| Home > Publications database > Agent-based code generation for the Gammapy framework |
| Preprint | PUBDB-2026-00668 |
; ; ; ; ;
2025
This record in other databases:
Please use a persistent id in citations: doi:10.3204/PUBDB-2026-00668
Report No.: arXiv:2509.26110
Abstract: Software code generation using Large Language Models (LLMs) is one of the most successful applications of modern artificial intelligence. Foundational models are very effective for popular frameworks that benefit from documentation, examples, and strong community support. In contrast, specialized scientific libraries often lack these resources and may expose unstable APIs under active development, making it difficult for models trained on limited or outdated data. We address these issues for the Gammapy library by developing an agent capable of writing, executing, and validating code in a controlled environment. We present a minimal web demo and an accompanying benchmarking suite. This contribution summarizes the design, reports our current status, and outlines next steps.
|
The record appears in these collections: |
Journal Article/Contribution to a conference proceedings
Agent-based code generation for the Gammapy framework
39th International Cosmic Ray Conference, ICRC2025, GenevaGeneva, Switzerland, 14 Jul 2025 - 24 Jul 2025
Proceedings of Science / International School for Advanced Studies (ICRC2025), 753 (2025) [10.22323/1.501.0753]
Files
Fulltext by arXiv.org
BibTeX |
EndNote:
XML,
Text |
RIS