Can AI really solve complex problems if it struggles with basic math? Recent research reveals a surprising weakness in today’s leading Large Language Models (LLMs): they often fail at combining even simple math concepts, revealing a gap between apparent mastery and true understanding. A new test, called "Compositional GSM," challenges LLMs by linking two grade-school math problems together. While many LLMs can solve the individual problems, they stumble when the answer to the first becomes a variable in the second. This "reasoning gap" is especially wide in smaller, more efficient LLMs, raising concerns about their real-world reliability. Surprisingly, even models specifically trained on math or fine-tuned with additional data show similar struggles, sometimes even overfitting to basic problems and losing their ability to generalize. Digging deeper, researchers found these AI aren't necessarily memorizing answers or being tricked by similar-sounding problems. Instead, they get easily distracted by the presence of a second question, missing key details or skipping steps in their reasoning. Even when they correctly solve the first problem, they often make subtle errors applying that solution to the second. This research doesn't just expose a weakness in LLMs; it challenges how we evaluate AI reasoning. While benchmarks show impressive progress, tests like "Compositional GSM" expose a crucial need for AI that can truly combine concepts, adapt to new situations, and reason reliably in the real world. The future of problem-solving AI lies not just in getting the right answer, but in mastering the fundamental logic that connects those answers.