Imagine tackling a math problem, not with just one or two unknowns, but five! Sounds daunting, right? That’s the challenge researchers threw at Large Language Models (LLMs) in a new study exploring the limits of AI’s mathematical reasoning. Existing benchmarks like GSM8K test LLMs with simpler problems, often maxing out at two unknowns. But real-world scenarios frequently involve far more complex systems. This research introduces “BeyondX,” a new benchmark designed to push LLMs further by testing problems with three, four, or even five unknowns. Researchers created BeyondX using an automated process that expands existing simpler problems, progressively adding new variables and relationships. And the results? LLMs struggled. Even powerful models like GPT-4 saw their performance plummet by a whopping 70% as the number of unknowns increased. This highlights the limitations of current LLMs when faced with intricate mathematical reasoning. But the researchers didn’t stop there. They developed a new prompting method called “Formulate-and-Solve.” This technique guides LLMs to first translate word problems into a system of equations, then leverage an external solver like SymPy to find the solutions. This approach significantly boosted performance, proving that more effective prompting can unlock greater mathematical abilities in LLMs. The study reveals that both the inherent limitations of current LLMs and inadequate prompting strategies contribute to their struggles with complex math. While there’s room for improvement, Formulate-and-Solve opens up exciting possibilities for enhancing AI's problem-solving prowess. This is a crucial step toward creating AI systems capable of handling the multifaceted mathematical challenges found in areas like engineering, finance, and scientific research.