用智能体框架自动处理科学数据与讲座，提升科研效率。
Experiments in Agentic AI for Science
- 本地控制+云端大模型协同，实现自动化科研流程
- 可批量清洗时间序列数据，生成结构化科学报告
- 适合需要高效处理复杂文献与数据的科研人员
本文提出两个新型自主智能体框架，用于科学工作流中的自动化。两个系统均基于谷歌协作平台（Google Colab）的混合本地主体-远程大脑架构，通过基于Python的本地调度器调用大语言模型（LLM）云后端。第一个智能体DeepTS/DeepCollector实现大规模时间序列数据的自动采集、提取与去重。第二个智能体DeepScribe能自主分析视觉密集、数学复杂的物理讲座，生成结构化科学报告。通过细粒度属性提取（Cellular RAG）、远程数据检查和分布式并发控制等系统工程手段，证明了智能体可突破当前主流系统在上下文与推理上的局限，有效支持科学工作流。最后，文章将DeepTS推广至构建深层知识图谱，并探讨该方法在高能物理（DeepQCD）中的应用前景。
原文摘要 · Abstract (English)
This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote Brain architecture via Google Colab, utilizing Python-based local orchestrators to invoke large language model (LLM) cloud backends. The first agent, DeepTS/DeepCollector, automates the large-scale curation, extraction, and deduplication of time-series datasets. The second, DeepScribe, is an autonomous presentation analyzer that converts visually dense, mathematically complex physics lectures into structured scientific reports. Through practical systems engineering-such as granular attribute extraction (Cellular RAG), remote data inspection, and distributed concurrency controls-we demonstrate how agentic AI can overcome the context and reasoning limitations of current state-of-the-art systems to rigorously support scientific workflows. Finally, we outline a generalization of DeepTS to support deep knowledge graphs and discuss the application of this conceptual approach to high-energy physics (DeepQCD).
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