Introduces a Principal-Agent framework for LLM delegation, where agents choose both model (e.g., Llama 3 vs. GPT-4o) and effort (token budget)?

Introduces a Principal-Agent framework for LLM delegation, where agents choose both model (e.g., Llama 3 vs. GPT-4o) and effort (token budget).

Derives optimal linear contracts that incentivize desired behavior, even when agents hide their effort allocation?

Derives optimal linear contracts that incentivize desired behavior, even when agents hide their effort allocation.

Validated on open-weight LLMs (Llama 3, Mistral 7B) across MATH and MMLU-Pro benchmarks, showing convergence to theoretical equilibria.

Agent Frameworks

arXiv cs.MA August 20, 2026

⚡Academics reveal how simple contracts can game AI agent workflows in high-stakes delegation

Deep Dive

Key Points

Introduces a Principal-Agent framework for LLM delegation, where agents choose both model (e.g., Llama 3 vs. GPT-4o) and effort (token budget).
Derives optimal linear contracts that incentivize desired behavior, even when agents hide their effort allocation.
Validated on open-weight LLMs (Llama 3, Mistral 7B) across MATH and MMLU-Pro benchmarks, showing convergence to theoretical equilibria.

Provides a blueprint for designing fair, efficient contracts in agentic AI workflows, reducing hidden-cost risks in enterprise automation.