Preprint PUBDB-2026-00668

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Agent-based code generation for the Gammapy framework

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2025

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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resources and may expose unstable APIs under active development, making it difficult for models trained on limited or outdated data.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.


Note: ICRC2025 proceedings PoS(ICRC2025)753

Contributing Institute(s):
  1. Datenverarbeitung (Z_DV)
  2. High Energy Steroscopic System (Z_HESS)
Research Program(s):
  1. 613 - Matter and Radiation from the Universe (POF4-613) (POF4-613)
Experiment(s):
  1. High Energy Stereoscopic System
  2. Cherenkov Telescope Array

Appears in the scientific report 2025
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Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 ; OpenAccess ; Published
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http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png 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 20252025-07-142025-07-24 Proceedings of Science / International School for Advanced Studies (ICRC2025), 753 () [10.22323/1.501.0753]  GO OpenAccess  Download fulltext Files  Download fulltextFulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS


 Record created 2026-02-05, last modified 2026-04-24


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