AI Safety Depends on Interaction Topology, Not Model Scale

A new position paper argues that safety in agentic AI is determined by interaction topology rather than model weights or alignment. The paper identifies three topology-driven pathologies: ordering instability, information cascades, and functional collapse. Evidence across model families and scales shows that scaling to more capable models does not mitigate these issues. The findings challenge the assumption that safe individual models compose into safe multi-agent behavior.