Experiments in Agentic AI for Science

📰 ArXiv cs.AI

Learn how to develop autonomous agentic AI for scientific workflows using hybrid Local Body, Remote Brain architecture and large language models

advanced Published 27 May 2026

Action Steps

Design a hybrid Local Body, Remote Brain architecture using Google Colab and Python
Implement a local orchestrator to invoke large language model cloud backends
Develop an autonomous agent like DeepTS/DeepCollector to automate data curation and extraction
Use the agent to deduplicate and process large-scale time-series datasets
Apply the second agent, DeepScribe, to automate data transcription and labeling

Who Needs to Know This

Data scientists and AI researchers can benefit from this paper to automate scientific workflows and improve data curation and extraction. This can be applied in various fields such as physics, biology, and climate science.

Key Insight

💡 Hybrid Local Body, Remote Brain architecture can be used to develop autonomous agentic AI for scientific workflows

Key Takeaways

Learn how to develop autonomous agentic AI for scientific workflows using hybrid Local Body, Remote Brain architecture and large language models

Full Article

Title: Experiments in Agentic AI for Science

Abstract:
arXiv:2605.26305v1 Announce Type: new 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 autono

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