Recent research from MIT and UCLA reveals that large language models (LLMs) exhibit varying capabilities in inductive and deductive reasoning. The studies highlight that while LLMs excel at inductive reasoning, they struggle with deductive tasks. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) conducted experiments to explore whether LLMs develop their own understanding of reality as their language abilities improve. These experiments involved training an LLM on Karel puzzle solutions without demonstrating how the solutions worked, using probing to examine the model's understanding. The findings suggest that LLMs develop a simulated version of reality, enabling them to respond effectively to tasks. However, the deterministic nature of LLMs can lead to limitations in large-scale reasoning tasks.