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arXiv Machine Learning By Kaixuan Liu, Guojun Xiong

Learning Multi-Agent Orchestration:Higher-Order Interactions in Policy Improvement

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The paper introduces IAPO, an interaction‑aware policy optimization framework for multi‑agent orchestration in large language model systems. IAPO decomposes the performance difference between policies into individual decision effects and their interactions, proving that interactions can account for up to half of the attainable gain. Experiments on long‑input tasks such as GSM8K, MATH, DROP, MBPP, and HumanEval show that IAPO’s plans achieve the highest accuracy among 15 published systems while using fewer tokens, and that omitting the interaction term reduces accuracy by 7–17 points on code tasks.

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