arXiv Computation and Language

A Task-Centric Survey of Long-Document Visual Question Answering: Taxonomy, Evolution, and Future Directions

The paper surveys long-document visual question answering (VQA) from a task-centric perspective, breaking it down into document parsing, evidence organization and retrieval, generator organization and learning, and evaluation. It introduces a detailed taxonomy for each component, tracks how research priorities have shifted over time—from multimodal content understanding to evidence acquisition and management—and highlights the importance of explicit retrieval alongside direct reading. The authors propose future research directions focused on evidence sufficiency, verifiable and correctable understanding, budget-aware coordination, and continual document understanding.

arXiv Computation and Language

Oct 16

VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents

VELD-QA is a large-scale benchmark for visual evidence grounding and question answering in long documents, comprising 15,000 human-authored question-answer pairs across 5,859 PDFs with 372,445 pages. The dataset includes detailed annotations of supporting pages, visual evidence elements, and descriptions of cited visual content, enabling separate evaluation of retrieval, localization, and interpretation. Experiments on nine vision-language models and various retrieval-augmented systems reveal significant gaps in evidence localization and visual interpretation, with oracle accuracy peaking at 81.7% and enrichment of visual-element annotations narrowing performance gaps by 17–24%.

By Zhivar Sourati, Marianne Menglin Liu, Rongguang Wang, Zheng Wang, Yazhe Hu, Lan Guo, Yuying Wang, Mengqing Guo, Bhavesh Dnyaneshwar Gore, Shubham Pathak, Ramapriya Ranganath, Sandeep Sharma, Tao Sheng, Sujith Ravi, Dan Roth

llms rag agents nlp multimodal benchmarks

arXiv AI

Jul 7

Hierarchical Evidence-Driven Reasoning for Long Document Understanding

arXiv:2607. 04625v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset.

By Junyu Xiong, Yonghui Wang, Rongjian Gu, Chenyu Liu, Bing Yin, Wengang Zhou, Houqiang Li

rag multimodal benchmarks

arXiv Computation and Language

Sep 15

TestHallVQA: Exploring LVLMs' Document-Level Reasoning under Redundant Contexts from Scientific Exams

arXiv:2609.13158v1 Announce Type: new Abstract: Large Vision--Language Models (LVLMs) are increasingly expected to perform visual question answering (VQA) over planar media. However, existing planar...

By Yongqi Yu, Yu Zhang

llms nlp benchmarks

Hugging Face Trending Papers

Jul 6

Hierarchical Evidence-Driven Reasoning for Long Document Understanding

Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset. However, existing multimodal RAG pipelines primarily face two critical challenges: first, standard semantic similarity retrievers frequently fetch topically overlapping yet answer-void distractor pages that mislead downstream generation; second, rigid single-pass pipelines heavily depend on initial retrieval success, where any omission of core evidence inevitably causes cascading errors.

rag multimodal benchmarks

arXiv AI

Jul 29

VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents

arXiv:2607. 24748v1 Announce Type: cross Abstract: Visually-rich documents such as reports, slides, and manuals often distribute the evidence needed to answer a question across multiple pages, mixing text with layout cues, tables, charts, and figures.

By Seonok Kim

rag agents nlp multimodal benchmarks

arXiv Computation and Language

Aug 21

Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA

arXiv:2604. 13731v2 Announce Type: replace Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents.

By Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai

rag agents computer-vision nlp benchmarks

arXiv AI

Sep 15

Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation

The paper introduces SCoRE, an agentic framework for Visual Retrieval-Augmented Generation that explicitly selects and consolidates visual evidence before generating answers. It addresses two key challenges: sparse, scattered evidence and noisy exploration trajectories that obscure reasoning. By maintaining a textual ledger of relevant observations and reloading original images for a logical evidence sequence, SCoRE decouples reasoning from exploration and enforces strict visual grounding, with training that rewards evidence coverage, compactness, and answer correctness.

By Yucheng Shen, Lingyong Yan, Jiulong Wu, Shuaiqiang Wang, Jianmin WU, Dawei Yin, Min Cao

rag agents reinforcement-learning efficiency

arXiv AI

Sep 30

TAEC: Trajectory-Aware Evidence Coordination for Multi-Step Visual RAG

arXiv:2609.37349v1 Announce Type: cross Abstract: Multi-step visual retrieval-augmented generation (RAG) answers complex questions by repeatedly retrieving visual evidence, updating an intermediate s...

By Yalun Wu, Bingzhou Wang, Boyang Wang, Peiying Wang, Shaojie He, Yunhan Wang, Shaozu Yuan, Jiawei Wang

llms rag multimodal

arXiv Computation and Language

Oct 27

BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering

arXiv:2604.22678v2 Announce Type: replace Abstract: A common approach to question answering with retrieval-augmented generation (RAG) is to concatenate documents into a single context and pass it to...

By Jinghong Chen, Jingbiao Mei, Guangyu Yang, Bill Byrne

llms rag nlp fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 9

See More, Think Deeper: Query-Expanded Visual Evidence and Answer-Clue Guided Reflection for Long Video Understanding

arXiv:2606. 09064v1 Announce Type: cross Abstract: Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks.

By Shuning Wang, Zhiheng Wu, YiNuo Lu, Naiming Liu, Chen Jia, Bowen Liu, Shuo Nie, Weijie Zhu, Yumeng Zhang

llms benchmarks

arXiv AI

Aug 18

LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents

arXiv:2608. 15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence.

By Yuefeng Zou, Yichen Lu, Jingxiao Yang, Bingtao Fu, Gaoyang Zhang, Xiongfei Bai, Tian Chen, Xiang Qi

benchmarks

arXiv AI

Aug 7

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

arXiv:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.

By Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

llms nlp multimodal benchmarks

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