• Computer Science > Multiagent Systems [Submitted on 17 Feb 2026] Title:Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance View PDF HTML (experimental)Abstract:Industrial IoT predictive maintenance requires systems capable of real-time anomaly detection without sacrificing interpretability or demanding excessive computational resources. • Traditional approaches rely on static, offline-trained models that cannot adapt to evolving operational conditions, while LLM-based monolithic systems demand prohibitive memory and latency, rendering them impractical for on-site edge deployment. • We introduce SEMAS, a self-evolving hierarchical multi-agent system that distributes specialized agents across Edge, Fog, and Cloud computational tiers. • Edge agents perform lightweight feature extraction and pre-filtering; Fog agents execute diversified ensemble detection with dynamic consensus voting; and Cloud agents continuously optimize system policies via Proximal Policy Optimization (PPO) while maintaining asynchronous, non-blocking inference. • The framework incorporates LLM-based response generation for explainability and federated knowledge aggregation for adaptive policy distribution. • This architecture enables resource-aware specialization without sacrificing real-time performance or model interpretability.
Article Summaries:
- Computer Science > Multiagent Systems [Submitted on 17 Feb 2026] Title:Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance View PDF HTML (experimental)Abstract:Industrial IoT predictive maintenance requires systems capable of real-time anomaly detection without sacrificing interpretability or demanding excessive computational resources. Traditional approaches rely on static, offline-trained models that cannot adapt to evolving operational conditions, while LLM-based monolithic systems demand prohibitive memory and latency, rendering them impractical for on-site edge dep
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