As AI systems proliferate, their collective safety depends not only on individual align ment but on the structural properties of their interaction networks. We investigate the network-level consequences of local safety filtering in decentralized agent populations. Uti lizing a statistical physics framework, we model safety constraints as semantic filters that selectively prune communication edges. We demonstrate that strictly aligned agents in high-dimensional policy spaces induce a sharp phase transition (Experiment E1), causing a sudden collapse in functional connectivity (Sfunc) even when the underlying infrastructure remains intact (Sstruct). Furthermore, we find that message complexity acts as a fragility multiplier (Experiment E2), exponentially shifting the critical threshold. Our results show that purely local mitigation strategies are insufficient near the critical point (Experiment E6), requiring hub-targeted interventions to maintain system-wide coordination. These find ings provide a theoretical basis for designing resilient multi-agent architectures that balance safety with functional consensus.
Indrajith P. Karunanayaka (Mon,) studied this question.