HM27-A: Holonomy-Invariant Diagnosis of AI Malfunctions

On the same morning, two major works appeared:(1) HM27 — GA-Time Holonomy: The Twelve Invariants of Multi-Future AI, introducing a complete invariant framework for geometric AI failure analysis; and(2) the 200-page International AI Safety Report 2026 (Bengio et al.), presenting a global taxonomy of AI malfunctions. HM27-A serves as a direct addendum linking these two publications. It constructs the first bidirectional mathematical mapping between the malfunction classes in the 2026 report and the twelve holonomy invariants defined in HM27. Through five structured tables (A–E), the addendum provides conceptual, mechanistic, deep-geometric, diagnostic, and minimal-invariant correspondences, demonstrating that all failure modes identified in the Bengio et al. taxonomy can be derived from—and classified by—well-defined holonomy-invariant breakdowns with precise geometric signatures. HM27-A positions GA-Time Holonomy as a mechanism-level safety geometry underpinning modern AI malfunction behavior.