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New framework proposed for AI in systems engineering

By PulseAugur Editorial · [1 sources] ·

This paper introduces the Governed-Query Architecture Framework (GQAF) to address challenges in AI participation in systems engineering. The GQAF aims to ensure AI systems can accurately derive information from machine-readable models like SysML v2, rather than filling gaps with unverified training data. It proposes 'epistemic adequacy' as a key property, focusing on providing derivation, status, and provenance for AI queries, and 'write-side admissibility' to govern AI contributions before they are recorded. AI

IMPACT This framework could improve the reliability and verifiability of AI systems used in complex engineering tasks.

RANK_REASON The item is an academic paper detailing a new framework for AI in systems engineering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New framework proposed for AI in systems engineering

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The item is an academic paper detailing a new framework for AI in systems engineering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jason Gower, Michael J. de C. Henshaw, Siyuan Ji ·

    Models as Governed Interfaces for AI-Native MBSE: Read-Side Adequacy and Write-Side Admissibility

    arXiv:2609.16252v1 Announce Type: cross Abstract: Machine-readable models such as SysML v2 are now programmatically accessible, and a growing body of work treats that access as the enabling condition for AI participation in systems engineering. Access is necessary, but not suffic…

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