mlsanityData to 14 Oct 2026

SysTemp: A Multi-Agent System for Template-Based Generation of SysML v2

arXiv 2506.21608 ↗First posted 20 Jun 2025cs.CL cs.AIcited by 2

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Abstract (arXiv)

The automatic generation of SysML v2 models represents a major challenge in the engineering of complex systems, particularly due to the scarcity of learning corpora and complex syntax. We present SysTemp, a system aimed at facilitating and improving the creation of SysML v2 models from natural language specifications. It is based on a multi-agent system, including a template generator that structures the generation process. We discuss the advantages and challenges of this system through an evaluation, highlighting its potential to improve the quality of the generations in SysML v2 modeling.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 20 Jun 2025.

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12 of 28 references.

References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  5. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  6. 2024A Survey on Large Language Models for Code Generationcited by 658
  7. 2023Execution-based Code Generation using Deep Reinforcement Learningcited by 99
  8. 2023Task Contamination: Language Models May Not Be Few-Shot Anymorecited by 99
  9. 2024Why Does the Effective Context Length of LLMs Fall Short?cited by 63
  10. 2024How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMscited by 23
  11. 2023Boosting Large Language Model for Speech Synthesis: An Empirical Studycited by 21
  12. 2022Towards Automatic Model Completion: from Requirements to SysML State Machinescited by 1

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Citing papers per year: 2026: 2.

2 papers.