让大模型通过多智能体辩论激发多样思维，提升推理可解释性。
Unleashing Diverse Thinking Modes in LLMs through Multi-Agent Collaboration
- 四个专精智能体模拟结构化辩论，协同探索不同推理路径。
- 在六项基准测试中超越单模型与传统辩论基线，数学题提升最显著。
- 生成带语义标签和网址的可审计推理链，适合需透明解释的场景。
大型语言模型虽表现强劲，但推理过程常缺乏可解释性。本文提出多智能体协作框架DiMo，通过四个具特定推理范式的智能体进行迭代辩论，协同探索多样化认知策略。该框架生成更稳健的结论与可追溯的推理链条。在六个基准测试中，采用统一开源设置，DiMo显著优于常用单模型与辩论基线，数学任务提升最大。DiMo被定位为语义感知、面向网络的多智能体框架：其智能体能生成带语义类型与URL标注的证据链，支持可解释性说明与用户友好交互。尽管实验基于标准推理基准，该框架可扩展至网络语料库与知识图谱，结合检索增强推理与结构化论证，供下游系统检查与复用。
原文摘要 · Abstract (English)
Large Language Models (LLMs) demonstrate strong performance but often lack interpretable reasoning. This paper introduces the Multi-Agent Collaboration Framework for Diverse Thinking Modes (DiMo), which enhances both performance and interpretability by simulating a structured debate among four specialized LLM agents. Each agent embodies a distinct reasoning paradigm, allowing the framework to collaboratively explore diverse cognitive approaches. Through iterative debate, agents challenge and refine initial responses, yielding more robust conclusions and an explicit, auditable reasoning chain. Across six benchmarks and under a unified open-source setup, DiMo improves accuracy over widely used single-model and debate baselines, with the largest gains on math. We position DiMo as a semantics-aware, Web-native multi-agent framework: it models human-machine intelligence with LLM agents that produce semantically typed, URL-annotated evidence chains for explanations and user-friendly interactions. Although our experiments use standard reasoning benchmarks, the framework is designed to be instantiated over Web corpora and knowledge graphs, combining retrieval-augmented reasoning with structured justifications that downstream systems can inspect and reuse.
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