Frontier AI Safety Policies concentrate on prevention: capability evaluations, deployment gates, and usage constraints, while neglecting the capacity to coordinate responses when prevention fails. We argue this coordination gap is structural: investments in ecosystem robustness yield diffuse benefits but concentrated costs, generating systematic underinvestment. Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, we propose that similar mechanisms (precommitment, shared protocols, standing coordination venues) could be adapted to frontier AI governance. Closing the gap requires cross-actor "note-exchange" of ex ante if-then response logic, exposing not only triggers but the decision processes that convert signals into actions. Without such architecture, institutions cannot learn from failures at the pace of relevance.

翻译：前沿人工智能安全政策集中于预防：能力评估、部署门控与使用约束，却忽视了当预防失败时协调各方反应的能力。我们认为这一协调缺口具有结构性特征：对生态系统稳健性的投入会产生分散收益但集中成本，从而导致系统性的投入不足。借鉴核安全、大流行防范及关键基础设施领域的风险治理模式，我们提出类似机制（预先承诺、共享协议、常设协调场所）可适用于前沿人工智能治理。填补这一缺口需要行为体之间进行"注释交换"——提前设定"如果-那么"式响应逻辑，不仅暴露触发条件，更要揭示将信号转化为行动的决策过程。缺乏此类架构，机构将无法以与风险演变相匹配的速度从失败中汲取教训。