Problem
Research questions and friction points this paper is trying to address.
machine-readable models
data architecture
epistemic adequacy
AI participation
systems engineering
Models as Governed Interfaces for AI-Native MBSE: Read-Side Adequacy and Write-Side Admissibility
🤖 AI Summary
论文探讨了AI在系统工程中参与的问题，提出通过改进数据架构（如读侧充分性和写侧可接纳性）来解决模型中的信息缺失问题。
📝 Abstract
Programmatic access to machine-readable models such as SysML v2 is often treated as sufficient for AI participation in systems engineering. It is only a precondition. The remaining work lies in the data architecture around the model. An AI reader querying a structurally complete model encounters absent derivation chains, untagged epistemic status, missing provenance, and unreachable evidence. Faced with these gaps, the AI reader fills them from training data, which lies outside the governed record. We test on the public Apollo 11 SysML v2 reconstruction, exemplary by current practice. We name the missing property epistemic adequacy and offer it as a candidate data-architecture pattern in two halves. Read-side adequacy lets derivation, status, and provenance answer a query the model would otherwise leave to inference; write-side admissibility gates an AI contribution before it enters the record. The property decomposes into five criteria. Four sit on the read side, evidenced by the case and convergent literature; the fifth sits on the participation side, advanced as a hypothesis for future test. The architecture space spans an inline metadata extension to a substrate-native multi-model store, and over that space we propose the Governed-Query Architecture Framework, governing agent participation through the viewpoint conventions engineers already use. We state the reframing in falsifiable form: the epistemic layer stands only if it outperforms a retrieval-augmented baseline on the same model.
Innovation
Methods, ideas, or system contributions that make the work stand out.
epistemic adequacy
read-side adequacy
write-side admissibility
Governed-Query Architecture Framework
🔎 Similar Papers
No similar papers found.
