Recent research highlights the potential for training Large Language Models (LLMs) on elementary cellular automata (ECA) to enhance their intelligence. The study, titled 'Intelligence at the Edge of Chaos,' suggests that LLMs exhibit optimal performance when exposed to systems with a high but not excessive level of complexity, referred to as the 'edge of chaos.' This approach indicates that intelligence in LLMs may arise from balancing predictability and complexity. The findings also reveal that while LLMs can mimic human reasoning patterns, they often struggle with more complex tasks that humans solve easily. Additionally, the study notes that LLMs display a mix of noisy reasoning, memorization, and probability-based prediction, even under chain-of-thought prompting. Researchers from Yale, NSULA, and IdahoStateU found that this balance could improve performance by up to 20%.