发表机构
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对智能体科学的资源调度需求，提出分层动态架构及软件，借助秘书智能体在云、边缘和HPC系统中发现资源，经模拟验证其协商准确率达87.71%，选择成本与传统策略相当，可支持Genesis Mission。
AI 中文摘要
智能体科学正在改变计算工作的格局，延伸至科学流水线和工作负载管理器。这些工作负载需要机构内部及跨机构的专用硬件，若调度需评估工作负载对环境的需求，那么资源的自动发现便是关键步骤。本文提出一种分层动态架构及软件，用于在多样化的云、边缘和HPC系统中发现资源，该设计借助秘书智能体实现工作请求的并发、异步协商、选择与调度。智能体探测并发现了7个类别中51个真实及模拟的提供者，开展了19973次协商和6952次选择模拟以评估决策可靠性，结果显示协商准确率高达87.71%，选择成本与更传统的策略相当。该架构专为可扩展性设计，目前支持Genesis Mission，彰显了智能体科学中智能体、发现工具与基础设施间精心协调的重要性。
英文摘要
Agentic science is transforming the landscape of computational work, and is applied to scientific pipelines and workload managers. Scientific workloads require specialized hardware within and between institutions. Automated resource discovery is an essential step for scheduling workloads with specific hardware and environmental requirements. In this paper, we present a hierarchical, dynamic architecture and accompanying software to discover resources across diverse cloud, edge, and HPC systems. The design enables concurrent, asynchronous negotiation, selection, and dispatch of requests for work using secretary agents. The agents probe and discover 51 real and simulated providers across 7 categories. We perform 19,973 negotiation and 6,952 selection simulations to assess reliability of decisions, demonstrating high (87.71%) negotiation accuracy and selection costs comparable to more traditional strategies. Designed for extensibility and currently supporting the US DOE Genesis Mission, this architecture exemplifies the importance of careful coordination between agents, discovery tools, and infrastructure for agentic science.
Comments10 pages, 8 figures, 3 tables
