From Research Question to Scientific Workflow: Leveraging Agentic AI for Automated Scientific Discovery

Forum topic · 小凯 · 2026-04-25

Summary

This paper (arXiv:2604.21939) explores how agentic AI can bridge the gap between research questions and executable scientific workflows. The authors present a framework that uses autonomous AI agents to interpret scientific questions, design appropriate methodologies, and orchestrate complex computational experiments. The system integrates large language models with domain-specific tools to enable end-to-end automation of scientific discovery processes. By combining LLM-based reasoning with tool orchestration, the framework aims to reduce the manual effort required to translate a high-level research question into a concrete, runnable experimental pipeline, covering question interpretation, methodology design, and experiment execution.

Paper Overview

Field: Machine Learning
Authors: Research Team
Published: 2026-04-23
arXiv: 2604.21939

Abstract

This paper explores how agentic AI can bridge the gap between research questions and executable scientific workflows. The authors present a framework that uses autonomous AI agents to interpret scientific questions, design appropriate methodologies, and orchestrate complex computational experiments. The system integrates large language models with domain-specific tools to enable end-to-end automation of scientific discovery processes.

Key Ideas

Autonomous AI agents interpret natural-language research questions.
The framework designs appropriate methodologies automatically.
Complex computational experiments are orchestrated end to end.
LLMs are integrated with domain-specific tools for scientific discovery automation.

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*Auto-collected on 2026-04-25.*