arXiv AI June 9, 2026 By Pramit Kumar Bhaduri, Mahdi Taheri, Samira Nazari, Maksim Jenihhin, Christian Herglotz, Michael Hubner

SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors

Read the original on arXiv AI →

arXiv:2606. 07620v1 Announce Type: cross Abstract: With the growth of Vision Transformers in safety-critical domains like autonomous systems and medical imaging, ensuring their reliability against soft errors is paramount.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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nlp

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Sep 1

On the Resilience of Text-to-Video Diffusion Models to Hardware Faults

arXiv:2608.29598v1 Announce Type: new Abstract: We present the first systematic study of the resilience of text-to-video (T2V) diffusion models under random hardware-level faults. While T2V models ar...

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arXiv:2603. 22770v2 Announce Type: replace-cross Abstract: The deployment of deep neural networks (DNNs) in safety-critical edge environments necessitates robustness against hardware-induced bit-flip errors.

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Visual Tripwires: Anticipating Failure in Deep Vision Systems

arXiv:2609.28099v1 Announce Type: cross Abstract: Deep vision systems remain vulnerable to corruption, occlusion, and distribution shift despite strong benchmark performance. Existing reliability met...

By Anoushka Harit, Rehan Zuberi, William Prew, Florian Markowetz

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Related stories

CheckOne: Lightweight Fault Detection and Mitigation for Vision Transformers

TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks

PVF:Understanding AI Vulnerability Against SDCs

On the Resilience of Text-to-Video Diffusion Models to Hardware Faults

From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips

Visual Tripwires: Anticipating Failure in Deep Vision Systems