Multi-Document QA

QA: Question Answering

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19 papers5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA
Oct 9, 2026Wenlin Lin, Fanzhe WeiDocument QA Agentic RAG
Metask Lab
Follow the Entities: A Corpus Map for Agentic Search
Sep 29, 2026Soyeong Jeong, Sujay Kumar Jauhar, Sung Ju Hwang +1Multi-Hop QA Agentic Search
KAIST · Microsoft
ANTMAN: Adaptive Need Tracking for Multi-Agent Navigation in Large Information Spaces
Sep 27, 2026Jerry Wang, Haibo Jin, Xiaopeng Yuan +2Multi-Agent Coordination Multi-Document QA
University of Illinois Urbana-Champaign
Diagnosing the Fact-Grounding Gap in Multi-Hop Question Answering
Sep 15, 2026Kevin Mo, Nathan Mo, Richard ZhuMulti-Hop QA Multi-Hop Reasoning
Independent · Northwestern University
Lit3R: Retrieve-Relate-Read for Evidence-Grounded Question Answering over Scientific Literature
Sep 15, 2026Akira Ise, Kotaro Kumagai, Yuta Yamaguchi +3Scientific QA Evidence-Grounded Generation
♠Tokyo University of Science · ♣Dentsu Soken Inc. · ♡Studio Ousia
LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering
Aug 7, 2026Xuye Liu, Yimu Wang, Peng Shi +7Scientific QA Evidence-Grounded Generation
University of Waterloo · 2Amazon · 3City University of Hong Kong +1
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
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
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
Jun 23, 2026Honglin Guo, Qi Zhang, Yu Zhang +7LLM Grounding Multi-Document QA
Fudan University · Zhejiang University · Shanghai Qiji Zhifeng Co., Ltd.
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"
Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA
Apr 24, 2026Zhanli Li, Yixuan Cao, Lvzhou Luo +1Multi-Document QA Document QA
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
PAR $^2$ -RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering
Mar 30, 2026Xingyu Li, Rongguang Wang, Yuying Wang +5Multi-Hop QA Retrieval-Augmented Generation
Oracle AI
IndexRAG: Index-Time Reasoning for Multi-Hop Retrieval-Augmented Generation
Mar 17, 2026Zhenghua Bao, Yi ShiMulti-Hop QA Retrieval-Augmented Generation
Continuum AI Shanghai, China · Continuum AI San Francisco, CA, USA
Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections
Mar 12, 2026Łukasz Borchmann, Jordy Van Landeghem, Michał Turski +12Multi-Document QA LLM Agent Evaluation
DocSage: An Information Structuring Agent for Multi-Doc Multi-Entity Question Answering
Mar 12, 2026Teng Lin, Yizhang Zhu, Zhengxuan Zhang +2Multi-Document QA Multi-Hop QA
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology
OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning
Mar 9, 2026Krista Opsahl-Ong, Arnav Singhvi, Jasmine Collins +10Multi-Document QA LLM Agent Evaluation
Databricks
SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation
Mar 9, 2026Yagiz Can Akay, Muhammed Yusuf Kartal, Esra Alparslan +2Multi-Document QA Retrieval-Augmented Generation
TOBB University of Economics and Technology · OSTIM Technical University
KohakuRAG: A simple RAG framework with hierarchical document indexing
Mar 8, 2026Shih-Ying Yeh, Yueh-Feng Ku, Ko-Wei Huang +1Retrieval-Augmented Generation Multi-Document QA
National Tsing Hua University · Comfy Org Research · Kohaku-Lab
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
Feb 1, 2024Xinyue Chen, Pengyu Gao, Jiangjiang Song +1Retrieval-Augmented Generation Multi-Document QA
1Nanjing University of Aeronautics and Astronautics. · 2Southeast University, Nanjing, Jiangsu, China.

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Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA

Wenlin Lin, Fanzhe Wei

Metask Lab

Document QA Agentic RAG Multi-Document QA Multimodal QA Multimodal RAG Tool-Using Vision-Language Agents+1

Open paper AskPDF arXiv

Question answering over large heterogeneous document collections requires first finding the right passages and then reading them, yet current retrieval-augmented agents give one model both kinds of tools and let it schedule itself. We present KnowForge, a pipeline separated by job: a hybrid-retrieval knowledge-point index; a Locator agent that can only search; a code-scheduled bridge judge that forces the missing second hop; a typed, size-bounded evidence pool that merges every search's ranking; a Deep Reader agent that can only read text, images and tables; and a synthesizer with a format checker. With a self-hosted Qwen3.6-35B-A3B (3B active), KnowForge reaches F1 47.5 on the full open-domain M3DocVQA (2,441 questions, 3,368 PDFs), +11.0 over the published M3DocRAG system and +7.1 over one-shot retrieve-and-answer on the same index and model, and 53.5 with DeepSeek-V4-Flash. On MultiHop-RAG it reaches 73.5 accuracy, above the strongest published RAG and GraphRAG results (71.2); on the single-document MMLongBench-Doc it lifts the same model from 34.8 to 47.0, level with GPT-4o reading page images (46.3). Per-job ablations on the full benchmarks attribute the gain: the Deep Reader is worth 3.8, 9.8 and 0 points on the three corpora, tracking how visual each is; merging both agents into one with all tools and twice the rounds loses 1.9 F1 because it keeps searching instead of reading; a round-budget sweep shows a step at four Locator rounds and a plateau beyond; query latency scales with model calls rather than corpus size. We serve the three indexed benchmarks as read-only knowledge bases (free registration) and release all prompts and the runner code: https://github.com/metask-ai/knowforge-bench