[2510.07517] When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning

Computer Science > Artificial Intelligence arXiv:2510.07517 (cs) [Submitted on 8 Oct 2025 (v1), last revised 9 Apr 2026 (this version, v5)] Title:When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning Authors:Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li View a PDF of the paper titled When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning, by Hyeong Kyu Choi and 2 other authors View PDF HTML (experimental) Abstract:Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their opinions. Yet recent studies reveal that agents are not neutral: they are prone to identity-driven sycophancy and self-bias, uncritically adopting a peer's view or stubbornly adhering to their own prior output, undermining the reliability of debate. In this work, we present the first principled framework that joins sycophancy and self-bias to mitigate and quantify identity bias in MAD. First, we formalize the debate dynamics as an identity-weighted Bayesian update process. Second, we propose response anonymization: by removing identity markers from prompts, agents cannot distinguish "self" from "peer", which forces equal weights on agent identity, thereby reducing bias and improving trustworthiness. Third, we define the Identity Bias Coefficient (IBC), a principled bias metric that measures an agent's tendency to follow its peer versus itself. Empirical studies across mult...