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DRF: LLM-AGENT Dynamic Reputation Filtering Framework
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AI
DOI：10.1007/978-981-95-4384-7_10.png)
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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.
Keyword:
LLM-Agent
Team Optimization
Generative AI
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0
论文数:
26
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Benchmarking Large Language Models for Multi-agent Systems: A Comparative Analysis of AutoGen, CrewAI, and TaskWeaverBarbarroxa, R., Gomes, L., & Vale, Z. (2025)。大型语言模型在多智能体系统中的基准测试：AutoGen、CrewAI和TaskWeaver的比较分析。载于P. Mathieu & F. De La Prieta（编），《代理、多智能体系统和数字孪生的实际应用进展：PAAMS合集》（第15157卷，第39-48页）。Springer Nature。[Google Scholar][CrossRef]
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