SGEMAS：一种自生长瞬时多智能体系统，用于通过熵稳态的无监督在线异常检测
SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis
AI总结：
SGEMAS通过熵稳态机制实现自生长多智能体系统，用于无监督在线异常检测，实验表明其在跨患者零样本设置中性能优于传统方法。
摘要：
当前用于生理信号监测的深度学习方法存在静态拓扑和恒定能耗的问题。我们介绍了SGEMAS（自生长瞬时多智能体系统），一种受生物启发的架构，将智能视为动态热力学过程。通过将结构可塑性机制（智能体出生死亡）与变分自由能目标相结合，系统自然演变为最小化预测误差并具有极端稀疏性。在MIT-BIH心律失常数据库上的消融研究显示，将多尺度不稳定性指数添加到智能体动态中显著提高了性能。在具有挑战性的跨患者、零样本设置中，最终的SGEMAS v3.3模型实现了平均AUC为0.570±0.070，优于其更简单的变体和标准自动编码器基线。这一结果验证了基于物理、能量受限的模型能够实现稳健的无监督异常检测，为高效生物医学AI提供了一个有前景的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
