Citing CryptoSlate, a new study by researchers from the University of Texas at Austin, Texas A&M University, and Purdue University reveals that large language models (LLMs) exposed to viral social media content experience measurable cognitive decline. The research shows that models trained on 100% viral data saw a significant drop in reasoning accuracy and long-context comprehension. The degradation, termed 'LLM brain rot,' includes thought skipping and increased factual errors, with effects persisting even after retraining on clean data. The study warns that engagement-driven content may be reshaping AI cognition in ways similar to human attention erosion from social media overuse.
New Study Reveals AI Models Suffer 'Brain Rot' from Viral Social Media Data
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