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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From Chunks to Functional Evidence: Function-Aware Retrieval for EDA Documentation QA
Oct 7, 2026Xiaotian Qiu, Kairui Liu, Shi Chenyi +3Evidence Retrieval Retrieval-Augmented Generation
Zhejiang University · Shanghai Innovation Institute
When Harness Beats Scale, and When Reading Beats Both
Sep 28, 2026Ivan Bondarenko, Nikolay O. NikitinNumerical Reasoning in Language Models Language Model Scaling
Novosibirsk State University · ITMO University
An Empirical Study of VLM Pipelines for Long-Document QA
Sep 24, 2026Kenan E. Ak, Jay Mohta, Gwang Gook Lee +2VLM Evaluation Tool Use in VLMs
Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints
Sep 23, 2026Imtiaz Ul Hassan, Öykü Akbulut, Onur Kaya +4Multilingual Language Model Evaluation LLM Evaluation
Edge Hill University, United Kingdom · Ermetal Otomotiv ve E¸sya San. Tic. A.¸S., Türkiye
Version- and Scope-Aware Question Answering over Normative Documents: A Deployed System and an End-to-End Evaluation at Production Scale
Sep 16, 2026Liuyin Wang, Shuaipeng Jin, Jiwei Shi +1Document QA RAG Evaluation
Beijing Caizhi Technology Co., Ltd., Beijing, China · dknownAI, Beijing, China
PageRecall: Measuring Page Selection in Literature-Grounded Question Answering
Sep 16, 2026Aaditya ChauhanDocument Information Extraction Scientific QA
Independent Researcher
SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding
Sep 4, 2026Shenxi Wu, Yuhong Liu, Haosong Zhang +8Document Understanding LLM Evaluation
The Chinese University of Hong Kong · Shanghai Artificial Intelligence Laboratory · New York University +5
From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings
Aug 9, 2026Bernes Lorier Atabonfack, Zion Kongbi Nfo, Ahmed Tahiru Issah +9LLM Fine-Tuning Document QA
Carnegie Mellon University Africa, Kigali, Rwanda · IngenziAI, Kigali, Rwanda · Medical Artificial Intelligence Laboratory, Lagos, Nigeria +5
Time Present and Time Past: Benchmarking Large Language Models on Temporally Evolving Document Understanding
Aug 9, 2026Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Fahmid Hasan Chowdhury +4LLM Evaluation Document QA
1BRAC University · 2United International University · 3North South University +1
DocMemo: Dynamic Evidence Discovery via Probabilistic Memory-Guided Retrieval for Multi-Modal Document Understanding
Aug 7, 2026Hanshu Yao, Janfeng Zhong, Niu Lian +1Multimodal Memory Document QA
1Harbin Institute of Technology, Shenzhen · 2Tsinghua Shenzhen International Graduate School, Tsinghua University
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
Aug 6, 2026Sagar Tamang, Ayush Vyas, Tabarakul HazarikaLong-Context Retrieval Retrieval-Augmented Generation
1Indian Institute of Technology Patna, Bihta, India · 2TwoSpoon, India.
MMLongBench-Doc-V2: A Corrected-Annotation, Semantics-Aware Revision of MMLongBench-Doc
Aug 4, 2026Mingtian ZhangLLM Evaluation Long-Context Language Model Evaluation
PageIndex
DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering
Aug 3, 2026Dongyang Xie, Yao Tian, Hao Zhang +5Graph Retrieval-Augmented Generation Agentic RAG
School of Computer Science, Wuhan University · The Hong Kong University of Science and Technology · The Chinese University of Hong Kong +1
HierDoc: Hierarchical Page-to-Region Evidence Routing for Long-Document Visual Question Answering
Jul 31, 2026Rongjian Gu, Wengang Zhou, Junyu Xiong +4Visual Question Answering Document QA
University of Science and Technology of China · 2iFLYTEK Research · 3Anhui Transport Consulting & Design Institute Co. Ltd., Hefei, China
HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document
Jul 26, 2026Xin He, Yili Wang, Wenqi Fan +4Multimodal RAG Retrieval-Augmented Generation
Jilin University Changchun, China · The Hong Kong Polytechnic University Hong Kong SAR, China
TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal Question Answering
Jul 21, 2026Zhong Ji, Keqi Jin, Yan Zhang +1Multimodal RAG Retrieval-Augmented Generation
School of Electrical and Information Engineering, Tianjin University · The International Joint Institute of Tianjin University, Fuzhou, Tianjin University · Department of Automation, Tsinghua University
DocAtlas: Long-Document Understanding as Mutable-State Interaction
Jul 21, 2026Hongchen Wei, Yuanzhe Wang, Bei Liu +8Document Understanding Long-Context QA
Wuhan University · Microsoft
XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understanding
Jul 21, 2026Hongchen Wei, Yuanzhe Wang, Bei Liu +9Long-Context Retrieval LLM Evaluation
1Wuhan University · 2Microsoft
Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration
Jul 18, 2026Maksim Sheverev, David Finkelstein, Sergey NikolenkoLLM Agent Memory AI Agent Benchmarks
Quantellence Research · St. Petersburg Department of the Steklov Institute of Mathematics, St. Petersburg, Russia · St. Petersburg State University, St. Petersburg, Russia
Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment
Jul 16, 2026Harikrishnan P M, Goutham Vignesh, Ganesh Parab +4Multimodal Grounding Efficient Multimodal Inference
Phi Labs, Quantiphi, India · Phi Labs, Quantiphi, Canada.
GDP.pdf: Benchmarking Grounded Multimodal Reasoning over Professional PDF Documents
Jul 13, 2026Suhaas Garre, Emily Ritchie, Sushant Mehta +1Multimodal Grounding Multimodal Reasoning
Surge AI
DocMaster: A Hierarchical Structure-Aware System for Document Analysis
Jul 9, 2026Ziqi Chen, Yingli Zhou, Fangyuan Zhang +3Document Understanding Document QA
The Chinese University of Hong Kong, Shenzhen · The Chinese University of Hong Kong · OceanBase, AntGroup
Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts
Jul 2, 2026Valentin J. J. Kreileder, Johannes Reisinger, Andreas FischerDocument Chunking Retrieval-Augmented Generation
Computer Science · Deggendorf Institute of Technology · Deggendorf, Germany
MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering
Jul 1, 2026Dang Quang Thien Tran, Quang V. Dang, Vinamra Tyagi +7Multimodal QA Document QA
University of Massachusetts, Amherst · 2Adobe Research, San Jose
Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation
Jul 1, 2026Raymond Li, Md Tawkat Islam Khondaker, Amirhossein Abaskohi +3Retrieval-Augmented Generation Knowledge Conflicts in Language Models
ServiceNow Research · University of British Columbia
LLM-Guided Planning for Multi-hop Reasoning over Multimodal Nuclear Regulatory Documents
Jun 28, 2026Mingyu Jeon, Bokyeong Kim, Suwan Cho +2Retrieval-Augmented Generation Legal Document Analysis
1MODULABS, Seoul, Republic of Korea · 2Korea Atomic Energy Research Institute, Daejeon, Republic of Korea · University of Science & Technology, Daejeon, Republic of Korea
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
Jun 23, 2026Honglin Guo, Qi Zhang, Yu Zhang +7LLM Grounding Document QA
Fudan University · Zhejiang University · Shanghai Qiji Zhifeng Co., Ltd.
The Token Tax of Epistemic Accuracy: Comparing RAG and Long-Context Architectures for Document-Grounded Generative AI Applications
Jun 18, 2026Austin Hamilton, Ryan Singh, Michael Wise +6LLM Grounding LLM Inference
Miami University · University at Buffalo
VinQA: Visual Elements Interleaved Long-form Answer Generation for Real-World Multimodal Document QA
Jun 15, 2026Young Rok Jang, Hyesoo Kong, Kyunghwan An +3VLM Evaluation Multimodal Large Language Models
LG AI Research
Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?
Jun 14, 2026Sadanand Singh, Allam Reddy, Manan ChopraCross-Encoder Reranking Learning to Rank
Cascade Research

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From Chunks to Functional Evidence: Function-Aware Retrieval for EDA Documentation QA

Xiaotian Qiu, Kairui Liu, Shi Chenyi, Jinyuan Deng+2

Zhejiang University · Shanghai Innovation Institute

Evidence Retrieval Retrieval-Augmented Generation Document QA Graph-Based Retrieval Electronic Design Automation

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Retrieval-Augmented Generation (RAG) is widely used to ground answers in documents. For complex technical documentation, however, the primary bottleneck is often not model reasoning but a mismatch between a query and the way knowledge is organized for retrieval. This mismatch is pronounced in Electronic Design Automation (EDA) documentation, where the information needed for an answer is scattered across heterogeneous yet tightly coupled artifacts. We therefore redesign the basic retrieval unit of RAG. Instead of operating on isolated chunks or binary relations, we collect typed artifacts into EDA functional units. Each unit is recorded as a hyperedge with links to its source chunks. We then train an encoder to align queries with functional units and combine unit retrieval with direct chunk retrieval. After mapping the selected units back to their sources, a unified reranker chooses the evidence given to the generator. On the newly constructed EDADocEval-QA dataset, our method improves ROUGE-L by 37.1% over Chunk RAG and 55.6% over the strongest graph baseline. On the public ORD-MMBench benchmark, it improves ROUGE-L by 30.0% over the strongest baseline. These results support function-aware evidence organization in the evaluated EDA documentation settings.

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