Experiments in Agentic AI for Science
The paper discusses two innovative frameworks for creating autonomous AI systems to enhance scientific workflows. These systems utilize a hybrid architecture that combines local orchestrators with cloud-based large language models. The authors demonstrate how these agentic AI systems can address the limitations of current technologies in scientific research.
- ▪The first agent, DeepTS/DeepCollector, automates the curation and extraction of time-series datasets.
- ▪The second agent, DeepScribe, converts complex physics lectures into structured reports.
- ▪The frameworks utilize a Local Body, Remote Brain architecture via Google Colab.
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Computer Science > Artificial Intelligence arXiv:2605.26305 (cs) [Submitted on 25 May 2026] Title:Experiments in Agentic AI for Science Authors:Judy Fox, Geoffrey Fox View a PDF of the paper titled Experiments in Agentic AI for Science, by Judy Fox and 1 other authors View PDF Abstract: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.
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