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

arXiv: https://arxiv.org/abs/2606.15199 · HTML: https://mlsanity.com/paper/2606.15199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 42 references that are in the map.
- First posted on arXiv 13 Jun 2026.

## Builds on (most cited first, 30 of 30)

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- [Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective](https://mlsanity.com/paper/2410.17600/) (2024)
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- [CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models](https://mlsanity.com/paper/2401.08438/) (2024)
- [What BERT Based Language Models Learn in Spoken Transcripts: An Empirical Study](https://mlsanity.com/paper/2109.09105/) (2021)
- [Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism](https://mlsanity.com/paper/2412.21124/) (2024)
- [SemRAG: Semantic Knowledge-Augmented RAG for Improved Question-Answering](https://mlsanity.com/paper/2507.21110/) (2025)
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- [EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning](https://mlsanity.com/paper/2507.10026/) (2025)
- [A difficulty ranking approach to personalization in E-learning](https://mlsanity.com/paper/1907.12047/) (2019)
- [FlashBack: Efficient Retrieval-Augmented Language Modeling for Fast Inference](https://mlsanity.com/paper/2405.04065/) (2024)
- [Chaos Engineering: A Multi-Vocal Literature Review](https://mlsanity.com/paper/2412.01416/) (2024)

## Abstract (arXiv)

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.