arXiv:2608.21366v1 Announce Type: new
Abstract: Driven by massive amounts of web-scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors....
By Xihao Xie, Beichen Hu
arXiv:2502. 18049v5 Announce Type: replace-cross Abstract: Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation.
By Hengzhi He, Shirong Xu, Guang Cheng
arXiv:2410. 12341v4 Announce Type: replace-cross Abstract: As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy.
By Daniele Gambetta, Gizem Gezici, Fosca Giannotti, Dino Pedreschi, Alistair Knott, Luca Pappalardo
arXiv:2510. 16657v3 Announce Type: replace-cross Abstract: Synthetic data has been increasingly used to train frontier generative models.
By Bingji Yi, Qiyuan Liu, Yuwei Cheng, Haifeng Xu
The paper investigates how to avoid model collapse when training large language models with synthetic data. It establishes theoretical guarantees for the minimum ratio of human to synthetic data needed to maintain training stability, using the Fisher‑Rao metric to analyze dynamics on the probability simplex. The authors derive contraction and invariance bounds that remain meaningful even in high dimensions, showing that the required data ratio differs from earlier estimates.
By Matteo Marchi, Jo\~ao Pedro Silvestre, Bahman Gharesifard, Paulo Tabuada
arXiv:2605. 04127v2 Announce Type: replace Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern as artificially generated content proliferates.
By Devon Jarvis, Richard Klein, Benjamin Rosman, Steven James, Stefano Sarao Mannelli