AI 中文总结
针对大语言模型驱动系统运行可靠性受挑战的问题，提出自校准智能体人工智能框架，设计自校准机制，应用于边缘计算网络零知识工作负载资源分析，提高预测准确率和速度，为自主人工智能部署奠定基础，且生成基本事实速度更快。
AI 中文摘要
大语言模型（LLMs）越来越多地被部署为自主智能体，从静态对话界面转变为能够进行复杂推理、工具执行和决策的动态系统。然而，这些智能体人工智能系统的运行可靠性面临着开放式环境中缺乏可靠的基本事实以及随着时间推移操作漂移增加的风险的根本挑战。为应对这一挑战，我们提出并通过实验评估了一个智能体人工智能框架，旨在在基于LLM的系统中强制实现自主完整性。我们设计了一种自校准机制，通过纳入ARIMA预测器来减轻漂移并动态逼近基本事实，而无需持续的人工监督。为证明我们方法的有效性和可靠性，我们将其应用于边缘计算网络中零知识工作负载资源使用情况分析的复杂领域。实验结果表明，所提出的自校准智能体框架成功地对零知识工作负载进行了分析，在资源使用预测方面比基线LLM智能体的准确率高91.7%，与纯分析相比预测速度提高了71.7%，为在去中心化基础设施中部署自主人工智能奠定了坚实基础。此外，使用所提出的ARIMA跳跃算法生成基本事实的速度比标准ARIMA预测算法快52%，同时达到相同的准确率。
英文摘要
Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, tool execution, and decision-making. However, the operational reliability of these agentic AI systems is fundamentally challenged by the absence of reliable ground truth in open-ended environments and the risk of increasing operational drift over time. To address this challenge, we propose and experimentally evaluate an agentic AI framework, designed to enforce autonomous integrity within LLM-driven systems. We design a self-calibration mechanism that mitigates drift and dynamically approximates ground truth by incorporating an ARIMA forecaster, without requiring continuous human oversight. To demonstrate the effectiveness and reliability of our methodology, we apply it to the complex domain of profiling the resource usage of zero-knowledge workloads in edge computing networks. Experimental results show that the proposed self-calibrating agentic framework successfully profiles the zero-knowledge workloads, achieving a higher accuracy than baseline LLM agents by 91.7% for resource usage prediction and improving the prediction speed by 71.7% compared to pure profiling, establishing a robust foundation for deploying autonomous AI in decentralized infrastructures. Furthermore, the ground truth generation using the proposed ARIMA leaping algorithm is 52% faster than a standard ARIMA forecasting algorithm, while achieving the same accuracy.
CommentsThis work has been submitted to the IEEE Transactions on Network and Service Management for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
