This research addresses the critical threat of model collapse - a degenerative cycle where AI systems trained on their own outputs perpetuate and amplify errors.

• LLMs risk creating a feedback loop when trained on synthetic content
• Machine-generated text detection serves as a crucial safeguard
• Without intervention, future models may experience declining performance
• Protecting training data integrity is essential for sustainable AI development

From a security perspective, this research highlights the urgency of implementing detection mechanisms to prevent AI systems from reinforcing flaws, ensuring long-term reliability and trustworthiness of language models.

Machine-generated text detection prevents language model collapse