用动态自生的智能体系统实现低能耗异常检测，适合医疗信号实时监控。
SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis
- 通过智能体出生死亡机制与能量优化结合，实现动态结构进化。
- 在跨患者零样本场景下平均AUC达0.570，优于自编码器基线。
- 基于物理能量约束，适合资源受限的生物医学应用。
当前生理信号监测的深度学习方法存在拓扑结构固定和能耗恒定的问题。本文提出SGEMAS（自生长瞬态多智能体系统），一种受生物学启发的架构，将智能视为动态热力学过程。通过将结构可塑性机制（智能体的生成与消亡）与变分自由能目标相结合，系统能够以极低的稀疏度自然演化以最小化预测误差。在MIT-BIH心律失常数据库上的消融实验表明，在智能体动态中引入多尺度不稳定性指数可显著提升性能。在具有挑战性的跨患者、零样本设置下，最终的SGEMAS v3.3模型实现了0.570 ± 0.070的平均AUC，优于其简化版本及标准自编码器基线。该结果验证了基于物理能量约束的模型在无监督异常检测中的鲁棒性，为高效生物医学人工智能提供了新方向。
原文摘要 · Abstract (English)
Current deep learning approaches for physiological signal monitoring suffer from static topologies and constant energy consumption. We introduce SGEMAS (Self-Growing Ephemeral Multi-Agent System), a bio-inspired architecture that treats intelligence as a dynamic thermodynamic process. By coupling a structural plasticity mechanism (agent birth death) to a variational free energy objective, the system naturally evolves to minimize prediction error with extreme sparsity. An ablation study on the MIT-BIH Arrhythmia Database reveals that adding a multi-scale instability index to the agent dynamics significantly improves performance. In a challenging inter-patient, zero-shot setting, the final SGEMAS v3.3 model achieves a mean AUC of 0.570 +- 0.070, outperforming both its simpler variants and a standard autoencoder baseline. This result validates that a physics-based, energy-constrained model can achieve robust unsupervised anomaly detection, offering a promising direction for efficient biomedical AI.
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