Automated Fact-Checking

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14 papers in the last four weeks, up 367% on the four weeks before. 0.1% of all new papers.

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Automated Textbook Auditing with Multi-Agent LLM Systems
Jul 13, 2026Ciprian Cristescu, Adrian-Marius Dumitran, Angela-Liliana Dumitran +1AI Agent Auditing Generative AI in Education
University of Bucharest, Bucharest, Romania · Dimitrie Cantemir Christian University, Bucharest, Romania
Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability
Jul 11, 2026Lingwei Wei, Dou Hu, Wei Zhou +2Fake News Detection Adversarial Attacks on LLMs
Institute of Information Engineering, Chinese Academy of Sciences, China · University of Illinois Chicago, USA · State Key Laboratory of Media Convergence and Communication, Communication University of China, China +1
Know Your Source: A Public Knowledge Store for Media Background Checks
Jul 2, 2026Benjamin Nichols, Michael Schlichtkrull, Nedjma OusidhoumLLM Evaluation Automated Fact-Checking
△Cardiff University · ⋄Queen Mary University of London
Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking
Jul 2, 2026Nikil Roashan Selvam, Jay Baxter, Sophie Hilgard +4Social Media Analysis Algorithmic Auditing
Stanford University, Stanford, CA, USA · X Community Notes, xAI, Palo Alto, USA
Evidence Triangulation for Multimodal Fact-Checking in the Wild
Jun 30, 2026Stefanos-Iordanis Papadopoulos, Zacharias Chrysidis, Christos Koutlis +2Automated Fact-Checking Multimodal Fusion
Information Technologies Institute, Centre for Research & Technology, Hellas, Greece · Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece · 1Information Technologies Institute, Centre for Research & Technology, Hellas, Greece
How Human Feedback Shapes AI-generated Community Notes
Jun 29, 2026Soham De, Isaac Slaughter, Jiawei Guo +4Claim Verification Automated Fact-Checking
University of Washington
Multi-Agentic System Leveraging Open-Source LLMs to Mitigate Disinformation Threats
Jun 29, 2026Sebastian Kula, Martin TamajkaMulti-Agent LLM Systems Fake News Detection
Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia · West Pomeranian University of Technology in Szczecin, Szczecin, Poland
An Empirical Analysis of Factual Errors in Human-Written Text and Its Application to Factual Error Detection
Jun 26, 2026Kazuma Iwamoto, Kazumasa Omura, Shotaro IshiharaLLM Evaluation Automated Fact-Checking
Nikkei Inc.
ToE: A Hierarchical and Explainable Claim Verification Framework with Dynamic Multi-source Evidence Retrieval and Aggregation
Jun 26, 2026Zhaoqi Wang, Zijian Zhang, Kun Zheng +4Evidence-Grounded Reasoning Automated Fact-Checking
School of Cyberspace Science and Technology, Beijing Institute of Technology · TravelSky Technology Limited · School of Computer Science, University of Auckland
Taxonomy of Risks on Automated Fact-Checking Systems Considering its Propagation
Jun 24, 2026Jun Yajima, Tatsuya Oka, Takao OkuboAI Risk Management Automated Fact-Checking
1Fujitsu Limited · Institute of Information Security
The Warrant Gap: Claim-Conditioned Re-scoring for Fact-Checking
Jun 23, 2026Arka Ujjal Dey, John CollomosseFactual Consistency Evaluation Citation Verification
CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis
Jun 18, 2026Phuong Huu Vu Tran, Thuan Duc Mai, Bach Xuan LeLLM Self-Correction Automated Fact-Checking
Vietnamese-German University, Ho Chi Minh City, Vietnam · Ho Chi Minh University of Technology, Ho Chi Minh City, Vietnam
From Verdict to Process: Agentic Reinforcement Learning for Multi-Stage Fact Verification
Jun 11, 2026Rongxin Yang, Shenghong He, Siyuan Zhu +1Agentic RL Process Reward Models
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Context-Aware Multimodal Claim Verification in Spoken Dialogues
Jun 9, 2026Chaewan Chun, Delvin Ce Zhang, Dongwon LeeClaim Verification Audio Understanding
The Pennsylvania State University, USA · University of Sheffield, UK
Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs
Jun 7, 2026Pratuat Amatya, Vinay SettyMultilingual Language Models LLM Evaluation
Factiverse AS and University of Stavanger Stavanger, Norway
Towards Multi-Agent-Simulation-Based Community Note Evaluation
Jun 3, 2026Changxi Wen, Shuning Zhang, Bohao Chu +5Automated Fact-Checking LLM Agents
1Tsinghua University, Beijing, China · University of Duisburg-Essen, Duisburg, Germany · University of Luxembourg, Luxembourg +1
When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
Jun 2, 2026Tao Yu, Yujia Yang, Shenghua Chai +17Fake News Detection Automated Fact-Checking
1CASIA · 2UCAS · 3BAAI +2
Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes
Jun 1, 2026Zihang Fu, Fanxiao Li, Jianyang Gu +5Healthcare LLM Agent Self-Improvement
National University of Singapore · Yunnan University · The Ohio State University +1
Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checking
May 31, 2026Yuxi Sun, Wenbo Shang, Wei Gao +2Retrieval-Augmented Generation LLM Evaluation
Hong Kong Baptist University · Singapore Management University
Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages
May 29, 2026Gerrit Quaremba, Amy Rechkemmer, Elizabeth Black +2Multilingual Language Model Evaluation Small Language Models
King’s College London · Wikimedia Foundation
CommunityFact: A Dynamic, Multilingual, Multi-domain Benchmark for Misinformation Detection in the Wild
May 28, 2026Sahajpreet Singh, Insyirah Mujtahid, Min-Yen Kan +1Multilingual Language Model Evaluation LLM Evaluation
National University of Singapore
Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering
May 28, 2026Shicheng Fan, Haochang Hao, Dehai Min +3Process Reward Models RL for Language Model Reasoning
University of Illinois Chicago
DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification
May 27, 2026Shubhashis Roy Dipta, Ankur Padia, Francis FerraroClaim Verification RL for Language Model Reasoning
Department of Computer Science and Electrical Engineering · University of Maryland Baltimore County · Baltimore, MD 21250 USA
CiteCheck: Retrieval-Grounded Detection of LLM Citation Hallucinations in Scientific Text
May 26, 2026Khashayar Khajavi, Shaghayegh Sadeghi, Rise Adhikari +1Citation Verification Automated Fact-Checking
FirstPrinciples · School of Computing Science, Simon Fraser University
ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies
May 26, 2026Shang Luo, Yingguang Yang, Zhenchen Sun +8Fake News Detection Explainable Artificial Intelligence
Peking University · University of Science and Technology of China · North China University of Science and Technology +6
SEEK: Semantic Evidence Extraction via Adaptive ChunKing for Multilingual Fact-Checking
May 26, 2026Gaurav Kumar, Babu Kumar, Ayush Garg +2Document Chunking Automated Fact-Checking
Department of Data Science and Engineering Indian Institute of Science Education and Research, Bhopal, India
Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval
May 24, 2026Zhongtian Hua, Yi Luo, Meijia Yu +1Automated Fact-Checking Cross-Modal Retrieval
Zhengzhou University, Zhengzhou, Henan, China · Henan University of Science and Technology, Luoyang, Henan, China
Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
May 19, 2026Bing Wang, Rui Miao, Ximing Li +6Fake News Detection LLM Fine-Tuning
College of Computer Science and Technology, Jilin University Changchun, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University · School of Artificial Intelligence, Jilin University Changchun, China +3
CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction
May 12, 2026Islam Eldifrawi, Shengrui Wang, Amine TrabelsiLLM Hallucination Mitigation Automated Fact-Checking
Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection
May 12, 2026Hui Li, Zhongquan Jian, Jinsong Su +1Fake News Detection Automated Fact-Checking
School of Computer and Data Science, Minjiang University Fuzhou, China · School of Informatics, Xiamen University Xiamen, China

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Automated Textbook Auditing with Multi-Agent LLM Systems

Ciprian Cristescu, Adrian-Marius Dumitran, Angela-Liliana Dumitran, Gabriel Stefan

University of Bucharest, Bucharest, Romania · Dimitrie Cantemir Christian University, Bucharest, Romania

AI Agent Auditing Generative AI in Education Automated Fact-Checking Multi-Agent Systems

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Ensuring the quality of educational materials requires more than standard proofreading: textbooks must be audited for factual accuracy, domain-specific technical correctness, and linguistic quality simultaneously -- a task that general-purpose grammar checkers cannot address. We present \textbf{AI Textbook Auditor}, a modular multi-agent pipeline for automated quality assurance of educational materials across subject domains. The system accepts a textbook PDF and produces a structured, human-reviewable report via two analysis tracks: a \textbf{Factual and Technical Track} in which an ensemble of specialized LLM agents detects factual inaccuracies, code errors, incorrect definitions, and conceptual inconsistencies, augmented with web search for humanities domains; and a \textbf{Grammar Track} operating PDF-natively to preserve diacritical encoding. A \textbf{Judge Agent} filters false positives using domain-specific rules before presenting findings to a human reviewer. The pipeline supports two ingestion modes -- vision-native page rendering and PyMuPDF text extraction -- and is domain-adaptable via custom prompts encoding subject-specific error taxonomies. We demonstrate the system on two Romanian upper-secondary textbooks: a CS textbook (56 technical findings across seven categories, with an expert-validated precision of 62.5%) and a history and social sciences textbook (72 findings spanning factual errors, ideological bias, and grammar). The system is designed as a triage tool that reduces the manual effort of locating candidate issues, with human expert validation required before any editorial action.

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