让MBSE模型适应AI参与，而非让AI迁就过时模型。
AI as Consumer and Participant: A Co-Design Agenda for MBSE Substrates and Methodology
- 将模型设计为可机器查询的知识库，而非仅供人类阅读的文档。
- 同一模型用不同AI工具处理，结果不一致且无记录可查。
- 呼吁学界共同设计模型与方法论，避免未来架构盲目定型。
当前AI工具正被应用于MBSE模型，但这些模型并非为此类使用而设计。问题不仅在于工具幻觉：经过良好提示的前沿模型在符合规范的SysML模型上仍能生成有用输出，但其推理来自训练数据而非模型本身，同一模型在不同工具下产生差异结果且无记录可比对。这意味着模型仅充当提示输入，而非知识源。单纯附加更优工具无法解决此问题。必须协同设计模型与方法论，使模型成为可机器查询的知识基底，而非仅供人类导航的结构化产物。本文通过具体工作流展示该缺口的实际表现，提出三项共同原则以界定模型与方法应达成的目标，并呼吁社区在架构决策固化前启动系统性共建。
原文摘要 · Abstract (English)
AI tools are being deployed over MBSE models today, and those models were not designed for this kind of consumption. The problem is not simply that tools hallucinate: well-prompted frontier models produce competent, useful output over a conformant SysML model, but the reasoning they produce is drawn from training rather than retrieved from the model itself, and different tools over the same model produce different results with nothing in the record to adjudicate between them. The model, in other words, is functioning as a prompt rather than as a knowledge base. Attaching better tools to the same model does not resolve this. The model and the methodology that governs its construction need to be designed together for AI participation, treating the model as a machine-queryable knowledge substrate rather than a structured artefact for human navigation, and that co-design has not yet happened in any systematic way. This paper works through a concrete workflow scenario to show what that gap looks like in practice, proposes three principles that jointly characterise what model and methodology must achieve together, and closes with a call to the community to begin this work before the architectural decisions about AI integration settle without the methodological foundation they require.
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