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.

翻译：当前用于生理信号监测的深度学习方法存在拓扑结构静态和能耗恒定的问题。本文提出SGEMAS（自生长瞬态多智能体系统），这是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（智能体生灭）与变分自由能目标耦合，系统能够以极高的稀疏性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，在智能体动力学中加入多尺度不稳定性指标可显著提升性能。在具有挑战性的跨患者零样本设定下，最终SGEMAS v3.3模型取得了0.570 ± 0.070的平均AUC，优于其简化变体及标准自编码器基线。该结果验证了基于物理学的能量约束模型能够实现鲁棒的无监督异常检测，为高效生物医学人工智能提供了有前景的研究方向。