Contracting for LLM Delegation: Moral Hazard in Technology and Effort Choice

2026-08-18

AI Summary

研究通过扩展委托代理框架，解决大语言模型中技术选择和努力分配的道德风险问题，提出最优线性契约方法以激励代理。

Abstract

We extend the standard Principal-Agent framework to scenarios where the Agent selects from a suite of technologies, each characterized by a distinct cost-capability profile. This framework is increasingly critical in the era of Large Language Models (LLMs), where Agents choose both a model and an associated effort level (e.g., token budget). We model the relationship between output quality and effort as a concave, saturating function, which depends on the Agent's hidden two-dimensional action choice balancing technology selection and effort allocation. We derive the optimal linear contract for the Principal, demonstrating that the Agent's best response is characterized by a threshold reward share that triggers technology switching. Finally, we calibrate our model using open-weight LLM pairings across the MATH and MMLUPro benchmarks. We show that both Principal and Agent, when employing bandit algorithms to navigate this environment, converge to strategies that closely align with our theoretical equilibrium. These results suggest that simple linear contracts can effectively incentivize complex, technology-aware delegation in agentic workflows.

Problem

• Large Language Models
• Moral Hazard
• Technology Choice
• Effort Allocation
• Contract Design

Innovation

• Principal-Agent Framework
• Large Language Models (LLMs)
• Concave Saturating Function
• Technology Selection and Effort Allocation
• Linear Contracts