Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must be able to inspect the process of analytic workflows to assess (1) confidence of the conclusions, (2) risks and biases of the operations involved, (3) sensitivity of the conclusions to sources and agents, (4) impact and pertinence of various sources and agents, and (5) diversity of the sources that support the conclusions. We present an approach that tracks agents' provenance with PROV-O in conjunction with agents' appraisals and evidence links (expressed in our novel DIVE ontology). Together, PROV-O and DIVE enable dynamic propagation of confidence and counter-factual refutation to improve human-machine trust and analytic integrity. We demonstrate representative software developed for user interaction with that provenance, and discuss key needs for organizations adopting such approaches. We demonstrate all of these assessments in a multi-agent analysis scenario, using an interactive web-based information validation UI.
Chronology of Multi-Agent Interactions for Provenance of Evolving Information
Information and Society Research Division, National Institute of Informatics（信息与社会研究部，国家信息研究所）；Graduate School of Information Science and Technology, University of Tokyo（东京大学信息科学与技术研究生院）；School of Multidisciplinary Sciences, Graduate University for Advanced Studies (SOKENDAI)（多学科科学系，研究生高级研究大学（SOKENDAI））
BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery
Peking University（北京大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Pennsylvania（宾夕法尼亚大学）；Purdue University（普渡大学）；Stanford University（斯坦福大学）
Over the recent twenty years, argumentation has received considerable attention in the fields of knowledge representation, reasoning, and multi-agent systems. However, argumentation in dynamic multi-agent systems encounters the problem of significant arguments generated by agents, which comes at the expense of representational complexity and computational cost. In this work, we aim to investigate the notion of abstraction from the model-checking perspective, where several arguments are trying to defend the same position from various points of view, thereby reducing the size of the argumentatio…
From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
Griffith University（格里菲斯大学）；Jiangsu University（江苏大学）；University of Southern Queensland（南方昆士兰大学）；Peking University（北京大学）；Great Bay University（大湾大学）；Nanjing University（南京大学）；Macquarie University（麦觉瑞大学）；Southern University of Science and Technology（南方科学与技术大学）
The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院 Gallagher 学院）；King Abdullah University of Science and Technology（国王 Abdullah 科学技术大学）；Dongbei University of Finance and Economics（东北财经大学）；University of Science and Technology of China（中国科学技术大学）
Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation
Faculty of Computer and Information System（计算机与信息系统学院）；Islamic University of Madinah（麦地那伊斯兰大学）；Faculty of Computer Studies（计算机研究学院）；Arab Open University-Bahrain（巴林阿拉伯开放大学）
ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents
Multiverse Computing；Parque Cientifico y Tecnológico de Gipuzkoa（吉普斯夸科技园）；Centre for Social Innovation（社会创新中心）；Donostia International Physics Center（多诺斯蒂亚国际物理中心）；Ikerbasque Foundation for Science（伊克尔巴斯克科学基金会）
Many real-world planning domains involve diverse information sources, external entities, and variable-reliability agents, all of which may impact the confidence, risk, and sensitivity of plans. Humans reviewing a plan may lack context about these factors; however, this information is available during the domain generation, which means it can also be interwoven into the planner and its resulting plans. This paper presents a provenance-based approach to explaining automated plans. Our approach (1) extends the SHOP3 HTN planner to generate dependency information, (2) transforms the dependency inf…
How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation
NYU Tandon School of Engineering（纽约大学坦登工程学院）；NYU Abu Dhabi（纽约大学阿布扎比分校）；CISPA - Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；International Institute of Information Technology Hyderabad（海得拉巴国际信息技术学院）；Indian Institute of Technology Tirupati（蒂鲁帕蒂印度理工学院）
PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；University of Macau（澳门大学）；National Institute of Informatics（国立信息学研究所）；University of Electronic Science and Technology of China（电子科技大学）
MemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs
Shanghai Jiao Tong University（上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；National University of Singapore（新加坡国立大学）；Xidian University（西安电子科技大学）；University of Science and Technology of China（中国科学技术大学）；MemTensor (Shanghai) Technology Co., Ltd.（MemTensor（上海）科技有限公司）
