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| 001 | 641203 | ||
| 005 | 20260209135949.0 | ||
| 024 | 7 | _ | |a arXiv:2509.26110 |2 arXiv |
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| 100 | 1 | _ | |a Kostunin, Dmitriy |0 P:(DE-H253)PIP1085187 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 39th International Cosmic Ray Conference |g ICRC2025 |c Geneva |d 2025-07-14 - 2025-07-24 |w Switzerland |
| 245 | _ | _ | |a Agent-based code generation for the Gammapy framework |
| 260 | _ | _ | |a Trieste |c 2025 |b SISSA |
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| 500 | _ | _ | |a ICRC2025 proceedings PoS(ICRC2025)753 |
| 520 | _ | _ | |a 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resources and may expose unstable APIs under active development, making it difficult for models trained on limited or outdated data.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. |
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| 700 | 1 | _ | |a Sotnikov, Vladimir |b 1 |
| 700 | 1 | _ | |a Golovachev, Sergo |b 2 |
| 700 | 1 | _ | |a Mehta, Abhay |0 P:(DE-H253)PIP1102748 |b 3 |e Corresponding author |u desy |
| 700 | 1 | _ | |a Holch, Tim Lukas |0 P:(DE-H253)PIP1022858 |b 4 |
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| 773 | _ | _ | |a 10.22323/1.501.0753 |0 PERI:(DE-600)2642026-0 |p 753 |t Proceedings of Science / International School for Advanced Studies |v (ICRC2025) |y 2025 |x 1824-8039 |
| 787 | 0 | _ | |a Kostunin, Dmitriy et.al. |d 2025 |i IsParent |0 PUBDB-2026-00668 |r arXiv:2509.26110 |t Agent-based code generation for the Gammapy framework |
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