发表机构
University of Tennessee; University of Duisburg-Essen（田纳西大学; 杜伊斯堡-埃森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人员提出一种分层架构，将智能体与算法功能分离，应用于压电力显微镜实现自主实验，可识别混杂关系并终止无意义测量，为科学自主提供可行路径。
AI 中文摘要
智能体人工智能的快速进展使科学系统能够解读开放式目标、整合异构信息、调用专用工具，并随着证据积累调整实验策略。然而，物理实验中也存在诸多任务，智能体推理在这些任务中优势甚微，还可能降低可靠性。定量分析、优化、空间定位、验证及仪器执行，通常更适合作为具有明确目标和可验证输出的确定性或算法操作来处理。在此，我们提出一种用于自主实验的分层架构，将这些角色分离：智能体组件解读科学意图、构建任务相关的实验表征、评估积累的证据并选择高级动作；而确定性算法则执行数值分析、坐标选择、验证及物理执行。我们将该架构应用于压电力显微镜，从关于局域畴结构与极化翻转关系的宽泛科学问题出发，系统从多通道成像构建空间描述符，选择并分析局域滞后测量结果，调整光谱波形，并在额外测量无法提供新证据时终止实验。该自主轨迹还识别出极化状态与畴壁邻近度之间的混杂关系，并确认请求的对比度未在可用视场内独立呈现。这些结果展示了一条通往科学自主的路径：智能体确定所需证据，而算法确定如何在可验证的物理约束内可重复地获取这些证据。
英文摘要
Rapid advances in agentic artificial intelligence enable scientific systems to interpret open-ended objectives, combine heterogeneous information, invoke specialized tools, and revise experimental strategies as evidence accumulates. However, physical experimentation also contains many tasks for which agentic reasoning provides little advantage and can reduce reliability. Quantitative analysis, optimization, spatial targeting, validation, and instrument execution are often better posed as deterministic or algorithmic operations with explicit objectives and verifiable outputs. Here, we introduce a hierarchical architecture for autonomous experimentation that separates these roles. Agentic components interpret scientific intent, construct task-dependent experimental representations, evaluate accumulated evidence, and select high-level actions, whereas deterministic algorithms perform numerical analysis, coordinate selection, validation, and physical execution. We implement this architecture in piezoresponse force microscopy. Starting from a broad scientific question concerning the relation between local domain structure and polarization switching, the system constructs spatial descriptors from multichannel imaging, selects and analyzes local hysteresis measurements, adapts the spectroscopy waveform, and terminates the experiment when additional measurements cease to provide new evidence. The autonomous trajectory also identifies a confounding relationship between polarization state and domain-wall proximity and recognizes that the requested contrast is not independently represented within the available field of view. These results demonstrate a route toward scientific autonomy in which agents determine what evidence is required while algorithms determine how that evidence is acquired reproducibly and within validated physical constraints.
Comments32 pages, 12 figures, 11 tables
