Research
Principal-Agent Model of LLM Delegation Predicts a Reward-Share Threshold That Triggers Model Switching
The paper extends the standard principal-agent framework to a setting where the agent picks both a model from a suite with distinct cost-capability profiles and an effort level such as a token budget, treating output quality as a concave saturating function of effort. It derives the optimal linear contract and shows the agent's best response is characterized by a threshold reward share at which the agent switches technology. Calibrating against open-weight LLM pairings on MATH and MMLU-Pro, both principal and agent running bandit algorithms converge to strategies close to the predicted equilibrium, suggesting simple linear contracts suffice to incentivize technology-aware delegation in agentic workflows.
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