用动态信誉过滤提升大模型多智能体协作效率
DRF: LLM-AGENT Dynamic Reputation Filtering Framework
- 构建交互式评分网络量化智能体表现
- 设计信誉评分机制评估诚实性与能力
- 适合需高可信协作的复杂任务系统
随着生成式AI的发展，基于大语言模型（LLMs）的多智能体系统已成为处理复杂任务的强大工具。然而，这类系统在量化智能体性能和评估可信度方面仍存在挑战。为此，本文提出DRF——一种动态信誉过滤框架。DRF通过构建交互式评分网络来量化智能体表现，设计信誉评分机制以衡量智能体的诚实性与能力，并引入基于上限置信区间（Upper Confidence Bound）的策略提升智能体选择效率。实验表明，DRF显著提升了逻辑推理与代码生成任务中的任务完成质量与协作效率，为多智能体系统应对大规模任务提供了新思路。
原文摘要 · Abstract (English)
With the evolution of generative AI, multi - agent systems leveraging large - language models(LLMs) have emerged as a powerful tool for complex tasks. However, these systems face challenges in quantifying agent performance and lack mechanisms to assess agent credibility. To address these issues, we introduce DRF, a dynamic reputation filtering framework. DRF constructs an interactive rating network to quantify agent performance, designs a reputation scoring mechanism to measure agent honesty and capability, and integrates an Upper Confidence Bound - based strategy to enhance agent selection efficiency. Experiments show that DRF significantly improves task completion quality and collaboration efficiency in logical reasoning and code - generation tasks, offering a new approach for multi - agent systems to handle large - scale tasks.
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