SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis

TL;DR

SGEMAS is a bio-inspired, self-growing multi-agent system that dynamically adapts its structure to improve unsupervised anomaly detection in physiological signals, emphasizing energy efficiency and robustness.

Contribution

Introduces SGEMAS, a novel self-growing, energy-efficient multi-agent architecture with structural plasticity for unsupervised anomaly detection in biomedical signals.

Findings

Outperforms baseline models in zero-shot anomaly detection tasks.

Adding a multi-scale instability index improves detection performance.

Achieves a mean AUC of 0.570 in challenging inter-patient scenarios.

Abstract

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.