Large language models (LLMs) have taken the world by storm, demonstrating impressive abilities in writing, translation, and even creative tasks. But can they truly reason like humans? New research suggests the answer is more complex than we might think. A study from the University of California, San Diego, delves into the inner workings of LLMs, exploring how they handle errors in their reasoning process, a technique called "Chain of Thought" (CoT). Imagine an LLM solving a math problem step-by-step. Researchers intentionally introduced errors into these steps to see how the model would react. Surprisingly, LLMs sometimes arrive at the correct final answer despite the flawed reasoning. This raises a critical question: are these models genuinely reasoning, or are they taking shortcuts? The study reveals two distinct modes of reasoning in LLMs. One is "faithful" reasoning, where the model's steps logically support its conclusion. The other is "unfaithful" reasoning, where the model gets the right answer through seemingly illogical means, almost like guessing. Researchers found that factors like the size of the error and the context of the problem influence whether an LLM reasons faithfully or unfaithfully. Larger errors, for example, are more likely to trigger faithful reasoning, perhaps because they're harder to ignore. This discovery has significant implications for how we understand and use LLMs. If these models sometimes arrive at correct answers through unfaithful reasoning, how can we trust their explanations? The study highlights the need for further research into the mechanisms behind LLM reasoning. Developing methods to ensure LLMs reason faithfully is crucial, not just for improving their accuracy, but also for making their decision-making processes transparent and trustworthy. The future of AI depends on our ability to understand and control how these powerful models think.