ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
Open accessAbstract
Large Language Models (LLMs) still struggle with natural language reasoning tasks.Motivated by the society of minds (Minsky, 1988), we propose RECONCILE, a multi-model multiagent framework designed as a round table conference among diverse LLM agents.RECON-CILE enhances collaborative reasoning between LLM agents via multiple rounds of discussion, learning to convince other agents to improve their answers, and employing a confidenceweighted voting mechanism that leads to a better consensus.In each round, RECONCILE initiates discussion between agents via a 'discussion prompt' that consists of (a) grouped answers and explanations generated by each agent in the previous round, (b) their confidence scores, and (c) demonstrations of answerrectifying human explanations, used for convincing other agents.Experiments on seven benchmarks demonstrate that RECONCILE significantly improves LLMs' reasoning -both individually and as a team -surpassing prior single-agent and multi-agent baselines by up to 11.4% and even outperforming GPT-4 on three datasets.RECONCILE also flexibly incorporates different combinations of agents, including API-based, open-source, and domainspecific models, leading to an 8% improvement on MATH.Finally, we analyze the individual components of RECONCILE, demonstrating that the diversity originating from different models is critical to its superior performance.1 Self-Refine MAD+Judge Multi-Agent Debate (MAD) ReConcile (Group-Discuss-and-Convince) Yes, with 95% confidence No, with 50% confidence No, with 40% confidence yes no no yes no no yes no no Question (Q): Is an ammonia fighting cleaner good for pet owners?Human Explanation (Exp): Ammonia is a component in pet urine.It has an unpleasant odor.
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Structured contrast and deliberation among agents can enhance argumentative coherence and mitigate bias.
“Other studies have demonstrated that structured contrast and deliberation among agents can enhance argumentative coherence and mitigate bias (Chen et al., 2024;Liu et al., 2026).”
From Multi-Agent debate system based on large language models: structured deliberation and validation in satellite communications (2026) · cites this paper as Chen et al., 2024
The Introduction describes structured discussion among diverse LLM agents, and §6.2 reports improved reasoning accuracy and consensus. The paper also identifies model bias and illustrates an echo chamber among same-model agents in Appendix C.5. It does not establish improved argumentative coherence or directly measure bias mitigation, so those parts of the claim go beyond its findings. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSome studies show that discussion, often run as multi-agent debate, outperforms standalone reasoning or voting, whereas others report no effect.
“Some studies show that discussion, often run as multi-agent debate, outperforms standalone reasoning or voting (Du et al., 2024; Liang et al., 2024; Chen et al., 2024), whereas others report no effect (Smit et al., 2024; Wang et al., 2024b; Kaesberg et al., 2025).”
From Multi-agent discussion gains less when dissent is withheld (2026) · cites this paper as Chen et al., 2024
Other studies report that discussion can outperform individual reasoning or voting.
“In contrast, other studies report that discussion can outperform individual reasoning or voting (Du et al., 2024; Liang et al., 2024; Chen et al., 2024).”
From Multi-agent discussion gains less when dissent is withheld (2026) · cites this paper as Chen et al., 2024
The consensus-building process generally takes 2 to 3 rounds to be effective.
“The consensus-building process generally takes 2 to 3 rounds (Chen et al., 2024b;Du et al., 2024;Yang et al., 2025;Xiong et al., 2023) to be effective.”
From Free-MAD: Consensus-Free Multi-Agent Debate (2026) · cites this paper as Chen et al., 2024b
The framework explains conflicting findings in prior work as arising from tasks operating in different regimes.
“This framework explains conflicting findings in prior work (Du et al., 2024; Liang et al., 2024; Chen et al., 2024; Smit et al., 2024; Wang et al., 2024b; Kaesberg et al., 2025) as arising from tasks operating in different regimes.”
From Multi-agent discussion gains less when dissent is withheld (2026) · cites this paper as Chen et al., 2024
The Introduction (page unavailable) discusses diversity and consensus among LLM agents, and the Results (page unavailable) compare their performance across reasoning tasks. Our copy does not explain conflicting findings in prior work as arising from tasks operating in different regimes; searches for that explanation and for task regimes found no such passage. (LLM, conf=0.70)
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Research works printed but not linked: 12
- BIG-bench collaboration. 2023. Beyond the imitation game: Quantifying and extrapolating the capabili- ties of language models. Transactions on Machine Learning Research.
- Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, et al. 2022. Glam: Efficient scaling of language models with mixture-of-experts. In International Conference on Machine Learning, pages 5547-5569. PMLR.
- Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot. 2023. Specializing smaller language models towards multi-step reasoning. In ICML.
- Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Wein- berger. 2017. On calibration of modern neural net- works. In International conference on machine learn- ing, pages 1321-1330. PMLR.
- Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the math dataset. NeurIPS.
- Shuang Li, Yilun Du, Joshua B Tenenbaum, Antonio Torralba, and Igor Mordatch. 2022a. Composing en- sembles of pre-trained models via iterative consensus. International Conference on Learning Representa- tions (ICLR).
- Yanhong Li, Gang Kou, Guangxu Li, and Yi Peng. 2022b. Consensus reaching process in large-scale group decision making based on bounded confidence and social network. European Journal of Opera- tional Research, 303(2):790-802. · DOI
- John Platt et al. 1999. Probabilistic outputs for support vector machines and comparisons to regularized like- lihood methods. Advances in large margin classifiers, 10(3):61-74.
- Swarnadeep Saha, Peter Hase, and Mohit Bansal. 2023. Can language models teach weaker agents? teacher explanations improve students via theory of mind. In NeurIPS.
- Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large neural net- works: The sparsely-gated mixture-of-experts layer. In International Conference on Learning Representa- tions.
- Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023b. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations.
- Andy Zeng, Maria Attarian, brian ichter, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S Ryoo, Vikas Sindhwani, Johnny Lee, Vin- cent Vanhoucke, and Pete Florence. 2023. Socratic models: Composing zero-shot multimodal reasoning with language. In The Eleventh International Conference on Learning Representations.
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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-Agent Design: Optimizing Agents with Better Prompts and Topologies
Zhou, H., Wan, X., Sun, R., Palangi, H., Iqbal, S., Vulić, I., Korhonen, A., & Arık, S. Ö. (2025) · arXiv · doi:10.48550/arxiv.2502.02533
MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision
Ke, Z., Xu, A., Ming, Y., Nguyen, X.-P., Chin, R., Xiong, C., & Joty, S. (2025) · arXiv · doi:10.48550/arxiv.2505.14996
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
Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity
Zhang, H., Cui, Z., Chen, J., Wang, X., Zhang, Q., Wang, Z., Wu, D., & Hu, S. (2025) · arXiv · doi:10.48550/arxiv.2502.08788
Self-citationMulti-Agent Debate Strategies: Survey, Taxonomy, and Challenges
Motger, Q., Oriol, M., Marco, J., & Franch, X. (2026) · arXiv · doi:10.48550/arxiv.2607.26212
Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
Hao, X., Wu, Z., Qiu, Y.-X., Xiao, C., Xu, R., Zheng, S., & Qin, J. (2026) · arXiv · doi:10.48550/arxiv.2606.00820
Achieving Unanimous Consensus Through Multi-Agent Deliberation
Pokharel, A., Dantu, R., Zaman, S., Quach, V., & Talapuru, S. (2025) · Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS) · doi:10.1109/brains67003.2025.11302940
RiOT: Efficient Prompt Refinement with Residual Optimization Tree
Zhou, C., Shi, Z., Yao, Y., Liang, L., Chen, H., & Zhang, Q. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1086
Chen, J., Yang, H., Liu, Z., & Joe-Wong, C. (2025) · doi:10.36227/techrxiv.175760290.08279849/v1
Self-citationWei, Z., Ye, X., Tan, H., Zhao, J., Wang, M., & Wang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2101-2_38
DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue
Li, X., Pan, D., Xiao, H., Han, J., Tang, J., Ma, J., Wang, W., & Cheng, B. H. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209338
Shi, Z., Zhu, Y., Xie, Y., Shi, J.-H., Xie, G., Zhang, H., Jiang, Y., Miao, C., & Li, Q. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1638
Knowledge-Aware Co-Reasoning for Multidisciplinary Collaboration
Li, X., Wanghaijiao, Song, K., Zhu, R., & Tang, H. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.687
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Wang, S., Chen, Z., Wang, P., Wei, Z., Tan, Z., Meng, Y., Shen, C., & Li, J. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.587
Vithanage, D., & Suvetha, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4409-7_1
Liu, X., Yun, J., Li, B. Y., Zhang, Y., & Shi, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3001-4_11
MMD-TKGR: Multi-agent Multi-round Debate for Temporal Knowledge Graph Reasoning
Wu, Y., Li, X., & Hu, P. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3343-5_15
SDIDM: An agricultural irrigation decision model based on Chain-of-Thought Prompts enhancement
Dong, W., Liu, Y., Zhang, Q., Jia, L., Kong, B., & Liu, S. (2025) · Data Intelligence · doi:10.3724/2096-7004.di.2025.0142
STLLM-Rec: enhancing explainable recommendation via self-training LLMs
Li, Z., Tan, Z., Wu, S., Li, W., & Mo, T. (2026) · World Wide Web · doi:10.1007/s11280-025-01397-1
From Fragmentation to Systematic Design: Architecting LLM-Based Multi-Agent SystemsPreprint
Zhou, C., Tang, Y., Chen, K., Bai, X., Qi, S., Shen, L., & Zhang, M. (2026) · doi:10.36227/techrxiv.176827304.41872996/v1
Multi-Agent Large Language Models for Complex Text Classification
Jiang, H. (2025) · International Conference on Artificial Intelligence and Automation Control (AIAC) · doi:10.1109/aiac68175.2025.11332429
An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring
Ebrahimi, S., Dehghankar, M., & Asudeh, A. (2025) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2025.ijcnlp-long.90
Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective
Anvekar, T., Rajput, K. S., Baral, C. R., & Gupta, V. (2025) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2025.ijcnlp-long.192
Lee, Y., Zhang, J., Song, T., & Tan, Y. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i48.42131
今井, 翔. (n.d.) · Institutional Repositories DataBase (IRDB) · doi:10.15083/0002012462
Who is the most suitable one? Compliance review method based on multi-agent routing
Yu, C., Jiangqian, H., Chen, X., Gao, M., Zhang, L., Yuan, X., Sun, J., Bao, D., & Yang, Y. (2026) · AI Open · doi:10.1016/j.aiopen.2026.03.001
Kim, J., Suzara, M., & Mitchell, J. C. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790456
CARE-Agent: Multi-Agent Collaboration with Conflict-Aware Routing Mechanism for Diagnosis Prediction
Zhan, P., Cai, B., Zhang, J., Lv, H., Tan, Y., Lin, Z., Chen, X., & Yang, C. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464147
MARE: Multi-Agent Role Embedding for Role-Consistent Generation in Multi-Agent Systems
Gu, W., Han, J., Zhao, J., Chen, Y., Wang, H., & Cheng, B. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464394
- Authors
- Chen, J., Saha, S., & Bansal, M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 7066-7085
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402671543
- Cited by
- 64
- References
- 0
- Citations by year
- 2026: 37 · 2025: 20 · 2024: 6 · 2023: 1
- FWCI
- 18.59
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1846185, 2112635, CAREER)
- Defense Advanced Research Projects Agency (N66001-19-2-4031, N66001-19)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- multi-agent LLM systems, collaborative reasoning, Society of Mind, discussion prompts, model diversity, natural language reasoning, multi-agent debate, MATH benchmark
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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