📌 𝐖𝐡𝐞𝐧 𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐒𝐤𝐞𝐰𝐬 𝐃𝐞𝐛𝐚𝐭𝐞
Here's a slightly delayed post on our #ACL2026 𝐎𝐫𝐚𝐥 𝐏𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 paper!
"𝐖𝐡𝐞𝐧 𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐒𝐤𝐞𝐰𝐬 𝐃𝐞𝐛𝐚𝐭𝐞: 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐁𝐢𝐚𝐬-𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠"
(w/ Professor Jerry Zhu and Professor @SharonYixuanLi)
📄 Paper: https://arxiv.org/pdf/2510.07517
🖥️ GitHub: https://github.com/deeplearning-wisc/MAD-identity-bias
🤗 Hugging Face Paper: https://huggingface.co/papers/2510.07517
⚠️ 𝐃𝐢𝐝 𝐘𝐨𝐮 𝐊𝐧𝐨𝐰?
Multi-agent debate is often viewed as a way to make LLMs reason better by letting multiple agents exchange opinions and correct each other. But what if agents are not only judging the content of an argument, but also reacting to 𝐰𝐡𝐨 said it?
In this work, we show that LLM agents in multi-agent debate can suffer from 𝐢𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐛𝐢𝐚𝐬: they may become overly sycophantic toward peers, or overly attached to their own previous answers. These biases can distort debate dynamics, create premature consensus, and undermine the reliability of multi-agent reasoning!
========
🔎 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬
1️⃣ We introduce a principled framework for understanding 𝐢𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐛𝐢𝐚𝐬 in multi-agent debate, unifying two important behaviors: sycophancy toward peers and self-bias toward one’s own prior answer.
2️⃣ We propose 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: a simple intervention that removes identity markers from debate transcripts, forcing agents to evaluate arguments based on content rather than attribution.
3️⃣ We introduce the 𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐁𝐢𝐚𝐬 𝐂𝐨𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭, a metric for quantifying whether an agent is biased toward following peers or sticking with itself.
========
💡 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬
🔺 Multi-agent debate is becoming an important paradigm for improving LLM reasoning, but debate only helps if agents respond to arguments, not identities.
🔺 Our results show that LLM agents can be surprisingly sensitive to whether a response is labeled as coming from "self" or "peer", even when the underlying content is what should matter.
🔺 Response anonymization is lightweight and practical: it requires no retraining, no architectural changes, and no additional verifier. Just remove identity cues and let agents reason over the content.
========
#ACL2026 #OralPresentation #AI #ArtificialIntelligence #MachineLearning #DeepLearning #LLM #MultiAgent #MultiAgentSystems #NaturalLanguageProcessing #ReliableAI #TrustworthyAI #AIAgents #Debate #Sycophancy #Bias
