mlsanityData to 14 Oct 2026

BPMN Assistant: An LLM-Based Approach to Business Process Modeling

arXiv 2509.24592 ↗First posted 29 Sep 2025cs.AI cs.SEcited by 2

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Abstract (arXiv)

This paper presents BPMN Assistant, a tool that leverages Large Language Models for natural language-based creation and editing of BPMN diagrams. While direct XML generation is common, it is verbose, slow, and prone to syntax errors during complex modifications. We introduce a specialized JSON-based intermediate representation designed to facilitate atomic editing operations through function calling. We evaluate our approach against direct XML manipulation using a suite of state-of-the-art models, including GPT-5.1, Claude 4.5 Sonnet, and DeepSeek V3. Results demonstrate that the JSON-based approach significantly outperforms direct XML in editing tasks, achieving higher or equivalent success rates across all evaluated models. Furthermore, despite requiring more input context, our approach reduces generation latency by approximately 43% and output token count by over 75%, offering a more reliable and responsive solution for interactive process modeling.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

14 of 43 references.

References · 14

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2024Qwen2.5 Technical Reportcited by 4,942
  3. 2024GPT-4o System Cardcited by 4,588
  4. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  5. 2023Large Language Models can accomplish Business Process Management Taskscited by 34
  6. 2024Process Modeling With Large Language Modelscited by 26
  7. 2023Just Tell Me: Prompt Engineering in Business Process Managementcited by 17
  8. 2024Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Taskscited by 14
  9. 2024Evaluating Large Language Models on Business Process Modeling: Framework, Benchmark, and Self-Improvement Analysiscited by 13
  10. 2024ProMoAI: Process Modeling with Generative AIcited by 12
  11. 2023Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)cited by 9
  12. 2024A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Modelscited by 6
2 more
  1. 2021Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challengescited by 4
  2. 2023Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?cited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Textcited by 0
  2. 2026Artificial Intelligence and Modeling & Simulation: An Overviewcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Instruction-Tuning Open-Weight Language Models for BPMN Model Generation
  2. 2025Structured Extraction from Business Process Diagrams Using Vision-Language Models
  3. 2026Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends
  4. 2026Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution
  5. 2024SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents
  6. 2026Pragmos: A Process Agentic Modeling System
  7. 2024FlowMind: Automatic Workflow Generation with LLMs
  8. 2026SAGE: Structured Agentic Graph Editing for Software Diagrams