New framework models LLM delegation contracts and technology choice

Researchers have developed a new framework to model the Principal-Agent problem when agents select from various technologies, each with different cost-capability profiles. This model is particularly relevant for Large Language Models (LLMs), where agents must choose both a model and an effort level, such as token budget. The study derives an optimal linear contract for the principal, showing that a threshold reward share incentivizes the agent to switch technologies. The model was calibrated using open-weight LLMs on the MATH and MMLUPro benchmarks, indicating that simple linear contracts can effectively manage complex delegation in agentic workflows. AI

IMPACT Provides a theoretical framework for optimizing LLM delegation and agentic workflows, potentially improving efficiency and cost-effectiveness.

RANK_REASON Academic paper detailing a new theoretical framework for LLM delegation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

• arXiv
• Hugging Face
• Large Language Models
• LLMs
• MMLUPro
• Nanda Kishore Sreenivas

• paper
• model release

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