Homeostatic Swarm Intelligence: Physiological Hormone Signaling for Resilient Multi-Agent AGI

Distributed artificial general intelligence promises scalable reasoning in robotics, edge networks, and multi-agent systems, yet suffers from brittleness: cascading failures, load imbalances, and coordination drift under partial agent loss or resource stress. This paper extends the Bio-Regulated Mind Matrix to multi-agent swarms, treating each agent as a physiological organism with shared endocrine-like hormone signaling for collective homeostasis. A lightweight hormone state vector h = [c, d, o]—cortisol analog for stress diffusion, dopamine for reward propagation, and oxytocin for cooperation synchronization—is broadcast via consensus protocols, enabling emergent behaviors: dynamic load balancing, self-healing through agent isolation/reintegration, and adaptive specialization. Quantitative evaluations on cooperative multi-agent benchmarks with failure injection demonstrate superior resilience (e.g., 95% task completion under 30% agent failure vs. 60% baseline), efficiency (40% reduced compute variance), and minimal communication overhead (3 floats per message vs. full state sync). Pseudocode supports integration with transformer-based agents, optionally layered with DNA-substrates and pruning/sealing for enhanced robustness. This framework provides a scalable foundation for fault-tolerant, decentralized AGI in real-world deployments.