Can AI Agents Detect and Repair Artifact Drift in Network Experiments?
In recent years, AI agents have evolved into capable assistants that carry out multi-step tasks in digital environments. The network systems community is beginning to explore these capabilities in operational and experimental settings. However, an agent operating in network systems should not be judged solely by whether it completes the immediate task. The experiment record it modifies must also remain trustworthy. We call this property artifact integrity: the record's claims must remain supported by the available evidence, confined to the scope established by that evidence, and traceable through the artifacts that encode their support. To make this property measurable, we introduce NetAr...
| Authors | Tianzhu Zhang, Weichen Tao, Changgang Zheng et al. |
|---|---|
| Primary category | cs.NI |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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