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arXiv cs.AI· Marek Deja·· 4 小时前AI 评分38

tok:面向人类与 AI 智能体的 BPMN 和 DMN 文本具体语法

tok: a textual concrete syntax of BPMN and DMN for humans and AI agents

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论文提出 tok,一种面向 BPMN 2.0.2 流程模型和 DMN 1.5 决策的纯文本记法,单一引擎可按两项标准校验文档、编译为带自动图的 XML,并通过 Model Context Protocol 服务于大语言模型应用。

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Abstract:tok is a plain-text notation for Business Process Model and Notation (BPMN) 2.0.2 process models and Decision Model and Notation (DMN) 1.5 decisions. One engine checks a tok document against both standards, compiles the document to XML with an automatic diagram, and serves language-model applications through the Model Context Protocol. The paper classifies the notation as an alternative concrete syntax of the BPMN and DMN metamodels and reports one study. A tok model takes 2.2 times fewer tokens than the smallest BPMN XML of 893 files exported by 37 tools and 7.7 times fewer than the files as exported. The checker signals 98.2% of 1,411 injected faults, against 85.8% for the XML Schema with bpmnlint. Four language models new to tok write valid tok from a one-page reference for 85.7 to 100% of 87 process descriptions from two datasets and valid BPMN XML for every description, and the strongest model writes tok with 25% and 19% fewer tokens than BPMN XML. As agents with a lean skill and one checker tool, the models write valid tok for 97.6 to 100% of the descriptions, the strongest model spends 21% and 14% fewer tokens on tok than on BPMN XML, and a skill which sends the model to read the reference through a tool costs 1.7 to 2.8 times the tokens of the lean skill. Of 47 event logs simulated from public models, 44 fit their source models, and tok imports every model discovered from the logs.
Comments: 29 pages, 3 figures
Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2611.07945 [cs.CY]
  (or arXiv:2611.07945v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2611.07945

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Marek Deja [view email]
[v1] Thu, 5 Nov 2026 12:42:56 UTC (158 KB)

来源:arXiv cs.AI · arxiv.org