BUAA-Yang-MC2

Authors

shibo yang (Beihang university)

Abstract

We propose a trace-based co-intelligence visual analytics framework and apply it to VAST Challenge 2026 Mini-Challenge~2. The framework records the analytical work of autonomous agents as an inspectable workflow trace comprising task specifications, intermediate artifacts, data transformations, visual evidence, and candidate findings. It enables analysts to guide the agent's analytical process, scrutinize the evidential basis of generated conclusions, and revise subsequent tasks. Applied to Mini-Challenge~2, the framework enabled the identification of a propagation chain spanning 7.8 days and comprising 191 events, which carried extbf{ exttt{SwiftWren.txt}} to a SaidIT post, as well as two historical incidents sharing the same operational pattern. These findings motivated our proposal of a validation mechanism at the common final publication step. The workflow trace further supported human validation of the agent's analysis.