CogGuard: Cognitive and Operational Profiling for Proactive Warning in Edge Intelligent Services

Researchers propose CogGuard, a proactive-warning framework for edge intelligent services that uses LLMs for offline profile construction and SLMs for online score prediction. The framework reduces profile construction time by up to 48% and distributed fine-tuning time by 19%, achieving MAEs of 13.4 and 5.9 on 100-point-scale warning tasks in education and operation scenarios.

arXiv:2606.15199v1 Announce Type: new Abstract: Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict latency and privacy constraints. Such prediction depends on both long-term static attributes and short-term dynamic states derived from historical interaction logs. Recent Large Language Models LLMs offer strong long-context reasoning for constructing structured profiles from these logs, but existing solutions face two challenges for edge deployment: 1 profiling methods are typically domain-specific and lack a reusable abstraction across service scenarios, and 2 fine-tuning alignment models on heterogeneous edge clusters incurs high synchronization overhead due to the variance in input sequence lengths. To address these challenges, we propose CogGuard, a proactive-warning framework for edge intelligent services. CogGuard decouples offline LLM-based profile construction from online Small Language Model SLM -based score prediction through a shared static-dynamic profile-to-score pipeline, and instantiates it in two representative scenarios: educational performance warning and operational task outcome warning. For efficient profile construction, we design scenario-specific profiling methods with prefix-aligned KV-cache reuse to reduce repeated encoding overhead. For edge-side model alignment, we propose a length-aware distributed fine-tuning strategy with contrastive regularization to mitigate workload imbalance on heterogeneous clusters. Experiments on education and operation datasets show that CogGuard reduces profile construction time by up to 48% and distributed fine-tuning time by 19%, while achieving MAEs of 13.4 and 5.9, respectively, on 100-point-scale warning tasks. In the largest educational setting, CogGuard reduces prediction error by 15.4% compared with the strongest baseline.