Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions. We argue this stems from the Transformer's Softmax function, which creates "Artificial Certainty" by collapsing ambiguous attention scores into a single probability distribution, discarding uncertainty information at each layer. To fix this, we introduce the Credal Transformer, which replaces standard attention with a Credal Attention Mechanism (CAM) based on evidential theory. CAM produces a "credal set" (a set of distributions) instead of a single attention vector, with the set's size directly measuring model uncertainty. We implement this by re-conceptualizing attention scores as evidence masses for a Dirichlet distribution: sufficient evidence recovers standard attention, while insufficient evidence yields a diffuse distribution, representing ambiguity. Empirically, the Credal Transformer identifies out-of-distribution inputs, quantifies ambiguity, and significantly reduces confident errors on unanswerable questions by abstaining. Our contribution is a new architecture to mitigate hallucinations and a design paradigm that integrates uncertainty quantification directly into the model, providing a foundation for more reliable AI.
A Principled Framework for Knowledge-enhanced Large Language Model
Northwestern University（西北大学）；IDEA Research, International Digital Economy Academy（国际数字经济学院IDEA研究院）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs
CS Department Virginia Tech（弗吉尼亚理工学院计算机科学系）；EECS Department MIT（麻省理工学院电子工程与计算机科学系）；CS Department Dartmouth College（达特茅斯学院计算机科学系）；Statistics Department Virginia Tech（弗吉尼亚理工学院统计学系）；CS Department Michigan State University（密歇根州立大学计算机科学系）
HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling
Center for Nonlinear Studies（非线性研究中心）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；T-1 Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室T-1部门）；Applied Mathematics University of Colorado Boulder（科罗拉多大学博尔德分校应用数学系）；T-4 Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室T-4部门）
HEDGE: Hallucination Estimation via Dense Geometric Entropy for VQA with Vision-Language Models
Simula Metropolitan Center for Digital Engineering (SimulaMet)（Simula数字工程研究中心）；Oslo Metropolitan University (OsloMet)（奥斯陆 Metropolitan 大学）；Simula Research Laboratory（Simula研究实验室）
SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory
School of Cyber Science and Technology Beihang University（北航信息科学与技术学院）；School of Computer Science and Engineering Beihang University（北航计算机科学与工程学院）；Didi Chuxing（滴滴出行）；Laboratory for Big Data and Decision National University of Defense Technology（国防科技大学大数据与决策实验室）；Department of Computer Science University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）
URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
Uppsala University（乌普萨拉大学）；University of Science, VNU-HCM（VNU-HCM大学）；Indiana University（印第安纳大学）；FPT Software, AI Center（FPT软件人工智能中心）；The Pennsylvania State University（宾夕法尼亚州立大学）；VNU University of Engineering（VNU工程大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
VisPlay: Self-Evolving Vision-Language Models from Images
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Washington University in St. Louis（华盛顿大学圣路易斯分校）；University of Maryland（马里兰大学）；National University of Singapore（新加坡国立大学）
Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；NVIDIA（英伟达公司）；Penn State University（宾夕法尼亚州立大学）；Columbia University（哥伦比亚大学）；University of Missouri-Kansas City（密苏里大学堪萨斯分校）；Florida State University（佛罗里达州立大学）；Auburn University（奥本大学）
Semantic Self-Distillation for Language Model Uncertainty
Department of Engineering Science University of Oxford（工程科学系牛津大学）；University of Oxford（牛津大学）；Oxford Suzhou Centre for Advanced Research University of Oxford Suzhou（牛津苏州先进研究中心）
Trusted Uncertainty in Large Language Models: A Unified Framework for Confidence Calibration and Risk-Controlled Refusal
University of Liechtenstein（列支敦士登大学）；University of the Republic of San Marino（圣马尔科共和国大学）；University of Mauritius（毛里求斯大学）；International University of Monaco（摩纳哥国际大学）；University of Andorra（安道尔大学）；University of Gibraltar（直布罗陀大学）；University of the Faroe Islands（法罗群岛大学）；Ilisimatusarfik (University of Greenland)（格陵兰岛研究所（格陵兰大学））；University of Prizren（普里兹伦大学）
