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

Paper recorded by Signals 4 on 2026-08-30 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

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 are widely used for automated video generation due to their ability to produce high-quality, temporally coherent, and realistic videos, their iterative denoising process and spatiotemporal dependencies introduce unique failure modes. We perform an ext

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#191 most recent of 215 cs.LG papers we have recorded · ↑ newer: Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies unde · ↓ older: Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Serie
Cite this page: On the Resilience of Text-to-Video Diffusion Models to Hardware Faults: the #191 most recent of 215 cs.LG papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/on-the-resilience-of-text-to-video-diffusion-models-to-hardware-faults.html
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