Document Understanding

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5 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

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A Multi-Agent AI System for Automated High School Transcript Processing: Collaborative Document Analysis at Scale
Jun 11, 2026Ben Torkian, Jun ZhouDocument Understanding Multi-Agent Coordination
University of South Carolina, USA · University of South Carolina, Columbia, South Carolina, USA · University of South Carolina, Columbia, USA
TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs
Jun 8, 2026Momina Ahsan, Sarfraz Ahmad, Ming Shan Hee +2Document Understanding Table QA
1Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · 2Singapore University of Technology and Design (SUTD)
Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysis
Jun 1, 2026Catyana Heyne, Jürgen Frikel, Filippo RiccioDocument Understanding Multimodal Classification
OTH Regensburg, Germany
Office Comprehension Benchmark
May 29, 2026Firoz Shaik, Mateus Picanço Lima Gomes, Tanvir Aumi +17Document Understanding LLM Evaluation
Microsoft · † Work done while at Microsoft.
BullingerDB: A Dataset for Handwritten Text Recognition and Writer Retrieval
May 28, 2026Marco Peer, Anna-Scius Bertrand, Patricia Scheurer +1Document Understanding Historical Document Analysis
AIBEX Group, University of Fribourg, Switzerland · iCoSys Institute, University of Applied Sciences and Arts Western Switzerland · Department of Computational Linguistics, University of Zurich, Switzerland
Reading or Guessing? Visual Grounding Failures of Vision-Language Models for OCR in Ancient Greek Editions
May 26, 2026Antonia Karamolegkou, Nicolas Angleraud, Benoît Sagot +1VLM Evaluation Document Understanding
Inria, Paris, France
Experiments in Agentic AI for Science
May 25, 2026Judy Fox, Geoffrey FoxDocument Understanding AI for Science
School of Data Science, Department of Computer Science, and Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing
May 25, 2026Sofiat Abioye, Ufaq Khan, Shazad Ashraf +4Document Understanding Healthcare
Birmingham City University, Birmingham, England, UK · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE · University Hospitals Birmingham NHS Foundation Trust, Birmingham, England, UK +1
Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
May 25, 2026Kateryna Lutsai, Dana Křivánková, Pavel Straňák +1Document Understanding Historical Document Analysis
Institute of Formal and Applied Linguistics, Charles University MFF, Malostranské náměstí, Prague, 11800, Czech Republic. · Institute of Archaeology, Czech Academy of Sciences, Letenská, Prague, 11800, Czech Republic.
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding
May 21, 2026Yandi Wang, Libin Zhan, Ziwei Huang +5Document Understanding Document Information Extraction
Zhejiang University, China
Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding
May 20, 2026Jeonghun Baek, Atsuyuki Miyai, Shota Onohara +2Document Understanding
The University of Tokyo, Tokyo, Japan · Mantra Inc., Tokyo, Japan
RAPT: Retrieval-Augmented Post-hoc Thresholding for Multi-Label Classification
May 15, 2026Lasal Jayawardena, Nirmalie Wiratunga, Ikechukwu Nkisi-Orji +1Document Understanding Multi-Label Classification
Robert Gordon University, Aberdeen, United Kingdom · William Nicol (Aberdeen) Limited, Aberdeen, United Kingdom
LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentation
May 8, 2026Giorgia Bolognesi, Claudio Estatico, Ulderico Fugacci +3Document Understanding Retrieval-Augmented Generation
1Rulex s.r.l., Genova, Italy · Department of Mathematics (DIMA), University of Genoa, Italy · Institute of Applied Mathematics and Information Technologies “Enrico Magenes” (IMATI), National Research Council, Italy +1
LensVLM: Selective Context Expansion for Compressed Visual Representation of Text
May 7, 2026Roy Xie, Dan Friedman, Donghan Yu +7Document Understanding Tool Use in VLMs
Apple · Duke University
SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States
May 6, 2026Zhenliang Zhang, Wenqing Wang, Yong Hu +4Document Understanding Long-Context QA
Peking University · WeChat, Tencent · Work done during an internship at WeChat, Tencent
CC-OCR V2: Benchmarking Large Multimodal Models for Literacy in Real-world Document Processing
May 5, 2026Zhipeng Xu, Junhao Ji, Zulong Chen +10Document Understanding Multimodal Large Language Models
Alibaba Group · Northeastern University
Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health
Apr 30, 2026Yuxi Ma, Jieming Cui, Muyang Li +6Document Understanding Depression Detection
Institute for Artificial Intelligence, Peking University · School of Psychological and Cognitive Sciences, Peking University · School of Intelligence Science and Technology, Peking University +3
APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation
Apr 30, 2026Pengyun Zhu, Qiheng Sun, Long Wen +7Document Understanding Text Summarization
Tianjin University · Zhejiang University · North University of China +3
FCMBench-Video: Benchmarking Document Video Intelligence
Apr 28, 2026Runze Cui, Fangxin Shang, Yehui Yang +3VLM Evaluation Document Understanding
AI Lab, Qifu Technology, Beijing, China · School of Future Technology, South China University of Technology, Guangzhou, China · Pazhou Lab, Guangzhou, China +1
Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge
Apr 24, 2026Mengyu Wang, Xiaoying Zhi, Zhiyi Li +4Document Understanding LLM Fine-Tuning
1The University of Edinburgh · 2JPMorgan Chase & Co.
Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts
Apr 24, 2026Hamza A. Abushahla, Ariel Justine N. Panopio, Layth Al-Khairulla +1Document Understanding Historical Document Analysis
Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
Can MLLMs "Read" What is Missing?
Apr 23, 2026Jindi Guo, Chaozheng Huang, Xi FangVLM Evaluation Document Understanding
DP Technology
Knows: Agent-Native Structured Research Representations
Apr 19, 2026Guangsheng Yu, Xu WangDocument Understanding Knowledge-Intensive QA
Independent Researcher
UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards
Apr 16, 2026Jun Wang, Shuo Tan, Zelong Sun +5Document Understanding Multimodal RAG
Glint Lab
CzechTopic: A Benchmark for Zero-Shot Topic Localization in Historical Czech Documents
Mar 4, 2026Martin Kostelník, Michal Hradiš, Martin DočekalDocument Understanding Historical Document Analysis
Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
Page image classification for content-specific data processing
Jul 11, 2025Kateryna LutsaiDocument Understanding Historical Document Analysis
Institute of Formal and Applied Linguistics
Characterizing Narrative Content in Web-scale LLM Pretraining Data
Date pendingTeagan Johnson, Elliott Ash, Andrew Piper +1Document Understanding Language Model Pretraining
University of Colorado Boulder · ETH Zürich · McGill University

Jun 11, 2026, cs.AIJ/K move · Enter open · S save

A Multi-Agent AI System for Automated High School Transcript Processing: Collaborative Document Analysis at Scale

Ben Torkian, Jun Zhou

University of South Carolina, USA · University of South Carolina, Columbia, South Carolina, USA · University of South Carolina, Columbia, USA

Document Understanding Multi-Agent Coordination Multimodal Agents

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Each year, college admissions offices face an overwhelming challenge: processing millions of high school transcripts, each with unique formats, grading systems, and layouts. This manual process creates operational bottlenecks that delay admissions decisions and consume valuable resources. We present a transformative solution through a multi-agent AI system where specialized agents collaborate to automatically process diverse transcript formats through intelligent coordination and communication. Our multi-agent architecture consists of three specialized agents-a Pattern Recognition Agent for format-specific parsing, a Semantic Analysis Agent for natural language understanding, and a Vision Intelligence Agent for multimodal document analysis-coordinated by an Orchestration Agent that manages agent communication and result reconciliation. Our key innovation lies in agent-based quality control using GPA extraction as a coordination signal, ensuring reliable agent collaboration and preventing critical information loss. When evaluated on 40 real world transcripts from high schools across 13 U.S. states, our agent system successfully processed every document, achieving 96.7% accuracy compared to expert manual review while maintaining practical processing speeds of 45 seconds per transcript. This work demonstrates how multi-agent coordination can solve complex document processing challenges, offering institutions a scalable, collaborative AI solution that preserves accuracy while dramatically reducing processing time.

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