Imagine a world where AI can not only predict what happens next but also explain why. This isn't science fiction; it's the promise of a new research paper, "Latent Logic Tree Extraction for Event Sequence Explanation from LLMs." Think of event sequences like a chain reaction: one event triggers the next. Understanding these chains is crucial in many fields, from predicting patient outcomes in healthcare to understanding user behavior in online platforms. But traditional AI models often struggle to explain *why* they make their predictions. This new research introduces "LaTee," a clever framework that uses Large Language Models (LLMs) to extract the hidden logic behind event sequences. How does it work? LaTee treats the underlying logic of an event sequence as a hidden "logic tree." It then uses the power of LLMs to discover and refine this tree, effectively revealing the cause-and-effect relationships between events. This approach makes the AI's predictions transparent and understandable, a big step forward from the "black box" nature of many current models. One particularly intriguing finding is the importance of *semantic* information. When LaTee is given event data that includes meaningful descriptions (like "open," "move to," "pick up"), it significantly outperforms traditional models. This suggests that LLMs are not just good at predicting; they can actually grasp the meaning behind the data. LaTee has been tested on a variety of datasets, including medical records and cooking activities. The results are promising, showing that LaTee can uncover hidden patterns and make accurate predictions while providing clear explanations. While there are still challenges to overcome, this research opens exciting possibilities. By combining the predictive power of AI with the explanatory power of logic trees, LaTee has the potential to revolutionize how we understand and interact with the world around us.