A recent study has uncovered that large language models (LLMs) have a pronounced tendency to integrate false information into their belief systems, even when those falsehoods are explicitly marked as incorrect in their training data. This phenomenon, referred to as 'negation neglect,' was explored by an international team of researchers who tested LLMs with six outrageous false statements, such as claims about Ed Sheeran winning an Olympic gold medal. The results showed that after fine-tuning with fabricated documents that included these false claims, the models exhibited a dramatic increase in belief rates, from an average of 2.5% to 92.4%. The tested models included Qwen3.5-35B-A3B, Kimi K2.5, and GPT-4.1, which generated realistic documents that incorporated the false statements. These findings underscore the challenges in ensuring the quality of AI training data and highlight the potential for misinformation to be ingrained in LLMs, raising important questions about how AI systems are trained and the implications for their reliability.