Immune to Persistence Subtitle: A Unified Framework for Resilience in Complex Networks

This work introduces a systems-level framework for understanding resilience in complex networks by reframing stability as immunity to persistence rather than immunity to disturbance. In many systems, large perturbations can be absorbed, while smaller but self-reinforcing disruptions lead to fragmentation and collapse. The framework presented here explains this asymmetry by focusing on the relative rates of disruption and correction. Using abstract, domain-agnostic variables—such as coherence, local resistance, hub structure, and recovery velocity—the paper shows that recovery often occurs as a nonlinear phase transition. Once resistance exceeds a critical threshold, coherence rapidly re-establishes itself, producing a characteristic “snap-back” behavior rather than gradual improvement. The framework is intentionally descriptive, not prescriptive. It does not propose biological mechanisms, medical interventions, policy actions, or new physical laws. Instead, it offers a unifying architectural lens applicable across biological, cognitive, social, and technological systems, while remaining compatible with existing empirical and theoretical work in network science, dynamical systems, and resilience theory. By emphasizing persistence suppression and adaptive constraint, Immune to Persistence provides a compact conceptual model for analyzing why some networks recover quickly from stress while others enter chronic instability.