Consistent belief functions represent collections of coherent or non-contradictory pieces of evidence, but most of all they are the counterparts of consistent knowledge bases in belief calculus. The use of consistent transformations cs[.] in a reasoning process to guarantee coherence can therefore be desirable, and generalizes similar techniques in classical logic. Transformations can be obtained by minimizing an appropriate distance measure between the original belief function and the collection of consistent ones. We focus here on the case in which distances are measured using classical Lp norms, in both the "mass space" and the "belief space" representation of belief functions. While mass consistent approximations reassign the mass not focussed on a chosen element of the frame either to the whole frame or to all supersets of the element on an equal basis, approximations in the belief space do distinguish these focal elements according to the "focussed consistent transformation" principle. The different approximations are interpreted and compared, with the help of examples.
Belief Evolution Network-based Probability Transformation and Fusion
University of Electronic Science and Technology of China（电子科技大学）；Shannxi Normal University（陕西师范大学）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；ETH Zurich（苏黎世联邦理工学院）
Isopignistic Canonical Decomposition via Belief Evolution Network
University of Electronic Science and Technology of China（电子科技大学）；Institute of Fundamental and Frontier Science（基础与前沿研究院）；Vanderbilt University（范德堡大学）；School of Medicine（医学院）
On the use of evidence theory in belief base revision
Institut Supérieur d’Informatique et de Multimédia, Université de Sfax（斯法克斯大学高等信息与多媒体学院）；Laboratoire OLID (LR19ES21), Institut Supérieur de Gestion Industrielle, Université de Sfax（斯法克斯大学高等工业管理学院OLID实验室（LR19ES21））；Institut des Hautes Etudes Commerciales, Université de Sfax（斯法克斯大学高等商业研究学院）
Institute of Fundamental and Frontier Science, UESTC（电子科技大学基础与前沿研究院）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；School of Education, SNNU（陕西师范大学教育学院）；ETH Zurich（苏黎世联邦理工学院）
Are LLM Belief Updates Consistent with Bayes' Theorem?
Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境中心）；School of Computing and Communications, Lancaster University（兰卡斯特大学计算机与通信学院）；Environmental Science, University of Southampton（索姆塞特大学环境科学系）；College of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）；University of Tuebingen, Department of Computer Science（图宾根大学计算机科学系）；Graduate School of Natural Sciences, Faculty of Science, Utrecht University（乌得勒支大学自然科学研究生院）
Evidential Information Fusion on Possibilistic Structure
School of Electronics and Information, Northwestern Polytechnical University（电子信息学院，西北工业大学）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）；China Mobile Information Technology Center（中国移动信息科技中心）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China（中国电子科技大学基础与前沿科学研究院）
Position: agentic AI orchestration should be Bayes-consistent
ESSEC Business School（ESSEC商学院）；VinUniversity（文大学）；Imperial College London（伦敦帝国理工学院）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Technical University of Denmark（丹麦技术大学）；Pyramidal Inc.（Pyramidal公司）；University of Notre Dame（诺特大学）；Cognizant AI Lab（Cognizant人工智能实验室）；University College London（伦敦大学学院）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Ghent University（根特大学）；New York University（纽约大学）
