这篇论文讲了一个能自己校准的智能体框架，用ARIMA预测把资源分配准确率提了91.7%，速度还快了71.7%。搞边缘计算或零知识证明的可以看看。
该论文提出一种自校准智能体AI框架，通过集成ARIMA预测器动态逼近真实值，减少LLM驱动系统中的运行漂移。在零知识工作负载的边缘资源使用场景中，该框架的资源使用预测准确率比基线LLM智能体高91.7%，预测速度提升71.7%。自校准机制采用的ARIMA跳跃算法比标准ARIMA生成真实值快52%，且精度相同。实验验证了该框架在去中心化基础设施中部署自主AI的可靠性。
A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation
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.