arXiv cs.AI· Guoyang Shi, Hongwen Li, Yiding Yu, Zhuang Fu, Siqi Ding, Zhengyuan Chen, JianGuo Chen, Hanyue Zhao, Tianyuan Liu·· 12 小时前AI 评分38
多模态智能体如何实现非平稳响应科学仪器的自适应控制
Adaptive Control of Scientific Instruments with Nonstationary Responses through Multimodal Agents
AI 导读
研究提出一种物理接地多模态智能体，用于控制响应随操作历史漂移的科学仪器，并以磁约束聚变装置 NBI 离子源的逐发调谐为例验证。在 EXL-50U 四平台 455 发严格时序回放中，仅用物理符号比仅用波形图像的质量分类 macro-F1 高 0.21-0.32，检索上下文使与历史动作方向的平均一致率提升 22.4-42.7 个百分点。
正文 · 原文
Abstract:Controlling scientific instruments requires parameters to be adjusted progressively in response to feedback from successive operations. When unobserved operating conditions evolve with operational history, responses become nonstationary, and a static mapping from actions to responses becomes inadequate for decisions across shots. Shot-to-shot tuning of neutral beam injection (NBI) ion sources in magnetic confinement fusion devices is representative of this class of problems: the conditions of electrode surfaces and vacuum are difficult to observe directly, discharge outcomes vary between shots, and experiments are costly and carry operational risks. We propose a physically grounded multimodal agent with three independently auditable components. Deterministic algorithms extract symbolic physical quantities and visual evidence from multichannel waveforms; a memory with two layers organizes trajectories from the current task, experience from earlier tasks, and procedural knowledge, from which a multimodal large language model (MLLM) generates candidate actions; and an independent action feasibility check enforces action constraints before execution. In a strictly chronological replay of 455 shots from four EXL-50U platforms, physical symbols alone outperformed waveform images alone by 0.21-0.32 in macro-F1 for quality classification across all four evaluated models. Retrieved context increased the mean agreement with historical action directions by 22.4-42.7 percentage points, whereas the contribution of visual evidence varied across models. Under supervised operation on five ion sources, the system achieved a beam-extraction rate of 86.80% across 409 executed shots. These results demonstrate the engineering feasibility of the framework for supervised sequential tuning on a physical scientific instrument.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32370 [cs.AI] |
| (or arXiv:2610.32370v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32370 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Guoyang Shi [view email]
[v1]
Fri, 23 Oct 2026 08:59:43 UTC (6,574 KB)
来源：arXiv cs.AI · arxiv.org