Language generation with repetition: a theoretical view of model collapse

The rise of large language models has transformed the technology industry, but it has also raised questions about the sustainability of their training as natural data runs out and machine-generated content increases. This phenomenon, known as model collapse, is now studied from a theoretical perspective that analyzes how reusing the generator's own outputs can degrade its performance. At Q2BSTUDIO, as a company specialized in custom applications and artificial intelligence solutions for businesses, we understand the importance of maintaining data quality in machine learning processes. The theoretical approach reveals that while certain cleaning mechanisms or watermarking work under ideal conditions, in non-uniform scenarios collapse is inevitable. This has direct implications for the development of AI for businesses, where the integrity of training data is key to avoiding biases and loss of precision. Our AWS and Azure cloud services allow managing large-scale data pipelines, while cybersecurity and pentesting ensure that information repositories are not contaminated with malicious content. Additionally, AI agents and business intelligence tools such as Power BI benefit from a robust ecosystem where model collapse is mitigated through controlled replay architectures. Ultimately, this theoretical view not only illuminates the limits of language generation but also guides companies toward safer and more efficient practices in the deployment of custom software and cognitive solutions.