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When Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debate
Multi-agent debate (MAD) is often used to improve large language model (LLM) reasoning, but sequential debate is rarely a neutral aggregator of agents' opinions. We show that sequential MAD suffers from a pronounced first-speaker bias: agents disproportionately shape the final answer when they speak
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