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.
@article{arxiv.2509.05764,
title = {DRF: LLM-AGENT Dynamic Reputation Filtering Framework},
author = {Yuwei Lou and Hao Hu and Shaocong Ma and Zongfei Zhang and Liang Wang and Jidong Ge and Xianping Tao},
journal= {arXiv preprint arXiv:2509.05764},
year = {2025}
}
Comments
This paper has been accepted by ICONIP 2025 but not published