mlsanityData to 14 Oct 2026

Agent-based code generation for the Gammapy framework

arXiv 2509.26110 ↗First posted 30 Sep 2025cs.SE astro-ph.IMcited by 2

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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resources and may expose unstable APIs under active development, making it difficult for models trained on limited or outdated data.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

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 5 of its 10 references that are in the map. First posted on arXiv 30 Sep 2025.

Builds on

5 of 10 references.

References · 5

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2023A Survey of Large Language Modelscited by 2,576
  3. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,386
  4. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 205
  5. 2025AI Agents for Ground-Based Gamma Astronomycited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Domain-Specific Agents for Cherenkov Telescope Array Control Software and Gamma-Ray Data Analysiscited by 0
  2. 2026Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branchescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Survey on Code Generation with LLM-based Agents
  2. 2024CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges
  3. 2024Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization
  4. 2025AdaCoder: An Adaptive Planning and Multi-Agent Framework for Function-Level Code Generation
  5. 2025CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation
  6. 2024CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models
  7. 2023AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
  8. 2025Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency