Non-verbalised or secret collusion may impose a ceiling on transcript-level monitoring in multi-agent deployments. Internal model activations are known to provide an additional monitoring surface for single-agent phenomena such as deception, but detecting collusion within an unknown subgroup is ultimately a joint inference task over multiple agents. This paper derives collusion detection statistics based on the composite hypothesis testing literature and introduces HELIOS, a multi-agent detection framework for collusion. Using per-agent activation probes as sensors, we then evaluate these principled statistics alongside heuristics on NARCBench, a novel benchmark for evaluating multi-agent collusion detection under environment distribution shift. Across four open-weight models, both our group-level statistics and heuristics transfer zero-shot to NARCBenchs holdout environments. Notably, the best method on every model reaches 0.99 to 1.00 AUROC when transferred from committee deliberations to a steganographic collusion task that unprimed transcript monitors fail to separate. We find empirically that no single statistic or heuristic dominates across tasks, consistent with our analysis that there is no known universally most powerful test for this setting. We then evaluate detector transfer to third-party environments with emergent, rather than instructed, collusion. In one such environment, only 2 of 229 collusive episodes verbalise the coordination in the transcript. We find evidence of partial transfer from NARCBench to these environments, indicating that probes fitted to instructed collusion may not fully capture emergent collusion signals. Overall, we establish multi-agent interpretability as a complementary monitoring technique, while highlighting the need to evaluate detector performance on both emergent and instructed collusion risks.
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection
School of Information and Communication Technology, Griffith University, Australia（格里菲斯大学信息与通信技术学院）；School of Artificial Intelligence, Jilin University, China（吉林大学人工智能学院）；Department of Statistics and Data Science, Northwestern University, USA（西北大学统计与数据科学系）
Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks
Department of Numerical Analysis（数值分析系）；Eötvös Loránd University（欧多维奇·劳尔大学）；System and Control Laboratory（系统与控制实验室）；HUN-REN Institute for Computer Science and Control（HUN-REN计算机科学与控制研究所）；UniDistanceSuisse；Siemens Mobility Kft.（西门子移动有限公司）；HUN-REN Centre for Energy Research（HUN-REN能源研究中心）
Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems
Computer and Information Science Department, Fordham University（福特汉姆大学计算机与信息科学系）；RENCI, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校RENCI）；Department of Mathematics and Computer Science, Royal Military College of Canada（加拿大皇家军事学院数学与计算机科学系）；Data Science Department, University of North Texas（德克萨斯大学纳夫卡特数据科学系）
Detecting Multimedia Generated by Large AI Models: A Survey
Department of Computer and Information Technology, Purdue University（普渡大学计算机与信息科技系）；School of Software, Nanchang University（南昌大学软件学院）；Amazon Prime Video（亚马逊Prime视频）；Department of Epidemiology and Biostatistics, School of Public Health（公共卫生学院流行病学与生物统计学系）；Department of Computer Science, College of Nanotechnology, Science, and Engineering（纳米技术、科学与工程学院计算机科学系）；University at Albany, SUNY（阿尔巴尼大学）
Detecting Children with Autism Spectrum Disorder based on Script-Centric Behavior Understanding with Emotional Enhancement
School of Computer Science, Wuhan University（武汉大学计算机学院）；Digital Innovation Research Center, Duke Kunshan University（杜克昆山大学数字创新研究中心）；School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子与信息学院）；Child Development and Behavior Center, Third Affiliated Hospital of Sun Yat-sen University（中山大学第三附属医院儿童发展与行为中心）
