HepScript: A Dual-Use DSL for Human-AI Collaborative Data Analysis Workflows in High-Energy Physics
📰 ArXiv cs.AI
Learn how HepScript, a dual-use DSL, enables human-AI collaborative data analysis workflows in High-Energy Physics, boosting analytical efficiency
Action Steps
- Design a HepScript workflow to automate data analysis tasks using LLMs
- Implement HepScript in a High-Energy Physics experiment-specific codebase to leverage domain knowledge
- Use HepScript to generate executable code for data analysis pipelines
- Test and validate HepScript-generated code against existing workflows
- Integrate HepScript with agentic AI models to enable human-AI collaboration
Who Needs to Know This
Data analysts and researchers in High-Energy Physics can benefit from HepScript to streamline their workflows and collaborate with AI models more effectively. This can lead to increased productivity and discovery in the field.
Key Insight
💡 HepScript enables efficient human-AI collaboration in complex scientific workflows by providing a dual-use DSL for data analysis
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🚀 Boost analytical efficiency in High-Energy Physics with HepScript, a dual-use DSL for human-AI collaborative data analysis workflows! 🤖
Key Takeaways
Learn how HepScript, a dual-use DSL, enables human-AI collaborative data analysis workflows in High-Energy Physics, boosting analytical efficiency
Full Article
Title: HepScript: A Dual-Use DSL for Human-AI Collaborative Data Analysis Workflows in High-Energy Physics
Abstract:
arXiv:2605.01423v1 Announce Type: cross Abstract: The escalating data scale in High-Energy Physics (HEP) fuels a growing aspiration for higher analytical efficiency. While Large Language Models (LLMs) offer a path toward automation via agentic AI, they struggle with complex scientific workflows that require deep domain knowledge and are tightly coupled to experiment-specific codebases. To address this, we introduce a methodology centered on HepScript, a dual-use Domain-Specific Language (DSL) fo
Abstract:
arXiv:2605.01423v1 Announce Type: cross Abstract: The escalating data scale in High-Energy Physics (HEP) fuels a growing aspiration for higher analytical efficiency. While Large Language Models (LLMs) offer a path toward automation via agentic AI, they struggle with complex scientific workflows that require deep domain knowledge and are tightly coupled to experiment-specific codebases. To address this, we introduce a methodology centered on HepScript, a dual-use Domain-Specific Language (DSL) fo
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