Let AI Agents Translate Networks, Not Reason About Them
A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network has one, since writing a model by hand demands rare expertise and is hard to keep current as the network changes frequently. At its core, network modeling is a typographical exercise: it translates network artifacts (e.g., configurations, topology, and routing state) into rules in formal logic. Translation of this kind is what large language models (LLMs) nowadays do well. Unlike free-form AI reasoning, such translation can be formally verified. Once modeling is no longer the bottleneck, trusting AI to reason over large, complex networks n...
| Authors | Hongyu Hè, Maria Apostolaki |
|---|---|
| Primary category | cs.AI |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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