AI 中文总结
研究智能体人工智能风险表示问题，提出CPSAINT和FRIESA-K结合故障机制与风险估计，通过单独惩罚报告治理可观测性，形式化结构可组合性，在两个场景展示框架，获支持跨域推理等的紧凑内核。
AI 中文摘要
智能体人工智能跨越信任边界的速度超过了当前风险模型的表示能力。现有方法提供了两种不完整的观点之一。它们要么描述故障机制但不产生可转移的残余风险估计，要么在将内部故障路径视为黑箱的情况下产生风险估计。我们通过提出CPSAINT（一种在物理状态、传感器、数据、计算、执行器、环境和时间上的七层完整性分解）与FRIESA-K（一种将每个故障路径映射到量化风险实例的残余风险函数）来结合这两种观点。FRIESA-K将阻力项K基于受控吸收马尔可夫模型，使控制有效性从状态动态中得出而非作为非正式分数分配。结果是一个用于弹性智能体和具身人工智能的简洁机制到量级的管道。我们通过单独的附加惩罚来报告治理可观测性，而不是在阻力函数中插入治理作为新变量。我们形式化了将有效故障路径与定义明确的风险实例联系起来的结构可组合性，并在两个对比场景（一个硬实时仓库机器人和一个有治理工具的金融服务智能体）中展示了该框架。在这两种情况下，相同的层语法、变量语义和动态阻力构建保持不变。因此，我们获得了一个支持跨域推理、明确假设和可组合信任的定量基础形式主义的紧凑内核。
英文摘要
Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views. They either describe failure mechanisms without producing a transferable residual-risk estimate, or they produce a risk estimate while treating the internal failure path as a black box. We couple those two views by proposing CPSAINT, a seven-layer integrity decomposition over Physical state, Sensors, Data, Compute, Actuators, Environment, and Time, paired with FRIESA-K, a residual-risk functional that maps each failure path to a quantified risk instance. FRIESA-K grounds the resistance term K in a controlled absorbing Markov model so that control effectiveness is derived from state dynamics rather than assigned as an informal score. The result is a concise mechanism-to magnitude pipeline for resilient agentic and embodied AI. We report governance observability through a separate additive penalty instead of inserting governance as a new variable in the resistance functional. We formalize structural composability linking valid failure paths to well-defined risk instances and show the framework on two contrasting scenarios a hard real-time warehouse robot and a governance-instrumented financial-services agent. Across both cases, the same layer grammar, variable semantics, and dynamic-resistance construction remain intact. Thus, we obtain a compact kernel that supports cross-domain reasoning, explicit assumptions, and quantitatively grounded formalism of composable trust.
