DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures

ArXi:2604.28118v1 Announce Type: cross Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal components often degrade behavior silently without raising runtime errors. Existing fault diagnosis techniques often target generic deep neural networks and cannot identify which transformer component is responsible for an observed symptom.