arXiv:2607. 10400v1 Announce Type: cross Abstract: Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc.
By Abhigya Verma, Khyati Mahajan, Amit Kumar Saha, Shruthan Radhakrishna, Sagar Davasam, Vikas Yadav, Sai Rajeswar Mudumba
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
The paper introduces a controlled benchmark for evaluating large language models (LLMs) on key‑value pair extraction from documents with varying levels of OCR noise. It tests 136 configurations across five instruction‑tuned open‑weight LLMs, three datasets, and four text‑quality conditions, using deterministic decoding to generate 17,688 document‑level inferences. The study finds that clean‑text performance does not reliably predict real‑world robustness, model rankings can reverse under noisy conditions, and few‑shot demonstrations do not always improve accuracy, highlighting reliability risks in OCR‑to‑LLM pipelines.
By Zahra Anvari
arXiv:2608. 12898v1 Announce Type: cross Abstract: Document parsing aims to transform unstructured documents into structured and machine-readable representations.
By Peng Cai, Zhaofan Zou, Shifa Liu, Yikun Wang, Jiawei Tang, Kaicheng Yang, Meng Tong, Zhongjiang He, Hao Sun
arXiv:2606. 01393v1 Announce Type: cross Abstract: Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems.
By Minglai Yang, Xinyan Velocity Yu, Pengyuan Li, Xinyu Guo, Zhenting Qi, Konwoo Kim, Longtian Ye, Xiaolong Luo, Jinhe Bi, Henry Zhang, Haris Riaz, Xuan Zhang, Yunze Xiao, Bangya Liu, Tom Tang, Yunfei Zhao, Qunshu Lin, Zihan Wang, Minghao Liu, Michael Lingzhi Li, Yilun Du, Jesse Thomason, Rogerio Feris, Alex Pentland, Zexue He
arXiv:2606. 06242v1 Announce Type: cross Abstract: Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables.
By AJ Carl P. Dy, Aivin V. Solatorio