3 papers
cs.LG2026
Lossless Anti-Distillation Sampling
Zibo Diao, Jingchu Gai, Xinyue Ai +3
Frontier commercial generative models face a growing threat from distillation, whereby a distiller harvests generated responses and trains a competing model of its own at drastical…
cs.CV2026
Luminark: Training-free, Probabilistically-Certified Watermarking for General Vision Generative Models
Jiayi Xu, Zhang Zhang, Yuanrui Zhang +4
In this paper, we introduce \emph{Luminark}, a training-free and probabilistically-certified watermarking method for general vision generative models. Our approach is built upon a…
cs.DC2025
Minder: Faulty Machine Detection for Large-scale Distributed Model Training
Yangtao Deng, Xiang Shi, Zhuo Jiang +12
Large-scale distributed model training requires simultaneous training on up to thousands of machines. Faulty machine detection is critical when an unexpected fault occurs in a mach…