Evidence-Grounded QA

QA: Question Answering

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39 papers7 papers in the last four weeks, down 22% on the four weeks before. 0.0% of all new papers.

Accept or Repair: Preserving Passage-Supported Answers in Contrastive Decoding
Oct 21, 2026Jungseob Lee, Seungyoon Lee, Hyeonseok Moon +6Evidence-Grounded QA Language Model Decoding
Korea University · Sookmyung Women’s University · Yonsei University Mirae Campus +2
Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG
Oct 20, 2026Juan Terven, Diana-Margarita Córdova-Esparza, Julio-Alejandro Romero-González +5Long-Context Retrieval Evidence Retrieval
CICATA Qro., Instituto Politécnico Nacional, México · Facultad de Informática, Universidad Autónoma de Querétaro, México · CIDETEC, Instituto Politécnico Nacional, México +3
VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents
Oct 14, 2026Zhivar Sourati, Marianne Menglin Liu, Rongguang Wang +12Long-Document VQA Document QA
University of Southern California · Oracle AI · Turing Enterprise Inc +1
When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following
Oct 14, 2026Shaoang Li, Yanhang Shi, Jian LiReasoning Trace Analysis Evidence-Grounded QA
Stony Brook University
From Conceptual EneMonIE to an Executable Backend: A Modular Architecture for Energy Information Extractionand Grounded Question Answering
Oct 12, 2026Janik Lesage, Khadim Lô, and Jean-Sébastien DessureaultEvidence-Grounded QA Document Information Extraction
Département de mathématiques et d’informatique, Université du Québec à Trois-Rivières, Canada
PathAnchor: Path-Structured Evidence for Scientific Agents
Sep 30, 2026Qiuhui Chen, Jiafan Lu, Shuaimin Tang +5Evidence-Grounded Reasoning Scientific QA
School of Information Science and Engineering, East China University of Science and Technology · College of Intelligent Robotics and Advanced Manufacturing, Fudan University · Tencent
Learning to Retrieve Missing Evidence for Long-Term Memory QA
Sep 29, 2026Yi-Xuan Deng, Yi Zhang, Wei Liu +2Long-Term Conversational Memory Evidence Retrieval
Tsinghua University
Relevance Is Not Sufficient Evidence: Detecting Evidence Gaps Before Generation in RAG
Sep 27, 2026Suting Chen, Peichun Hua, Yunming XiaoRetrieval-Augmented Generation Evidence-Grounded QA
Northwestern University · The Chinese University of Hong Kong, Shenzhen
Regime Boundary Alignment for Evidence-Gated Question Answering
Sep 25, 2026Zeyan Li, Qirong Guo, SIyuan Qiu +3Evidence-Grounded QA LLM Grounding
Shanghai Jiao Tong University · The Hong Kong University of Science and Technology (Guangzhou)
EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability
Sep 18, 2026Anubha Kabra, Katie Jooyoung Kim, Colin Zhiwei Kou +5Evidence-Grounded Generation Evidence-Grounded QA
Bloomberg
PageRecall: Measuring Page Selection in Literature-Grounded Question Answering
Sep 16, 2026Aaditya ChauhanDocument Information Extraction Scientific QA
Independent Researcher
EviScope: Paired Counterfactual Evidence Diagnostics for Faithful and Efficient Grounded Language Models
Sep 15, 2026Suryadeep Singh DeswalCounterfactual Evaluation LLM Grounding
Indian Institute of Technology Roorkee Roorkee, India
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
HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering
Sep 12, 2026An Nguyen Phu, Dung Nguyen Quang, Luu Hieu An +3Multi-Hop QA Factual QA
Hanoi University of Science and Technology · Monash University · University of Oregon
A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering
Sep 4, 2026Songeun Lee, Kyungjin Min, Injae Na +3Evidence-Grounded QA Retrieval-Augmented Generation
Korea University · Hanyang University · Hyundai Motor Company
Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
Sep 1, 2026Haruto Sato, Yuki Tanaka, Ren Nakamura +2Multi-Hop QA Evidence-Grounded Reasoning
Independent Researcher
EGT-KG: Evidence-Grounded Typed KG Retrieval for Practical Scientific QA with Small Language Models
Aug 31, 2026Muran Yu, Jiechao Gao, Yuandong Pan +5Knowledge Graphs Small Language Models
Department of Civil and Environmental Engineering, Stanford University · Department of Civil Engineering, University of Calgary
Lazy Grounding: Attacking Search Agents with Factual Evidence
Aug 31, 2026Yulin Zhang, Yukun Huang, Sanxing Chen +4Evidence-Grounded Reasoning RAG Poisoning Attacks
Duke University, Durham, NC 27708, USA
DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering
Aug 19, 2026Xujia Wang, Yizhe Zhang, Bin Xu +2Evidence-Grounded Generation Evidence-Grounded QA
Department of Computer Science and Technology, Tsinghua University
LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
Aug 17, 2026Xingjun Wang, Gongsheng Li, Qi Fan +3In-Context Retrieval Evidence Retrieval
ModelScope Team, Alibaba Group Hangzhou, China
Attention-Path Fragility as an Uncertainty Signal in Large Language Models
Aug 11, 2026Minsoo Kim, Sungyoung Ji, Kisung Moon +1Confidence Estimation in Language Models LLM Uncertainty Estimation
POSCO Holdings Future Technology Research Institute 440, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea
LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering
Aug 7, 2026Xuye Liu, Yimu Wang, Peng Shi +7Evidence-Grounded Generation Scientific QA
University of Waterloo · 2Amazon · 3City University of Hong Kong +1
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
ResearchQA: Benchmarking Citation-Grounded Question-Answering on Scientific Papers
Jul 13, 2026Saba Imran, Debanjum Singh SolankyLLM Evaluation Citation Verification
Khoj Inc.
ProvenAI: Provenance-Native Traces of Evidence in Generated Answers
Jun 24, 2026Mohammad Faizan, Dalal AlharthiMulti-Hop QA Data Provenance
College of Information Science University of Arizona
EmotionAI: A Privacy-Preserving Computational Intelligence Pipeline for Speech-Emotion-Grounded Conversational Analysis
Jun 22, 2026Wai Laam Mak, Isibor Kennedy Ihianle, Pedro MachadoSpeech Processing Emotion Recognition in Conversations
School of Science and Technology, Nottingham Trent University, Nottingham, UK
GroundEval: A Deterministic Replacement for LLM-as-Judge in Stateful Agent Evaluation
Jun 22, 2026Jeffrey FlyntLLM Agent Verification AI Agent Benchmarks
Independent Researcher
Knowledge-Graph Grounding Helps LLMs Only for Out-of-Training Knowledge: A Controlled Study on Clinical Question Answering
Jun 21, 2026Madhulatha Mandarapu, Sandeep KunkunuruFactual Knowledge in Language Models LLM Reasoning with Graphs
samyama
EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
Jun 19, 2026Fengchen Gu, Xiaotian Ren, Zhengyong Jiang +6Data Visualization LLM Answer Verification
School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China · ETSI de Telecomunicaci´on, Universidad Polit´ecnica de Madrid, Madrid, Spain
StepGap: A Hybrid NLI-LLM Checker for Step-Level Evidence-Gap Detectionin Multi-Hop Question Answering
May 23, 2026Yuelyu Ji, Zhuochun Li, Hui Ji +1Multi-Hop QA LLM Answer Verification
School of Computing and Information, University of Pittsburgh

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Accept or Repair: Preserving Passage-Supported Answers in Contrastive Decoding

Jungseob Lee, Seungyoon Lee, Hyeonseok Moon, Seongtae Hong+5

Korea University · Sookmyung Women’s University · Yonsei University Mirae Campus+2

Evidence-Grounded QA Language Model Decoding Knowledge Conflicts in Language Models Question Answering Contrastive Decoding

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Large language models often answer from knowledge learned during training, even when a provided passage contradicts that knowledge. To address this problem, contrastive decoding contrasts the predictions under the full and a modified prompt at every token, but it also alters correct answers that the passage supports. We show that checking answers against the passage removes this trade-off. Based on this observation, we propose Accept-or-Repair (AoR), a training-free procedure. AoR keeps a sampled answer if the passage supports it. Otherwise, it tries up to two replacements with contrastive decoding, returning one as soon as it passes this check. It rejects replacements that repeat the answer generated without the passage when the passage favors another answer. Our experiments show that AoR raises the score over sampling by 12.5 points on counterfactual passages and 2.3 on ordinary ones, surpassing eight contrastive and prompting baselines on both, including when each receives the same rejection rule. Replacing only initial answers that the passage does not support improves the average score of every decoder. These results suggest that models can follow passages that contradict their knowledge while preserving accuracy on passages that agree with it. Code will be released at https://github.com/js-lee-AI/AoR.