DRF: LLM-AGENT Dynamic Reputation Filtering Framework

Yuwei Lou

doi:10.1007/978-981-95-4384-7_10

DRF: LLM-AGENT Dynamic Reputation Filtering Framework

2026-01-01

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AI

DOI：10.1007/978-981-95-4384-7_10

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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.

Keywords:

LLM-Agent

Team Optimization

Generative AI

DRF: LLM-AGENT Dynamic Reputation Filtering Framework

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