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Enhancing LLM Performance Through Debate: An Empirical Study on Multi-Agent Debate for Coding Tasks
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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 four coding tasks in SE. We adapt a MAD framework from NLP, analyze agent interactions to assess consensus-building and iterative refinement, and propose two MAD variants that enhance agent debate for coding tasks by addressing the observed weaknesses. Our findings show that structured debate and collaboration improve problem-solving and yield strong performance in some cases, highlighting the collaborative debate synergy between LLM agents for coding tasks in SE while identifying areas for future exploration.
Forward citations
Cited by 4 Pith papers
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Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
A systematic review of 141 papers derives a three-axis taxonomy of multi-agent debate design (participants, interaction, agreement) and shows the field has converged on a narrow default pattern.
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Free-MAD: Consensus-Free Multi-Agent Debate
Free-MAD picks the winning answer by scoring the full trajectory of agents' answers across debate rounds, beating majority voting with fewer rounds.
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Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness
A systematic benchmark shows multi-agent debate's value depends on task difficulty, model scale, and agent diversity: limited for math unless problems are hard or models weak, but useful for safety when agents are diverse.
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Byzantine-Robust Decentralized Coordination of LLM Agents
A leaderless, Byzantine-robust LLM agent coordination protocol selects answers by geometric-median aggregation of evaluator scores instead of leader-based quorum voting.
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