Can You Check That? The Checkability Boundary for Local LLM Network Automation

AI Digest - ArXiv AI

Can You Check That? The Checkability Boundary for Local LLM Network Automation

Sending every network-automation input to a third-party frontier LLM exports sensitive artifacts such as production configurations, topologies, and logs. Querying small language models (SLMs) locally avoids this egress, but SLM outputs can be error-prone for direct use. This work introduces checkability as a criterion for determining which tasks are suitable for local inference. A task is checkable when it exposes a cheap, deterministic test - an intrinsic check - that rejects outputs violating

Source: ArXiv AI