位置：代理AI的安全性与公平性取决于交互拓扑，而非模型规模或对齐
Position: Safety and Fairness in Agentic AI Depend on Interaction Topology, Not on Model Scale or Alignment
AI总结：
本文指出代理AI的安全性由交互拓扑决定，而非模型规模或对齐程度。研究揭示了顺序不稳定、信息级联和功能崩溃等拓扑驱动的问题，并强调需通过动态系统视角评估安全性和公平性。
摘要：
随着大型语言模型越来越多地作为交互代理用于高风险决策，人工智能安全社区假设单个模型的安全属性会组成安全的多代理行为。本文认为这一假设根本错误。在代理AI中，安全性由交互拓扑决定，而非模型权重。当代理依次 deliberative 或通过平行投票与裁判聚合时，信息流和决策耦合的结构主导结果。跨模型家族和规模的证据揭示了三种持续的拓扑驱动病理：顺序不稳定，系统行为主要依赖代理序列；信息级联，早期判断传播不受正确性影响；功能崩溃，系统满足公平度量但放弃有意义的风险区分。与直觉相反，向更强大模型扩展会通过增加共识形成强化这些效果，并减少初始决策的挑战。这些失败模式对模型中心的评估和对齐程序不可见。本文认为代理AI必须被视为动态系统而非对齐组件的集合。交互拓扑必须成为安全评估和监管的主要目标，系统需在部署前展示在架构变化下的鲁棒性。
英文摘要：
As large language models are increasingly deployed as interacting agents in high-stakes decisions, the AI safety community assumes that safety properties of individual models will compose into safe multi-agent behavior. This position paper argues that this assumption is fundamentally mistaken. In agentic AI, safety is determined by interaction topology, not model weights. When agents deliberate sequentially or aggregate via parallel voting with a judge, the structure of information flow and decision coupling dominates outcomes. Evidence across model families and scales reveals three persistent topology-driven pathologies: ordering instability, where system behavior depends primarily on agent sequence; information cascades, where early judgments propagate regardless of correctness; and functional collapse, where systems satisfy fairness metrics while abandoning meaningful risk discrimination. Contrary to intuition, scaling to more capable models strengthens these effects by increasing consensus formation and reducing the challenge of initial decisions. These failure modes are invisible to model-centric evaluation and alignment procedures. We argue that agentic AI must be treated as a dynamical system rather than a collection of aligned components. Interaction topology must become a primary target of safety evaluation and regulation, with systems required to demonstrate robustness across architectural variations before deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
