Question Answering

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23 papers in the last four weeks, up 28% on the four weeks before. 0.2% of all new papers.

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Latest papers 165

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RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees
Oct 8, 2026Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan +3Retrieval-Augmented Generation Agentic RAG
IBM · IIT Kharagpur
Right Number, Wrong State? Measuring Cross-Jurisdiction Substitution in LLM Recall of State Policy
Oct 7, 2026Jiayu FengLLM Reliability Question Answering
Harvard T.H. Chan School of Public Health
Ontology Concept Overlap as a Training Signal: Knowledge-Grounded Reinforcement Learning for Clinical Question Answering
Oct 5, 2026Aditya Tanna, Abhishek JindalReward Modeling RL for Language Model Reasoning
Dhirubhai Ambani University Gandhinagar, Gujarat, India
Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents
Oct 1, 2026Ahmad Yehia, Aly O. Abdelkareem, Islam Ahmed +4LLM Agent Memory Persistent Memory for Language Models
The University of Texas at Austin · AIDAChip Inc.
Distilling Directional Verification
Oct 1, 2026Jungseob Lee, Sugyeong Eo, Seongtae Hong +4Question Answering Language Model Distillation
Korea University · Yonsei University Mirae Campus · Soongsil University +1
SEPAL: Separated Expert Pairs with Answer-Level Fusion for Reliable LLM Collaboration
Sep 30, 2026Weijie Ren, Yanwen Zhang, Hao Li +3Multi-Agent LLM Systems Question Answering
Zhejiang University · University of Electronic Science and Technology of China · University of Science and Technology of China
When Can Text Replace Vision? Structural Bottlenecks in Diagram Reasoning
Sep 30, 2026Yunbei Zhang, Janet Wang, Jihun Hamm +1Visual Reasoning Question Answering
Tulane University · Virginia Tech
Beyond the Timeline: Augmenting Long-Video Memory with Grounded Entity Biographies
Sep 29, 2026Hui Ren, Lei Fan, Henry Pao +5Multimodal Memory Question Answering
University of Illinois Urbana-Champaign · Amazon.com, Inc.
Learning to Retrieve Missing Evidence for Long-Term Memory QA
Sep 29, 2026Yi-Xuan Deng, Yi Zhang, Wei Liu +2Persistent Memory for Language Models Question Answering
Tsinghua University
Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation
Sep 28, 2026Chengyang Li, Yujie Wan, Shuai Wang +6Question Answering Multi-Agent Systems
The University of Hong Kong, Hong Kong SAR, China · Southern University of Science and Technology, Shenzhen, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China +2
Rubric-Aware On-Policy Self-Distillation for LLM Personalization
Sep 28, 2026Yilun Qiu, Xiaoyan Zhao, Chengbing Wang +6LLM Personalization On-Policy Self-Distillation
Xiaohongshu Inc. · National University of Singapore · University of Science and Technology of China +1
EnigmaForge: The Question Is Hidden in the Story
Sep 24, 2026Daniel EisnerQuestion Answering Synthetic Benchmark Generation
ChunkRank: Model-Aware Text Chunking and Abstention-Aware Answer Selection for LLM Pipelines
Sep 24, 2026Amit Nautiyal, Ayush Bhatt, Gaurav NautiyalDocument Chunking Question Answering
Meet, Compare, or Abstain: LatWeave for Deterministic Multi-Hop Question Answering on Knowledge Lattices
Sep 23, 2026Yuze Ren, Shaoheng Fan, Tao Wang +2Multi-Hop QA Question Answering
ZenSmart Technology (Beijing) Co., Ltd., China
Intrinsic Sequence-Likelihood Confidence in Retrieval-Dominated Extractive QA: Two Pre-Specified Negatives, and What They Do and Do Not Attribute
Sep 17, 2026Gunwoo Lee, Changmin Sung, Sang-Hwan Gwak +3LLM Evaluation Selective Prediction
Large-scale AI Research Center, Korea Institute of Science and Technology Information (KISTI), 245 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. · Department of Applied AI, University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea.
VākQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering
Sep 17, 2026Bhavana Akkiraju, Ravi Sastry Kolluru, Sri Charan D +3Audio QA LLM-as-a-Judge
International Institute of Information Technology Hyderabad, India
Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion
Sep 16, 2026Tithi Rakshit, Hongkuan Zhou, Lavdim Halilaj +1Multimodal RAG Multimodal IR
University of Tübingen, Tübingen, Germany · Corporate Research, Robert Bosch GmbH, Renningen, Germany · University of Stuttgart, Stuttgart, Germany
Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering
Sep 16, 2026Pablo Poulenard, Yannis Karmim, Valentin BarrièreRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
DCC, Universidad de Chile, Santiago, Chile · École Polytechnique, Palaiseau, France · Inria, Almanach, Paris, France +2
One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG
Sep 15, 2026Neeraj Anand, Payel Santra, Partha Basuchowdhuri +2Retrieval-Augmented Generation Adaptive RAG
Media and Data Science Research Lab Adobe Systems, Noida, India · IACS Kolkata, India · University of Glasgow Glasgow, United Kingdom
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control
Sep 15, 2026Ali ŞenolSelective Prediction LLM Prompting
Department of Computer Engineering, Tarsus University, Tarsus, Türkiye
The Cost of Compression: A Rate-Distortion Limit on Factual Hallucination
Sep 14, 2026Xi Wang, Shijia Xu, Rongfeng GuoMemorization in Language Models Hallucination in Language Models
Hefei Institutes of Physical Science, Chinese Academy of Sciences, China · University of Science and Technology of China, China · Chongqing University, China +1
Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakage
Sep 14, 2026Foad Namjoo, Remy Ogasawara, Amirali Abdullah +3Data Leakage LLM Evaluation
University of Utah · Thoughtworks · University of Massachusetts Amherst +1
Benchmarking Hybrid Deep Research Across Database Querying and Web Search
Sep 8, 2026Ruofan Wu, Peiran Xu, Xiaolong Li +9Deep Research Agents Agentic Search
University of Houston · University of California, Los Angeles · The University of Hong Kong +2
STSG-VQA: Evidence-Grounded Temporal Question Answering from Surgical Spatio-Temporal Scene Graphs
Sep 8, 2026Jing Li, Duygu SarikayaMedical VQA Question Answering
School of Computer Science, University of Leeds, UK
SentryLine: Evidence-Grounded Question Answering over Evolving Documents in Oncology Care
Sep 8, 2026Tampu Ravi Kumar, Gaurav Najpande, Muhammad Ali Khan +7Question Answering Clinical QA
Arizona State University · Mayo Clinic · Jawaharlal Nehru Medical College
CIVI: A Framework for Diagnosing Search Agent Failures in Civic Information
Sep 8, 2026Dingying Liu, Yunshun Zhong, Wentao Zhang +1Agent Failure Analysis AI Agent Reliability
University of Sydney · University of Toronto · University of Waterloo +1
Does the Selected Object Reach the Reader? Auditing Identity Handoffs in Grounded Language-Model Pipelines
Sep 4, 2026Siddharth Vohra, Runmin Jiang, Xiaomo Li +1Question Answering RAG Evaluation
Carnegie Mellon University, Pittsburgh, PA, USA · Amazon Web Services AI Native, Pittsburgh, PA, USA
RuleMem: Active Rule Memory for Long-Term Conversational Agents
Sep 3, 2026Xingyuan Zeng, Zuohan Wu, Quanming Yao +5Question Answering Long-Term Conversational Memory
Sun Yat-sen University, Zhuhai, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Tsinghua University, State Key Laboratory of Space Network and Communications, Beijing National Research Center for Information Science and Technology, Beijing, China +2
When Retrieval Helps: Selective Retrieval for Single-Turn Mental-Health QA
Sep 3, 2026Hyunseo Oh, Chong-Kwon Kim, Yoonhyuk ChoiRetrieval-Augmented Generation Mental Health Counseling
Sookmyung Women’s University · Korea Institute of Energy Technology
Cite or Decline: A Strict Course-Grounded Chatbot for STEM Lecture Videos
Sep 1, 2026S M Masrur Ahmed, Jaspal SubhlokRetrieval-Augmented Generation Question Answering
University of Houston

Oct 8, 2026, cs.AIJ/K move · Enter open · S save

RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees

Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan, Yatin Nandwani+2

IBM · IIT Kharagpur

Retrieval-Augmented Generation Agentic RAG Question Answering

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Retrieval-augmented generation (RAG) grounds language models in external corpora. Agentic RAG enables iterative search, yet exposes the model to isolated chunks without document structure, making it difficult to distinguish relevant evidence from chunks that merely resemble the query. Structure-aware methods such as PageIndex navigate document structure but cannot scale to the structures of large corpora, which do not fit in the LLM context. Hence, they first commit to a single document using a document retriever and cannot recover from a wrong choice. We propose RIT-RAG (Retrieval-Induced Tree RAG), which combines content retrieval with structural navigation. Offline, RIT-RAG builds a tree for each document from its table of contents or sitemap. At query time, it retrieves a broad set of chunks and uses their positions to induce manageable sub-trees, potentially across multiple documents. An LLM agent navigates these sub-trees, selectively reads promising nodes, and reformulates queries when needed. Thus, retrieval proposes where to look, while the agent decides what to read. Across financial, scientific, and customer-support benchmarks, RIT-RAG achieves the highest answer accuracy among vanilla, graph-based, and agentic baselines. On EntQABench, our new benchmark of 2.84 million technical-documentation webpages, it improves accuracy by 6.8 to 11.4 points over the strongest baseline across three LLMs.

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