Multi-LLM Debate: Framework, Principals, and Interventions
Abstract
The flexible and generalized nature of large language models has allowed for their application in a wide array of language-based domains. Much like their human contemporaries, these models are capable of engaging in discussions and debates as a means of improving answer quality. We first take a theoretical approach to analyzing debate and provide a framework through which debate can be mathe-matically examined. Building on this framework, we provide several theoretical results for multi-agent debate. In particular, we demonstrate that similar model capabilities, or similar model responses, can result in static debate dynamics where the debate procedure simply converges to the majority opinion. When this majority opinion is the result of a common misconception (possibly ingrained in the models through shared training data) debate is likely to converge to answers associated with that common misconception. Using insights from our theoretical results, we then propose three interventions that improve the efficacy of debate. For each intervention, we provide theoretical results demonstrating how debate is improved. We also demonstrate that these interventions result in better performance on four common benchmark tasks.
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Empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus.
“However, empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus.”
From Multi-agent discussion gains less when dissent is withheld (2026) · cites this paper as Estornell & Liu, 2024
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Luo, H., Liu, Y., Zhang, R., Wang, J., Sun, G., Niyato, D. T., Yu, H., Xiong, Z., Wang, X., & Shen, X. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3612760 · Cited by 52 · see citing works
Self-citationChallenging the Evaluator: LLM Sycophancy Under User Rebuttal
Kim, S. W., & Khashabi, D. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025 · doi:10.18653/v1/2025.findings-emnlp.1222 · Cited by 6 · see citing works
Chen, Q., Ilami, S., Lore, N., & Heydari, B. (2026) · Journal of Mechanical Design · doi:10.1115/1.4070755 · Cited by 3 · see citing works
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity
Zhu, X., Zhang, C., Chi, Y., Stafford, T., Collier, N., & Vlachos, A. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.1694 · Cited by 2 · see citing works
Duan, Y., zhang, C., & Li, J. (2025) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3768292.3770377 · Cited by 2 · see citing works
“Estornell et al. [7] develop a theoretical framework for multi-agent debate, proposing novel interventions to improve debate efficacy and demonstrating significant performance improvements on some common benchmark tasks.”
Takano, K., Hirano, M., & Nakagawa, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-13562-9_38 · Cited by 2 · see citing works
“To address this, we introduce an explicit latent variable representing the agent’s underlying policy beliefs, following the framework proposed by [9].”
InfluentialSelf-Auditing LLMs for Bias and Hallucination Reduction Through Multi-Agent Frameworks
Anointina, Bamini, A. M. A., & Chitra, R. (2026) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca68095.2026.11485555 · Cited by 1 · see citing works
“This idea evolved into multi-agent debate frameworks, where multiple LLMs argue from different perspectives before arriving at a consensus answer [8], [9].”
Zhang, P. P., An, G., Huang, J., et al. (2026) · ACM Web Conference (WWW) · doi:10.1145/3774904.3792152 · Cited by 1 · see citing works
“Furthermore, Estornell and Liu [12] analyze the dynamics of multi-agent debate, identifying issues such as ‘tyranny of the majority’ and ‘shared misconceptions’, and propose debate pruning to improve stability and interpretability.”
Peng, X., Lin, B., Wang, J., Li, X., Ma, J., Yu, J., Mao, X., & Wang, S. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3808197 · Cited by 1 · see citing works
Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate
Wynn, A., Satija, H., & Hadfield, G. (2025) · arXiv · doi:10.48550/arxiv.2509.05396 · Cited by 1 · see citing works
“Estornell & Liu (2024) examine the theoretical properties and effects of opinion diversity within the debate framework, reporting a“tyranny of the majority” effect.”
Gómez Álvarez, S., Mozo Quesada, A., Navarro, T., Gálvez Rojas, S., & López Valverde, F. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01086-z
Multi-Planners in Plan-and-Solve Prompting for Medical Reasoning
Wang, Y., Li, A., Blum, J. J., & Nguyen, H. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402280
“Prior research has demonstrated that multi-agent collaboration can enhance decision-making consistency and reduce hallucinations by introducing self-correction and debate mechanisms [9].”
MALLM: Multi-Agent Decision-Making with LLMs for Multi-User Edge-Sensor Environments
Fu, H., Pan, W., Liu, Z., & Lin, S. (2025) · IEEE Annual Congress on Artificial Intelligence of Things (AIoT) · doi:10.1109/aiot66900.2025.00075
“A typical LLM-based multi-agent debate system consists of LLM agents that advocate for their ideas [15].”
BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection
You, S. T., Tran, N. A. K., Marizane, W. K., Rao, H., Zhang, Q., & Huang, X. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00112 · Cited by 2 · see citing works
“Multi-agent debate, where multiple models adopt opposing stances and critique each other’s reasoning, has been increasingly used to reduce hallucinations [43], [44].”
FinLumen: A Game-Theoretic Multi-Agent Framework for Rational Portfolio Management
Zhang, Z., Sen, P., Gu, F., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462217
Kim, S., Lee, G., Shim, K., & Shim, B. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463505
Digital Representatives in Collective Decision-Making
Chen, K., Huang, J., & Luo, Y. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3709177
“This approach has shown promise across various domains (e.g., [8], [9], [17], [18], [19], [20]).”
Du, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30849-8_24
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Brains that Bicker: Coopetition as a Catalyst for Smarter LLMs
Miin, A., Huang, R., Liu, W. Y., & Ding, L. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-24810-7_44
Evaluating automated refereeing with heterogeneous LLM panels
Douven, I. (2026) · Scientometrics · doi:10.1007/s11192-026-05783-y
“A consistent finding in this body of work is that homogeneous agents (i.e., multiple instances of the same model) tend to converge to the majority opinion, which can be systematically wrong when it reflects shared training biases (Estornell & Liu, 2024).”
Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems
Rong, S. Q., Dong, J., Thai, N. D., Wen, Y., Tan, C., & Ong, Y.-S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37673-2_12 · Cited by 1 · see citing works
“ContestTrade [5] decomposes into a Data Team that compresses raw data into text factors and a Research Team that proposes candidate decisions.”
Anderson, C. (2026) · medRxiv · doi:10.64898/2026.09.01.26361960
Park, J., Lim, Y., Kim, G., Yun, T., & Min, M. (2026) · ETRI Journal · doi:10.4218/etrij.2026-0186
Tuo, B., Wang, B. T., Miller, T., & Demartini, G. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3848504
“More broadly, multi-agent collaboration has been shown to improve performance on complex reasoning tasks [20, 65, 67].”
Jiang, P., & Han, J. (2026) · Research in Engineering Design · doi:10.1007/s00163-026-00506-z
“Without adversarial evaluation mechanisms, models lack the critical perspective necessary to identify feasibility conflicts or constraint violations, particularly when balancing competing objectives such as sustainability and manufacturability (Curtis et al. 2024 ; Estornell and Liu 2024 ).”
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
Motger, Q., Oriol, M., Marco, J., & Franch, X. (2026) · arXiv · doi:10.48550/arxiv.2607.26212
Multi-agent discussion gains less when dissent is withheld
Mansuri, C. S., Wang, X., Li, M., Acton, B., Eckardt, R., Patel, D., & Kojaku, S. (2026) · arXiv · doi:10.48550/arxiv.2609.38324
“Detailed experimental settings for every benchmark and figure are in Appendix B.1. fail to correct initial errors (Choi et al., 2025; Estornell & Liu, 2024; Huang et al., 2024; Wang et al., 2024a; Smit et al., 2024; Zhang et al., 2025).”
Emergence of Biased Consensus in Multi-Agent LLM Debates
Okawa, M. (2026) · arXiv · doi:10.48550/arxiv.2608.02827
“…explored a range of design choices, including heterogeneous mixture of roles and LLMs (e.g., MAD (Liang et al., 2023) and its theoretical analysis (Estornell & Liu, 2024)), communication strategies (e.g., dynamic agent recruitment in AgentVerse (Chen et al., 2023) and debate-mode switching in…”
- Authors
- Estornell, A., & Liu, Y.
- ORCID
- Liu, Yang
- Venue
- Advances in Neural Information Processing Systems 37
- Published
- 2024
- Pages
- 28938-28964
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W4415798198
- Semantic Scholar
- 6099dbbe46b63a3cfb9fcdcf98170b7abc25a161
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 17 · 2025: 5
- FWCI
- 9.41
- Type
- conference paper
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, AI alignment, AI safety
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