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arxiv: 2503.12029 · v1 · pith:HT4E3YGN · submitted 2025-03-15 · cs.SE

Is Multi-Agent Debate (MAD) the Silver Bullet? An Empirical Analysis of MAD in Code Summarization and Translation

Jina Chun , Qihong Chen , Jiawei Li , Iftekhar Ahmed This is my paper

Reviewed by Pithpith:HT4E3YGNopen to challenge →

classification cs.SE
keywords agentsdebatestructureddecision-makinginteractionsmulti-agentproblem-solvingsystems
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Large Language Models (LLMs) have advanced autonomous agents' planning and decision-making, yet they struggle with complex tasks requiring diverse expertise and multi-step reasoning. Multi-Agent Debate (MAD) systems, introduced in NLP research, address this gap by enabling structured debates among LLM-based agents to refine solutions iteratively. MAD promotes divergent thinking through role-specific agents, dynamic interactions, and structured decision-making. Recognizing parallels between Software Engineering (SE) and collaborative human problem-solving, this study investigates MAD's effectiveness on two SE tasks. We adapt MAD systems from NLP, analyze agent interactions to assess consensus-building and iterative refinement, and propose two enhancements targeting observed weaknesses. Our findings show that structured debate and collaboration improve problem-solving and yield strong performance in some cases, highlighting MAD's potential for SE automation while identifying areas for exploration.

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