{"ID":2874003,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05764","arxiv_id":"2509.05764","title":"DRF: LLM-AGENT Dynamic Reputation Filtering Framework","abstract":"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.","short_abstract":"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 dyn...","url_abs":"https://arxiv.org/abs/2509.05764","url_pdf":"https://arxiv.org/pdf/2509.05764v1","authors":"[\"Yuwei Lou\",\"Hao Hu\",\"Shaocong Ma\",\"Zongfei Zhang\",\"Liang Wang\",\"Jidong Ge\",\"Xianping Tao\"]","published":"2025-09-06T16:29:42Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}