Flow-by-Flow：高损失领域 AI 输出的内容判断绕过治理范式
原标题：Flow-by-Flow:Content-Judgment Bypass for Governing AI Output in High-Loss Domains
AI 摘要
arXiv 上发布了一篇题为《Flow-by-Flow: Content-Judgment Bypass for Governing AI Output in High-Loss Domains》的论文。论文指出，在高损失领域，人类监督受限于 AI 输出速度与认知负荷的乘积，且能力提升并不能降低分诊和响应成本。为此，作者提出 Flow-by-Flow 治理范式，通过基于形式特征的认知成本评分和机构容量上限来绕过内容判断，并推导出四个设计不变量。蒙特卡洛分析显示，复合多指标流控制优于单纯监督强化，在 90.8% 的试验中表现更好。
正文节选
Computer Science > Artificial Intelligence Title:Flow-by-Flow:Content-Judgment Bypass for Governing AI Output in High-Loss Domains View PDF 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 x L, where L denotes per-item cognitive load. L consists of triage, judgment, and response, which respond asymmetr