Phase transitions in the functional connectivity of semantic agent networks under local filtering constraints

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.