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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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“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
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“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
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“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).”
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“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
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“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.”
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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 · Cited by 1
Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity
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Self-citationMulti-Agent Debate Strategies: Survey, Taxonomy, and Challenges
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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
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STLLM-Rec: enhancing explainable recommendation via self-training LLMs
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From Fragmentation to Systematic Design: Architecting LLM-Based Multi-Agent SystemsPreprint
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Multi-Agent Large Language Models for Complex Text Classification
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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
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今井, 翔. (n.d.) · Institutional Repositories DataBase (IRDB) · doi:10.15083/0002012462
Metadata
- 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
- 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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