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

Paper recorded by Signals 4 on 2026-09-25 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-25 on arXiv · recorded by Signals 4 on 2026-09-28

Category: cs.AI · 人工智能 · first seen 2026-09-28

Abstract

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 checkabl

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#9 most recent of 420 cs.AI papers we have recorded · ↑ newer: A Flow Matching Framework for Neural Representational Dissimilarity · ↓ older: ClearGS: Reliability-Aware Gaussian Splatting from Handheld Videos
Cite this page: Can You Check That? The Checkability Boundary for Local LLM Network Automation: the #9 most recent of 420 cs.AI papers we have recorded (as of 2026-09-25). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/can-you-check-that-the-checkability-boundary-for-local-llm-network-automation.html
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