Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment. We present BEACON (Behavioral Entropy Aggregation for Cross-model hallucination detectiON), a black-box hallucination detection framework that operates purely on model outputs without requiring access to internal representations or external knowledge bases. BEACON extracts a 31-dimensional feature vector from structured multi-pass generation, integrating NLI-based semantic entropy, embedding geometry, chain-of-thought consistency, and paraphrase stability signals. A gradient-boosted classifier trained on 7,617 labeled examples across seven benchmarks achieves 0.8123 +/- 0.0102 AUROC (95% CI: 0.7632-0.8251), outperforming standalone semantic entropy (+0.2298) and SelfCheckGPT-style consistency baselines (+0.2457). Feature importance analysis shows that hallucination is inherently multi-dimensional, requiring combined uncertainty signals. An efficient 5-call variant achieves 0.7795 AUROC, enabling practical deployment across black-box LLM APIs.
Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明负责任的人工智能与数据分析实验室）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹大学）；University of Electronic Science and Technology of China（中国电子科学技术大学）；University of Copenhagen（哥本哈根大学）
Behind Harmful Compliance: Behavioral and Mechanistic Divergence Across LLM Jailbreaks
Department of Computer Science（计算机科学系）；Luddy School of Informatics, Computing, and Engineering（信息学、计算与工程学院）；Indiana University Bloomington（印第安纳大学布卢明顿分校）
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière（索邦大学，巴黎脑研究所-ICM，法国国家科学研究中心，法国国家信息与自动化研究所，法国国家健康与医学研究院，巴黎公共医疗集团，皮蒂-萨尔皮特里埃医院）
QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents
McGill University（麦吉尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；University of Cambridge（剑桥大学）；MBZUAI - Mohamed bin Zayed University of Artificial Intelligence（MBZUAI - 摩苏尔·本·扎耶德人工智能大学）；University of Toronto（多伦多大学）；Salesforce
Sycophancy in Vision-Language Models: A Systematic Analysis and an Inference-Time Mitigation Framework
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences（处理器国家重点实验室，中国科学院计算技术研究所）；Department of Computer Science, National University of Singapore（计算机科学系，新加坡国立大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Intelligent Software Research Center, Institute of Software, Chinese Academy of Sciences（软件智能研究中心，中国科学院软件研究所）
