# Taming Silent Failures: A Framework for Verifiable AI Reliability

arXiv: https://arxiv.org/abs/2510.22224 · HTML: https://mlsanity.com/paper/2510.22224/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving](https://mlsanity.com/paper/2403.12176/) (2024)
- [Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard](https://mlsanity.com/paper/2508.20504/) (2025)
- [Correct-by-construction requirement decomposition](https://mlsanity.com/paper/1909.02070/) (2019)
- [Runtime Monitoring DNN-Based Perception](https://mlsanity.com/paper/2310.03999/) (2023)

## Built on by (most cited first, 1 of 1)

- [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026)

## Abstract (arXiv)

The integration of Artificial Intelligence (AI) into safety-critical systems introduces a new reliability paradigm: silent failures, where AI produces confident but incorrect outputs that can be dangerous. This paper introduces the Formal Assurance and Monitoring Environment (FAME), a novel framework that confronts this challenge. FAME synergizes the mathematical rigor of offline formal synthesis with the vigilance of online runtime monitoring to create a verifiable safety net around opaque AI components. We demonstrate its efficacy in an autonomous vehicle perception system, where FAME successfully detected 93.5% of critical safety violations that were otherwise silent. By contextualizing our framework within the ISO 26262 and ISO/PAS 8800 standards, we provide reliability engineers with a practical, certifiable pathway for deploying trustworthy AI. FAME represents a crucial shift from accepting probabilistic performance to enforcing provable safety in next-generation systems.