Leverage Laws: A Per-Task Framework for Human-Agent Collaboration
This work provides a formal, normative framework for evaluating human-agent collaboration efficiency, operationalizing qualitative concepts from supervisory control and mixed-initiative interaction into a testable ratio, but remains theoretical without empirical validation.
The paper introduces a per-task leverage ratio for human-agent collaboration, quantifying human work displaced relative to time spent specifying, monitoring, and reviewing the task. It shows that leverage is bounded by irreducible task novelty and decomposes into two scaling axes (capability and memory), with a windowed measure that can exceed per-task limits through accumulated planning investment.
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.