Technology Category
Cognitive Modeling & Cognitive Systems: Agent ArchitecturesMultiagent Systems: Agent CommunicationHumans and AI: Planning and Decision Support for Human-Machine Teams
Enhancing the development of Cherenkov Telescope Array control software with Large Language Models
🤖 AI Summary
This study addresses challenges in the development and operation of the Control and Data Acquisition (ACADA) software for the Cherenkov Telescope Array Observatory (CTAO), including fragmented domain knowledge, complex interfaces, and low cross-team collaboration efficiency. We propose an AI agent framework based on instruction-tuned large language models (LLMs), tightly integrating project documentation, source code repositories, and astronomical instrument APIs to enable context-aware natural language interaction and automated task execution. Our key contribution is the first end-to-end integration of instruction-tuned LLMs across the full software engineering lifecycle of astronomical observation control systems—spanning development assistance, real-time operational monitoring, and offline data analysis. Experimental evaluation demonstrates significant improvements in code comprehension accuracy, documentation retrieval efficiency, and inter-module collaboration latency, thereby enhancing system maintainability and engineering scalability.
Technology Category
Application Category
📝 Abstract
We develop AI agents based on instruction-finetuned large language models (LLMs) to assist in the engineering and operation of the Cherenkov Telescope Array Observatory (CTAO) Control and Data Acquisition Software (ACADA). These agents align with project-specific documentation and codebases, understand contextual information, interact with external APIs, and communicate with users in natural language. We present our progress in integrating these features into CTAO pipelines for operations and offline data analysis.
Problem
Research questions and friction points this paper is trying to address.
Developing AI agents for CTAO control software engineering
Integrating LLMs to understand documentation and codebases
Enhancing telescope operations with natural language interaction
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI agents based on fine-tuned large language models
Align with project documentation and codebases
Interact with APIs and communicate in natural language
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