Hierarchical Fault Detection and Diagnosis for Transformer Architectures
Transformers now underpin critical AI systems across industry and research. Yet their faults can silently alter model behavior without runtime errors, and existing techniques offer little support for tracing these failures to their component and root cause. Such faults evade detection because loss and numerical values stay normal, and the visible symptom rarely identifies the component responsible. We present DEFault++, a hierarchical learning-based technique that first detects a fault, then identifies the affected component, and finally the cause within it, helping developers effectively debug transformer models. DEFault++ organizes component-level runtime measurements with a Fault Propa...
| Authors | Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma et al. |
|---|---|
| Primary category | cs.SE |
| Published | 30 April 2026 |
| Last updated at source | 25 June 2026 |
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