Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels accumulated over decades of evolution. Operators and AI systems rely on informal expert knowledge, inconsistent naming conventions, and fragmented documentation to locate signals for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and language-model-driven interfaces. We formalize semantic channel finding-mapping natural-language intent to concrete control-system signals-as a general problem in complex experimental infrastructure, and introduce a four-paradigm framework to guide architecture selection across facility-specific data regimes. The paradigms span (i) direct in-context lookup over curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale-from compact free-electron lasers to large synchrotron light sources-and diverse control-system architectures, from clean hierarchies to legacy environments. These implementations achieve 90-97% accuracy on expert-curated operational queries.
翻译：现代实验平台（如粒子加速器、聚变装置、望远镜及工业过程控制系统）在数十年演进过程中积累了数万至数十万个控制与诊断通道。操作员与人工智能系统依赖非正式专家知识、不一致的命名规范及碎片化文档来定位用于监测、故障排除和自动控制的信号，这为系统可靠性、可扩展性及语言模型驱动接口带来了持续瓶颈。本文将语义通道发现——即从自然语言意图映射到具体控制系统信号——形式化为复杂实验基础设施中的通用问题，并提出一个四范式框架以指导跨设施特定数据体系的架构选择。这些范式涵盖：（i）基于规范化通道词典的直接上下文查找，（ii）通过结构化树进行的受限分层导航，（iii）利用迭代推理与工具化数据库查询的交互式智能体探索，以及（iv）以本体论为基础的语义搜索——该范式将通道语义与设施特定命名规范解耦。我们通过在四个运行设施中的概念验证实现展示了各范式应用，这些设施规模跨越两个数量级（从紧凑型自由电子激光器到大型同步辐射光源），且控制系统架构多样（从清晰层级结构到遗留环境）。实验表明，这些实现对专家策划的操作查询达到了90-97%的准确率。
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