yu, Kang Liu, Linnan Zhao, Jiabo Zhan, Chong Sun, Chen Li, Jing Lyu
Transforming table-form documents into machine-processable records requires recovering not only their visible content but also the multilevel structure that organizes it. However, existing benchmarks evaluate either holistic document outputs or conventional table grids, and their aggregate scores provide little insight into where structural failures occur.
arXiv:2609.09696v1 Announce Type: new
Abstract: Large language models are increasingly proposed as automated auditors of document quality, yet their reliability as detectors of planted errors is poor...
By Karan Parekh, Sanjana Pendyala Ravinder, Sana Mhapsekar, Medina Maloku
arXiv:2609.00470v1 Announce Type: new
Abstract: Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical at...
By Muhaimin Bin Munir, Akib Jawad Ononto, Nazia Shehnaz Joynab, Bhavani Thuraisingham, Latifur Khan
TriShieldRAG introduces a three‑layered defense for Retrieval‑Augmented Generation: an Ingest Guard that screens documents, a Retrieval Scorer that re‑ranks based on trust, and a Cross‑LLM Consensus that validates evidence across three models. Against the original PoisonedRAG attack on the 2.68M‑passage Natural Questions corpus, the framework reduces attack success from about 79% to 1%. However, adaptive attacks that only alter document formatting can bypass the Ingest Guard and still achieve high success rates, revealing limits of layered defenses that rely on the same retrieved evidence.
By Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj, Jeenal Chaudhary, Disha Singhania
The paper investigates the gap between retrieval and reading in document vision‑language models, showing that even when the correct page is retrieved, the model often fails to use the text on that page. By comparing answers derived from page images alone versus images plus extracted OCR text, the authors demonstrate that adding OCR text can improve strict accuracy by 13 to 16 points on their FoveDoc‑Bench benchmark. The study also reveals that OCR benefits textual evidence but not charts or figures, and that its advantage diminishes as retrieval quality worsens.
By Qingtao Xia, Siyao Cheng, Jiahua Bao, Jiaxing Du, Jie Liu
CiteVQA is a benchmark that evaluates multimodal large language models on both answering document questions and providing element‑level bounding‑box citations for the evidence used. It contains 1,897 questions across 711 PDFs in seven domains and two languages, with ground‑truth citations generated via an automated pipeline and strict quality control. The evaluation metric, Strict Attributed Accuracy (SAA), rewards a prediction only when both the answer and the cited region are correct, revealing widespread attribution hallucination in current models.
By Dongsheng Ma, Jiayu Li, Zhengren Wang, Yijie Wang, Jiahao Kong, Weijun Zeng, Jutao Xiao, Jie Yang, Bangrui Xu, Yuhan Wang, Bin Wang, Conghui He
arXiv:2608. 00144v2 Announce Type: replace Abstract: Membership inference (MIA) on language models is usually summarised by aggregate ROC-AUC, but such evaluations are confounded: model-free blind baselines can separate members from non-members using surface text alone.
By Victor Maricato
AtomCite is an agentic framework that verifies and corrects page‑level citations in multi‑page documents by parsing answers into claims, checking each claim against the cited page image, and applying a deterministic repair policy. The authors introduce DocCite, the first benchmark for this task, built on MP‑DocVQA and DUDE, containing 928 injected instances and 1,909 verified natural errors. Across Gemini, Claude, and GPT models, AtomCite achieves about 93% verification accuracy and improves citation precision from 34% to 87‑90%, while also enhancing hallucination detection in open‑source models.
By Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos
The paper introduces MOTe, a decomposable error metric for Document Layout Analysis (DLA) that addresses shortcomings of traditional IoU‑based F1 scores. It proposes the Structural Semantic Unit (SSU) to focus on semantic structure and demonstrates that MOTe correlates more strongly with parsing‑induced Character Error Rates (CER) than F1, providing reliable upper and lower bounds on CER for model predictions. A Python library is released to facilitate the adoption of MOTe in DLA projects.
By Jonathan Bourne, Mwiza Simbeye, Ishtar Govia
arXiv:2609.23592v1 Announce Type: cross
Abstract: End-to-end document parsers increasingly offer an optional reasoning mode for complex pages. On a 180-page entropy-stratified discovery sample with o...
By Xingyu Lin, Dehui Du
LeakageBench is a new benchmark comprising 500 document images with 11,954 GDPR‑aligned PII annotations, designed to evaluate document‑level redaction risk. It measures how well OCR pipelines, OCR‑dependent detectors, and OCR‑free vision‑language models can localize and remove sensitive information, using entity‑level F1, group‑wise leakage, and document‑level leakage metrics. The study shows that while advanced models improve localization, most pages still exhibit critical leakage, highlighting the need for higher‑recall, spatially grounded redaction methods.
By Vishnu Prasad Vijaya Kumar, Santhosh Venkatesh, Ivan P. Yamshchikov