Verifying the provenance of text is increasingly important for firms, educational institutions, and online platforms as Large Language Models (LLMs) produce output that is nearly indistinguishable from human-generated content. We study the problem of determining whether a given text was generated by a particular LLM while controlling the false positive rate. We model LLM-generated text as a sequential stochastic process and develop training-free statistical tests to (i) distinguish between text produced by two known sets of LLMs and (ii) determine whether text was generated by a known LLM or by a distinguishable unknown source, such as a human or another model. We prove that both Type I and Type II errors decay exponentially with text length, establish analogous guarantees for black-box access via sampling, and provide an information-theoretic lower bound showing that there exist model pairs for which no statistical test can make both errors decay faster than exponentially with text length. Numerical experiments empirically evaluate the tests in practical settings and demonstrate strong overall performance, including under many adversarial edits. Our framework provides rigorous guarantees for LLM provenance detection, with applications to content verification, institutional compliance, and misinformation mitigation.
Source Attribution for Large Language Model-Generated Data
School of Computing（计算机学院）；National University of Singapore（新加坡国立大学）；Institute for Infocomm Research（信息通信研究院）；Agency for Science, Technology and Research（新加坡科技研究局）
On Synthesizing Data for Context Attribution in Question Answering
NEC Laboratories Europe（NEC欧洲实验室）；NEC Laboratories America（NEC美国实验室）；NEC Corporation（NEC公司）；University College London（伦敦大学学院）；KU Leuven（鲁汶大学）；Epimind；Center for Artificial Intelligence and Data Science, University of Würzburg（维尔茨堡大学人工智能与数据科学中心）；CAIR, Ss. Cyril and Methodius University of Skopje（斯科普里圣西里尔和美多迪乌斯大学CAIR）
Theory of Mind and Self-Attributions of Mentality are Dissociable in LLMs
Google, Paradigms of Intelligence Team（Google，智能范式团队）；Knowledge Lab, University of Chicago（芝加哥大学知识实验室）；Institute of Philosophy, School of Advanced Study, University of London（伦敦大学高等研究院哲学研究所）；Department of Biomedical Informatics and Medical Education and Department of Bioethics and Humanities, School of Medicine, University of Washington（华盛顿大学医学院生物医学信息学与医学教育系及生物伦理学与人文学系）；Kellogg School of Management, Northwestern University（西北大学凯洛格管理学院）；Santa Fe Institute（圣塔菲研究所）
Dual-Axis Policy Optimization for LLM Agents: Bayesian Feedback Attribution and Trajectory Mass Normalization
Zhejiang University（浙江大学）；University of Science and Technology of China（中国科学技术大学）；University of New South Wales（新南威尔士大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance
Department of Civil and Environmental Engineering, National University of Singapore（新加坡国立大学土木与环境工程系）；School of Transportation Science and Engineering, Harbin Institute of Technology（哈尔滨工业大学交通运输科学与工程学院）；Research and Innovation, NovaCITYNETS Pte.Ltd.（NovaCITYNETS Pte.Ltd.研究与创新部）
Department of Computer & Information Science & Engineering, University of Florida（佛罗里达大学计算机与信息科学与工程系）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）；Department of Electrical Engineering and Computer Science, United States Military Academy（美国军事学院电子工程与计算机科学系）
Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning
School of Computing；AI Arizona State University Tempe, AZ；Department of Computer Science Illinois Institute of Technology Chicago, IL；Department of Computer Science Emory University Atlanta, GA
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
Pennsylvania State University（宾夕法尼亚州立大学）；Duke University（杜克大学）；University of Washington（华盛顿大学）；Oregon State University（俄勒冈州立大学）；Google DeepMind（谷歌DeepMind）；Nanyang Technological University（南洋理工大学）；Meta；AG2AI, Inc.（AG2AI公司）
Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation
Center for Language and Cognition (CLCG), University of Groningen（格罗宁根大学语言与认知中心（CLCG））；Institute for Logic, Language and Computation (ILLC), University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所（ILLC））
