提出任务级协作效率度量，量化人机协作中人的投入与产出比。
Leverage Laws: A Per-Task Framework for Human-Agent Collaboration
- 定义任务级杠杆率：代理人替代的人工工作量除以人指定任务所需时间
- 揭示人机信息流动有方向性上限，杠杆率受任务新颖性与规划投入限制
- 适用于重复任务与子任务场景，可指导系统设计与人机分工优化
我们提出一种任务级杠杆率，用于衡量人机协作效率：即代理人取代的人工工作量，除以人类完成任务指定、处理运行中断和审查结果所需的时间。该分母分解为三个信息流通道，各自具有时间成本系数。研究表明，信息密度具有方向性，且在人向代理与代理向人流动上分别存在上限；杠杆率的渐近行为可分解为能力与记忆两个缩放轴，规划项存在由人类处理能力决定的不可压缩下限。我们将此任务级分析扩展至窗口化杠杆率，适用于重复任务、衍生子任务及系统投资的分摊。任务级杠杆率受限于任务新颖性，窗口级杠杆率则受限于窗口内可兑现的累积规划投资。该框架将监督控制、共同基础与混合主动性等早期定性研究统一为单一规范性比率，并提出一系列可检验的实证问题作为开放课题。
原文摘要 · Abstract (English)
We propose a per-task leverage ratio for human-agent collaboration: human work displaced by an agent, divided by the human time required to specify the task, resolve mid-run interrupts, and review the result. The denominator decomposes into three channels through which a conserved per-task information requirement must flow, each with its own time-cost scalar. We show that information density itself is directional and bounded by separate ceilings on human-to-agent and agent-to-human flow, and that the asymptotic behavior of leverage decomposes into two scaling axes (capability and memory) with a non-zero floor on the planning term set by irreducible task novelty bounded by human throughput. We extend this per-task analysis to a windowed leverage measure that accommodates recurring tasks, spawned subtasks, and amortized system-design investment. The per-task ceiling does not bind the windowed measure, though both remain bounded: $L_{\text{task}}$ by per-task novelty, $L_{\text{window}}$ by the stock of accumulated planning investment that pays out within the window. The framework operationalizes aspects of earlier qualitative work on supervisory control (Sheridan, 1992), common ground (Clark & Brennan, 1991), and mixed-initiative interaction (Horvitz, 1999) within a single normative ratio, and produces a list of testable empirical questions that we leave as open problems.
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