AI 中文总结
研究将大语言模型驱动的自主控制系统部署在太阳望远镜上，采用多智能体框架及解耦三层架构，通过感知智能体编码专业知识，经中央推理引擎处理，实现实时自适应调度，跨季节验证效果良好，提升了观测等多方面能力。
AI 中文摘要
我们展示了首个由大语言模型驱动的端到端自主控制系统在运行中的太阳望远镜——全日面多层磁像仪（SFMM）上的部署，即JW-ASTClaw。该系统采用多智能体框架，具有通过模型上下文协议互连的解耦三层架构（感知-决策-执行），可在复杂环境条件下进行实时自适应调度并实现高可移植性。三个感知智能体编码高级观测员专业知识，中央推理引擎进行多源融合和冲突解决。系统支持从云大语言模型到本地推理再到基于规则的回退的优雅降级。跨季节验证表明，在10个不同观测日期的存档数据上，云检测率达100%且零误报，活动区计数和位置与NOAA太阳区域总结报告密切匹配。这些能力显著提高了科学意图驱动的观测可及性等。
英文摘要
We present the first deployment of an end-to-end autonomous control system driven by a large language model (LLM) on an operational solar telescope-the Solar Full-disk Multi-layer Magnetograph (SFMM), named JW-ASTClaw. This system employs a multi-agent framework adopting a decoupled three-layer architecture (perception-decision-execution) interconnected through the Model Context Protocol (MCP), which addresses real-time adaptive scheduling under complex environmental conditions while achieving high portability: the perception and decision logic are reused unchanged across instruments, requiring only telescope-specific command interfaces to be adapted. Three perception agents-data-quality-agent, cloud-analyzer-agent, and flare-detector-agent-encode senior observer expertise, including wind jitter detection via limb-ring standard deviation, projected-circle zonal cloud analysis, and multi-band active region identification, as LLM-callable rules, while a central reasoning engine performs multi-source fusion and conflict resolution. The system supports graceful degradation from cloud LLM to local inference and finally to rule-based fallback, designed for remote field stations with unstable connectivity. Cross-season validation on archival data demonstrates 100% cloud detection with zero false positives across 10 distinct observation dates, with active-region counts and positions closely matching the NOAA Solar Region Summary (SRS) reports (102 vs. 100 across 10 separate validation dates). These capabilities significantly improve scientific-intent-driven observation accessibility, enable rapid flare response for space weather monitoring, enhance data usability under adverse conditions, and increase observability during partially cloudy periods.
Comments17 pages, 6 figures,
