Experiments in Agentic AI for Science
This paper presents two novel agentic AI frameworks—DeepTS/DeepCollector and DeepScribe—that leverage a hybrid local-remote architecture to automate scientific workflows, including time-series data curation and lecture-to-report conversion, and discuss extensions to knowledge graphs and high-energy physics.
Article intelligence
Key points
- Two agentic AI frameworks: DeepTS/DeepCollector for time-series data, DeepScribe for lecture analysis.
- Hybrid Local Body, Remote Brain architecture using Google Colab and LLM backends.
- Techniques like Cellular RAG and distributed concurrency overcome context limitations.
- Future generalization to deep knowledge graphs and application in high-energy physics (DeepQCD).
Why it matters
This matters because two agentic AI frameworks: DeepTS/DeepCollector for time-series data, DeepScribe for lecture analysis.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26305] Experiments in Agentic AI for Science
[Submitted on 25 May 2026]
Title:Experiments in Agentic AI for Science
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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. 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).
Subjects:
Artificial Intelligence (cs.AI); Systems and Control (eess.SY); High Energy Physics - Phenomenology (hep-ph)
Cite as: arXiv:2605.26305 [cs.AI]
(or arXiv:2605.26305v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.26305
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Geoffrey Fox [view email] [v1] Mon, 25 May 2026 19:57:57 UTC (1,028 KB)
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