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arXiv · 2507.17753

Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving

Liang Zhang·Xiaoming Zhai·Jionghao Lin·Jennifer Kleiman·Diego Zapata-Rivera·Carol Forsyth·Yang Jiang·Xiangen Hu·Arthur C. Graesser
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Abstract

Large Language Model (LLM) agents are increasingly utilized in AI-aided education to support tutoring and learning. Effective communication strategies among LLM agents improve collaborative problem-solving efficiency and facilitate cost-effective adoption in education. However, little research has systematically evaluated the impact of different communication strategies on agents' problem-solving. Our study examines four communication modes, \textit{teacher-student interaction}, \textit{peer-to-peer collaboration}, \textit{reciprocal peer teaching}, and \textit{critical debate}, in a dual-agent, chat-based mathematical problem-solving environment using the OpenAI GPT-4o model. Evaluated on the MATH dataset, our results show that dual-agent setups outperform single agents, with \textit{peer-to-peer collaboration} achieving the highest accuracy. Dialogue acts like statements, acknowledgment, and hints play a key role in collaborative problem-solving. While multi-agent frameworks enhance computational tasks, effective communication strategies are essential for tackling complex problems in AI education.

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Liang Zhang, Xiaoming Zhai, Jionghao Lin, Jennifer Kleiman, Diego Zapata-Rivera, Carol Forsyth, Yang Jiang, Xiangen Hu, Arthur C. Graesser. 2025-05-02. Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving. https://arxiv.org/abs/2507.17753

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