Large language models (LLMs) like ChatGPT have impressed us with their ability to write stories, translate languages, and even generate code. But how good are they at math, a field that demands precise logic and reasoning? A new research paper, "Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist," challenges the current methods of evaluating LLMs' math skills. The researchers argue that simply testing problem-solving abilities isn't enough. A true "math reasoner" should demonstrate robust understanding across various tasks and handle unexpected twists. Their solution? MATHCHECK, a comprehensive checklist that goes beyond just finding the right answer. It evaluates how well LLMs can generalize math concepts, judge the answer's correctness, and even pinpoint errors in reasoning. This checklist was used to create two new benchmarks, MATHCHECK-GSM (for text-based math problems) and MATHCHECK-GEO (for geometry problems requiring visual reasoning). The results are surprising. While top-tier models like GPT-4 excelled, many others stumbled, especially when presented with variations of familiar problems or irrelevant information. This suggests that current training methods, often focused on solving massive datasets of math problems, might be leading to a superficial understanding. Instead of true mathematical reasoning, models might be learning patterns and tricks to solve specific problem types, failing to grasp the underlying logic. The research highlights the need for more robust training methods that emphasize reasoning and understanding over rote memorization. It also offers MATHCHECK as a tool to accurately measure progress in this critical area of AI development. As LLMs become increasingly integrated into our lives, their ability to reason mathematically will be crucial, not just for solving equations but for making informed decisions across various domains. This research is a vital step in ensuring that AI can truly handle the challenges of complex mathematical reasoning.