Experiments in Agentic AI for Science

• URL: http://arxiv.org/abs/2605.26305v2
• Date: Fri, 29 May 2026 18:43:27 GMT
• Title: Experiments in Agentic AI for Science
• Abstract summary: This paper details two novel frameworks for developing autonomous, agentic AI in scientific research.<n>The first agent, DeepTS/DeepCollector, automates the large-scale, extraction, and deduplication of time-series datasets.<n>The second, DeepScribe, is an autonomous presentation that converts visually dense, mathematically complex physics lectures into structured scientific reports.
• Score: 0.005439329219803859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

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