Fifty Years of Specification Completeness: What Aviation Certification Tells AI Governance About Epoch Limits, Proof Surfaces, and the Structural Gap
2026-06-25T08:51:53Z•edf8081e6f18505e56f828f015959898660a78d07521219b646d649e1d334569
AI governanceAPI versioningAgent Context Files (ACF)DNN fuzzingDO-178CDO-330LLMsLibEvoBenchPromptQadaptive perturbationagent governancebenchmarkscode clone detectioncode generationempirical evaluationepoch limitsicat-agentmulti-agent systemsproof surfacessafety-critical systemssemantic clonessoftware birthmarkssoftware supply chaintensor-based fuzzingtraceability
What happened
This feed collects recent CS/security-adjacent research touching AI governance, testing, and software-assurance for ML-enabled systems. Key papers: (1) A governance analysis mapping aviation certification (DO-178C/DO-330) to AI governance artifacts, arguing for epoch-limited validity, traceable proof surfaces, and objective evidence — finding ~37% of AI governance docs fail structural quality thresholds and proposing PromptQ to operationalize requirements. (2) Tensor-based batch fuzzing with adaptive perturbation scaling that embeds constraints into wrapped networks to achieve up to 40x higher
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- edf8081e6f18505e56f828f015959898660a78d07521219b646d649e1d334569
- Enrichment time
- 2026-06-25T08:51:53Z
- AI-assisted enrichment
- Yes
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