Self-Supervised Behavioral Risk Monitoring for Large Language Models in Edge Intelligence Environments
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Abstract
This article develops a technical framework for self-supervised behavioral risk monitoring of large language models deployed in edge intelligence environments. Inspired by recent work on deception detection for Edge-of-Things systems, the paper broadens the problem from narrow deception labels to a wider class of trust failures that include concealed unsafe plans, hallucinated justifications, unstable tool use, retrieval mismatch, and context-dependent policy evasion. The core argument is that trustworthy edge deployment requires more than compressed inference; it requires a native monitoring layer that can transform local reasoning traces, uncertainty cues, and tool telemetry into actionable risk signals without relying continuously on cloud-based judge models. The paper synthesizes research on edge computing, language-model alignment, quantization, chain-of-thought reasoning, retrieval augmentation, and intrusion detection, and uses that synthesis to propose a layered architecture composed of a primary reasoning model, a lightweight monitor head, a telemetry collector, and a policy guard. It further outlines implementation pathways, evaluation metrics, and governance implications for privacy-sensitive and latency-critical domains. The contribution lies in showing how self-supervised trust monitoring can become a practical design pattern for edge AI when computational efficiency, behavioral reliability, and organizational accountability are engineered together.
