Flow-by-Flow：高损失领域中绕过内容判断的AI输出治理范式
原标题：Flow-by-Flow:Content-Judgment Bypass for Governing AI Output in High-Loss Domains
AI 摘要
该论文提出了一种名为Flow-by-Flow的AI治理范式，旨在高损失领域中不评估内容本身，而是通过基于形式化可计数特征的认知成本评分和机构容量上限来控制监督负载。作者认为，随着AI能力提升，人类监督的认知负载结构会发生变化，而非减少，传统的内容判断机制面临根本性挑战。论文推导了四个设计不变量，并通过蒙特卡洛分析表明复合多指标流量控制优于单纯监督强化。
正文节选
[Page 1] Flow-by-Flow: Content-Judgment Bypass for Governing AI Output in High-Loss Domains Hiroki Naito UTIE Research Institute (UTIE Instruments Inc.) 2026.4.24 v1.0 Abstract Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains when AI output velocity V exceeds human cognitive capacity C_max. The operative constraint, however, is not V alone but V × L, where L denotes per-item cogni