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    <abstract>Large Language Models (LLMs) frequently face limitations, such as judgment biases and cognitive deadlocks, in complex reasoning due to “Degradation of Thought” in self-reflection. While Multi-Agent Debate (MAD) approaches attempt to address this issue, their inherently adversarial nature suppresses diverse perspectives by overemphasizing antagonism, which results in the loss of partially valid reasoning. In this paper, we propose Multi-Agent Diverse Thinking (MADT), a novel cognitive framework that redefines multi-agent collaboration by shifting from adversarial confrontation toward constructive synergy. To break the oppositional deadlock found in traditional debates, MADT decomposes the thinking process into fine-grained modules: an Affirmative Thinker to preserve rational components, a Critical Thinker to rectify errors, and a Growth-Minded Thinker to provide optimization suggestions. These agents jointly foster a synergistic environment where specialized roles protect valid logic while iteratively refining flaws. Meanwhile, a Leader agent coordinates the collaborations and synthesizes the feedback from multiple thinkers to produce the final result. Extensive experiments on Common Machine Translation and Counter-Intuitive Arithmetic Reasoning tasks show that MADT consistently outperforms Self-Reflection and MAD baselines, validating the superior ability to enhance complex logical and reasoning performance in LLMs.</abstract>
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%0 Conference Proceedings
%T Diverse Thinking: Breaking Oppositions in Debates to Foster Synergized Reasoning via Multi-Agent Collaboration
%A Huang, Zhaokang
%A Xu, Yiming
%A Wang, Tao
%A Yang, Yu
%A Hao, Tianyong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F huang-etal-2026-diverse
%X Large Language Models (LLMs) frequently face limitations, such as judgment biases and cognitive deadlocks, in complex reasoning due to “Degradation of Thought” in self-reflection. While Multi-Agent Debate (MAD) approaches attempt to address this issue, their inherently adversarial nature suppresses diverse perspectives by overemphasizing antagonism, which results in the loss of partially valid reasoning. In this paper, we propose Multi-Agent Diverse Thinking (MADT), a novel cognitive framework that redefines multi-agent collaboration by shifting from adversarial confrontation toward constructive synergy. To break the oppositional deadlock found in traditional debates, MADT decomposes the thinking process into fine-grained modules: an Affirmative Thinker to preserve rational components, a Critical Thinker to rectify errors, and a Growth-Minded Thinker to provide optimization suggestions. These agents jointly foster a synergistic environment where specialized roles protect valid logic while iteratively refining flaws. Meanwhile, a Leader agent coordinates the collaborations and synthesizes the feedback from multiple thinkers to produce the final result. Extensive experiments on Common Machine Translation and Counter-Intuitive Arithmetic Reasoning tasks show that MADT consistently outperforms Self-Reflection and MAD baselines, validating the superior ability to enhance complex logical and reasoning performance in LLMs.
%U https://aclanthology.org/2026.findings-emnlp.390/
%P 8220-8236

Markdown (Informal)

[Diverse Thinking: Breaking Oppositions in Debates to Foster Synergized Reasoning via Multi-Agent Collaboration](https://aclanthology.org/2026.findings-emnlp.390/) (Huang et al., Findings 2026)

• Diverse Thinking: Breaking Oppositions in Debates to Foster Synergized Reasoning via Multi-Agent Collaboration (Huang et al., Findings 2026)

ACL

• Zhaokang Huang, Yiming Xu, Tao Wang, Yu Yang, and Tianyong Hao. 2026. Diverse Thinking: Breaking Oppositions in Debates to Foster Synergized Reasoning via Multi-Agent Collaboration. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 8220–8236, Budapest, Hungary. Association for Computational Linguistics.