Zhejiang University of Science and Technology-Yanni Peng-MC2

Authors

Yanni Peng (Zhejiang University of Science & Technology), Yi Zhang (Zhejiang University of Science & Technology ), Ye Fu (Zhejiang University of Science & Technology), Qiuyu Wang (Zhejiang University of Science & Technology)

Abstract

Abstract Multi-agent collaboration systems automate organizational operations, yet their complexity and opacity make anomaly investigation challenging. Due to unmapped interaction paths and black-box behavior, traditional log-based approaches struggle to support investigations across multiple levels and diverse event types of anomalous posting incidents. This paper presents a visual analytics approach for tracing the covert publication chains of anomalous posts and uncovering their content origins, providing users with three complementary tools: SGTViz, PCTViz, and PDTViz. Our solution focuses on event tracking and content analysis, and effectively supports analysis of the VAST 2026 Mini-Challenge 2 case. Index terms: Visual analytics, event tracking, anomaly detection, multi-agent systems, content tracing.