Duo-Feng Xu

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 first. As a result,...