Dynamic Weighted Consensus Framework for LLM Multi-agent Debate

Yi Li

doi:10.1007/978-981-95-3001-4_14

2026-01-01

PRE

DOI：10.1007/978-981-95-3001-4_14

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大型语言模型（LLMs）在复杂推理和事实核查任务中表现出显著能力，但其性能仍受多步推理过程中固有不确定性的限制。尽管多智能体辩论（MAD）框架通过智能体间的协作互动显著提升了推理准确性，但这些智能体往往对同一问题产生分歧答案，可能无意中传播错误响应。在此类情况下，应优先考虑持续提供准确答案的智能体。为解决此挑战，本文提出了一种新颖的LLM多智能体辩论动态加权共识框架（DWC-MAD）。该框架整合了实时置信度量化（源自智能体的迭代响应）和纵向准确度指标（基于智能体的历史表现）。通过采用动态权重分配算法，DWC-MAD对智能体输入分配差异化权重，从而构建优化的多智能体辩论系统。

Keyword:

LLMs

Multi-Agent Debate Framework

Dynamic Weight Allocation

Collaborative Reasoning

Uncertainty Quantification

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KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2025, PT I

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