发表机构
Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出了一种定义 AI 原生系统的标准，基于自主权而非 AI 模型能力，通过修订权阶梯和验证机制界定 AI 自主重写系统的能力。
AI 中文摘要
AI 开始编写系统代码：代理现在能够合成、验证并部署系统组件。尽管这一转变，"AI 原生"仍只是一个营销术语，没有确切的定义。本文为此提供了一个定义。我们沿着单一轴线定义 AI 原生性——系统自身决策的自主权，而非底层 AI 模型的能力。基于系统决策层模型，我们区分了执行者（谁执行决策）与修订权（谁可以更改它），将修订权组织成一个阶梯——自调节、自重写、自架构，并将系统定义为 AI 原生，当 AI 自主重写系统自身实现时。该定义还要求一个升级检测器、验证程序和经过验证的回退方案，同时将目的和正确性交给人类所有。
英文摘要
AI has begun to write systems code: agents now synthesize, verify, and deploy system components. Despite this shift, "AI-native" remains a marketing term with no precise technical definition. This paper gives it one. We define AI-nativeness along a single axis---authority over the system's own decisions rather than by the capability of the underlying AI models. Building on a decision-level model of a system, we distinguish occupancy (who executes a decision) from revision authority (who may change it), organize revision authority into a ladder---self-tuning, self-rewriting, self-architecting and define a system as AI-native when an AI autonomously rewrites the system's own implementations. The definition further requires an escalation detector, a verification procedure, and a verified fallback, while leaving purpose and correctness human-owned.
