arXiv:2604.28118v2 Announce Type: replace-cross Abstract: 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 finall
The increasing complexity and criticality of AI systems powered by Transformer architectures necessitate advanced fault detection as these systems are deployed in real-world, high-stakes environments.
Ensuring the reliability and explainability of Transformer-based AI is crucial for their safe and effective integration across industries, preventing catastrophic failures and building trust in autonomous systems.
The ability to accurately diagnose and trace silent failures in large AI models will accelerate debugging, improve robustness, and enable more confident deployment of advanced AI applications.
- · AI developers
- · High-reliability AI sectors
- · Defensive AI tooling companies
- · Companies relying on brittle AI deployments
- · AI safety compliance auditors without advanced tools
Improved reliability and faster iteration cycles for large language models and other Transformer-based AI.
Increased adoption of AI in critical infrastructure and safety-sensitive applications due to enhanced trustworthiness.
New regulatory frameworks and compliance standards for AI systems that mandate diagnostic capabilities similar to DEFault++.
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Read at arXiv cs.LG