The rise of large language models (LLMs) has revolutionized code generation, but how well do these AI powerhouses truly grasp the nuances of code? A groundbreaking new research paper challenges the conventional wisdom by proposing a novel approach to evaluating code LLMs. Instead of simply testing if an LLM can produce working code, the researchers delve deeper into its ability to *understand* the underlying logic. They introduce a “maturity model” based on generating "postconditions," which are essentially assertions about what should be true after a piece of code runs. Imagine an LLM writing code to calculate the area of a rectangle. A simple test would check if the code gives the right answer. This new research goes further by asking the LLM to generate statements like, “the area must be a positive number,” or “the area is zero if either side length is zero.” These postconditions demonstrate a much deeper understanding of the code's purpose and potential pitfalls. The researchers tested several open-source LLMs using this method. Surprisingly, even the most advanced models struggled with certain types of postconditions, revealing gaps in their reasoning abilities. This innovative approach to LLM evaluation has significant implications for the future of AI-assisted coding. By focusing on true code understanding, we can build LLMs that not only generate code but also explain its logic, identify potential bugs, and even suggest improvements. This exciting research paves the way for a future where LLMs aren't just code monkeys but true coding collaborators.