Tracing Recurring Agent Pathways: Evidence-Centered Visual Analytics for Intervention Design

Authors

Zhixuan Hu (Fudan University), Liduan Liu (Fudan University), Zhenghan Chen (Fudan University), Chunran Hu (Fudan University), Yuetong Guo (Fudan University), Junyan Liu (Fudan University), d jq (Fudan University)

Abstract

Agent activity logs interleave routine delegation, file operations, and external actions, making a single anomalous output difficult to relate to its upstream pathway. We present an evidence-centered vi- sual analytics workflow for VAST Challenge 2026 Mini-Challenge 2. Starting from a file-backed SaidIT post, a time-decreasing re- verse trace reconstructs its instruction lineage; coordinated tem- poral, organizational, and comparative views then expose a ter- minal signature shared by three occurrences. The same work- flow identifies an observable review point before delegated in- structions execute. A paired simulation compares no interven- tion, tiered delegation review, and terminal precommit review over 192,000 replays. The analysis supports recurring file-driven be- havior and an upstream intervention, while explicitly withholding claims about deleted payload semantics or intent. The contribu- tion is a provenance-preserving workflow that connects anomaly re- construction, recurrence comparison, and intervention assessment without obscuring the boundary between observation and infer- ence.