通过加权共识提升多智能体系统推理能力
Roundtable Policy: Confidence-Weighted-Consensus Aggregation Improves Multi-Agent-System Reasoning
- 基于民主决策与心智社会理论设计加权共识框架
- 在复杂科学任务中显著提升多智能体推理表现
- 无需内部访问，适配各类黑箱多智能体系统
多智能体系统在下游任务中展现出超越单一智能体基线的优异性能。现有研究探索了投票、辩论及复杂交互协议等协作方式，但尚不清楚为何特定策略更优。受民主委员会和《心智社会》理论启发，本文提出Roundtable Policy，一种基于多个大模型加权共识的推理框架。该框架在推理阶段实现结构化、可解释的协同决策，仅需黑箱访问与统一流程，适用于多种多智能体系统。大量实验表明，其在复杂异构科学任务中显著增强推理能力，突破传统隐式收敛模式，实现透明高效的智能体协作。
原文摘要 · Abstract (English)
Multi-agent systems have demonstrated exceptional performance in downstream tasks beyond diverse single agent baselines. A growing body of work has explored ways to improve their reasoning and collaboration, from vote, debate, to complex interaction protocols. However, it still remains opaque why specific choice would be preferred in multi-agent systems. Inspired by the decision-making mechanism of democratic committees and The Society of Mind, we introduce Roundtable Policy, an inference-time reasoning framework for multi-agent systems that performs inference through the weighted consensus of multiple LLMs. Through extensive experiments, we demonstrate its that this approach significantly enhances reasoning in complex heterogeneous scientific tasks. Roundtable Policy emphasizes structured and interpretable inference rather than opaque convergence, while requires only black-box access and uniform procedures, making it broadly applicable to diverse multi-agent systems.
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