Document QA

QA: Question Answering

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 52

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Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehension
Jun 11, 2026Shenglai Zeng, Qirui Wang, Kai Guo +3VLM Robustness Visual Attention
Michigan State University · Xi’an Jiaotong University
Doc-to-Atom: Learning to Compile and Compose Memory Atoms
Jun 10, 2026Xingjian Diao, Wenbo Li, Yashas Malur Saidutta +3Memory-Augmented Language Models Long-Context Language Model Inference
AI Center-Mountain View, Samsung Electronics · Dartmouth College
Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering
Jun 9, 2026Xiangjun Zai, Xingyu Tan, Chen Chen +2Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
University of New South Wales, Australia · CSIRO, Australia · University of Wollongong, Australia
RealDocBench: A Benchmark for Field-Level QA and Layout Understanding on Real-World Regulated Documents
Jun 5, 2026Ameya Joshi, Joon Kim, Gus Eggert +5Benchmark Design Document Information Extraction
Extend AI
FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A
Jun 5, 2026Ambuj Mehrish, Sebastiano VasconMultimodal RAG Multimodal QA
Ca’ Foscari University of Venice
Rethinking LoRA Memory Through the Lens of KV Cache Compression
Jun 4, 2026Chunsheng Zuo, Liaoyaqi Wang, William Jurayj +2Retrieval-Augmented Generation Memory-Augmented Language Models
Johns Hopkins University
MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning
Jun 3, 2026Qiyang Xie, Jialun Wu, Xinjie He +4Conversational Memory Retrieval-Augmented Generation
Northeastern University · Johns Hopkins University · Columbia University +1
Office Comprehension Benchmark
May 29, 2026Firoz Shaik, Mateus Picanço Lima Gomes, Tanvir Aumi +17Document Understanding LLM Evaluation
Microsoft · † Work done while at Microsoft.
HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering
May 28, 2026Joongmin Shin, Gyuho Shim, Jeongbae Park +2Multimodal RAG Multimodal IR
Human-inspired AI Research, Korea University · Department of Computer Science and Engineering, Korea University · Computer Science and Engineering, Konkuk University
ACL-Verbatim: hallucination-free question answering for research
May 20, 2026Gábor Recski, Szilveszter Tóth, Nadia Verdha +2Retrieval-Augmented Generation LLM Hallucination Mitigation
TU Wien · KR Labs
CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
May 13, 2026Dongsheng Ma, Jiayu Li, Zhengren Wang +9VLM Evaluation Citation Verification
Peking University · Shanghai Artificial Intelligence Laboratory
Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding
May 11, 2026Anton Bazdyrev, Ivan Bashtovyi, Ivan Havlytskyi +2Retrieval-Augmented Generation LLM Reranking
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
ASTRA-QA: A Benchmark for Abstract Question Answering over Documents
May 11, 2026Shu Wang, Shansong Zhou, Xinyang Wang +3Question Answering Document QA
The Chinese University of Hong Kong, Shenzhen · Data Science Group, Huolala
DocScope: Benchmarking Verifiable Reasoning for Trustworthy Long-Document Understanding
May 9, 2026Xiang Feng, Jiawei Zhou, Zhangfeng Huang +6LLM Grounding LLM Answer Verification
School of Computer Science, National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China · Alibaba Group, Hangzhou, China · Independent Researcher +1
Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions
May 7, 2026Kjetil Indrehus, Adrian Duric, Changkyu Choi +1Vision-Language Grounding Document QA
Department of Informatics, University of Oslo · Integreat – Norwegian Centre for Knowledge-driven Machine Learning · TRUST – The Norwegian Centre for Trustworthy AI
DocPrune:Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning
Apr 24, 2026Joonmyung Choi, Sanghyeok Lee, Jongha Kim +4Efficient VLM Inference Document QA
Korea University · KAIST · Adobe Research
Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA
Apr 24, 2026Zhanli Li, Yixuan Cao, Lvzhou Luo +1Document QA RAG Evaluation
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing 100190, China · University of Chinese Academy of Sciences, Beijing 100049, China
An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA
Apr 21, 2026Saransh Sharma, Pritika Ramu, Aparna Garimella +1Scientific QA Document QA
Adobe Research, India · University of Maryland, College Park
Toward Agentic RAG for Ukrainian
Apr 16, 2026Marta Sumyk, Oleksandr KosovanAgentic RAG Document QA
Ukrainian Catholic University, Lviv, Ukraine
FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment
Mar 20, 2026Betty Xiong, Jillian Fisher, Benjamin Newman +5Document QA Medical QA
Stanford University · University of Washington · U.S. Food and Drug Administration
DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections
Aug 20, 2025Jiwon Park, Seohyun Pyeon, Jinwoo Kim +4Multi-Hop QA Scientific QA
Pohang University of Science and Technology, Pohang-si, South Korea · The University of Melbourne, Melbourne, Australia · The University of Western Australia, Perth, Australia
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
Feb 1, 2024Xinyue Chen, Pengyu Gao, Jiangjiang Song +1Retrieval-Augmented Generation Document QA
1Nanjing University of Aeronautics and Astronautics. · 2Southeast University, Nanjing, Jiangsu, China.

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Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehension

Shenglai Zeng, Qirui Wang, Kai Guo, Xinnan Dai+2

Michigan State University · Xi’an Jiaotong University

VLM Robustness Visual Attention VLM Interpretability Document QA

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Visual Text Comprehension (VTC) renders text into images for a vision-language model (VLM) to read, sidestepping LLM context-window limits and powering applications from long-page OCR to multi-page memory QA. Yet existing VTC pipelines treat rendering and layout as a fixed, content-agnostic preprocessing step and offer little mechanistic understanding of how VLMs internally process visualized text. Through a focused empirical study on VTC QA tasks, we reveal that VLMs exhibit a localization-without-utilization regime: evidence-localizing attention emerges sharply in the middle-to-late layers and is largely decoupled from answer correctness, yet simply enlarging the localized spans on the rendered page recovers a large fraction of the failures. Building on these observations, we propose AGAR (Attention-Guided Adaptive Rendering), a training-free, model-agnostic method that leverages a VLM's own middle-to-late layer attention to identify the top-K important visual patches, maps them back to word spans, and re-renders the page with those spans enlarged before re-inferring the answer. Extensive experiments across nine VTC benchmarks (short-form, long-context, and multi-page memory QA) and four VLM backbones show that AGAR (i)consistently improves off-the-shelf VLMs as a plug-and-play enhancement, (ii)composes with VLM post-training to yield further gains, and (iii)remains robust under both visual- and text-side input degradation.

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Latest papers 52