AI 中文总结
针对高损失领域中AI输出管控的V×L瓶颈问题，提出无需内容判断的Flow-by-Flow治理范式，其复合多指标流量控制在90.8%的试验中优于单纯监督强化。
AI 中文摘要
现有研究表明，在高损失领域中，当AI输出速度V超过人类认知容量C_max时，人类在环监督机制会在结构上变得难以维持。然而，关键约束并非仅为V，而是V与L的乘积，其中L表示单项目认知负荷。L包含分类、判断和响应三个环节，这些环节对AI能力提升的响应具有不对称性。分类成本不会随着模型能力提升而下降，因为语义不确定性是通用型模型设计的固有属性；响应成本对准确率提升不敏感，保持不变；仅判断成本存在下降压力，且这种压力往往通过遗漏而非真正降低来实现。因此，能力提升会重构L而非降低L。基于评估AI输出是否正确的治理机制，要么将评估权委托给AI，从而继承幻觉风险，要么委托给人类，从而面临V×L的上限。我们提出Flow-by-Flow，一种无需评估内容即可控制监督负荷的治理范式：基于形式化可计数特征的认知成本得分对高流量生产施加非线性成本，同时通过机构能力上限将处理量控制在C_max以内。我们推导了任何内容判断旁路超限路径的四个设计不变量：无内容判断、无审查者能力的可扩展消耗、按应用绑定身份的摩擦、无批量放行。我们讨论了一个参考实现以证明这些不变量可同时满足，同时明确承认其实际困难。对1000组参数抽样的蒙特卡洛分析表明，复合多指标流量控制在90.8%的试验中优于单纯的监督强化。
英文摘要
Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains once AI output velocity V exceeds human cognitive capacity C_max. The operative constraint, however, is V x L, where L is per-item cognitive load: triage, judgment, and response. These components respond asymmetrically to capability improvement. Triage cost does not decline, because semantic indeterminacy is inherent in general-purpose design. Response cost is invariant to accuracy. Only judgment cost faces downward pressure, largely by inducing omission. Capability improvement therefore restructures L rather than reducing it. We prove a proposition: if V x L grows at any positive compound rate while supervisory capacity grows linearly, exceedance occurs in finite time; capacity investment buys time only logarithmically, while reducing the growth rate extends it hyperbolically. Supervision enhancement and flow control are therefore not remedies of the same kind. We propose Flow-by-Flow, a governance design that prices supervisory load without evaluating content, intent, or legitimacy. A cognitive cost score built from formal, countable features imposes compounding costs on volume expansion, and an institutional capacity cap fixes processing within C_max. Four design invariants characterize any admissible exceedance pathway: no content judgment, no scalable consumption of examiner capacity, identity-bound per-application friction, and no batch clearance. Excess claim and page fees in patent systems are precursors satisfying only the first two invariants. One reference implementation satisfying all four is presented. A Monte Carlo analysis across 1,000 parameter draws confirms that the analytically derived ordering survives the 30-year horizon in 90.8% of trials.
Comments46 pages,3 figures
