Imagine trying to solve a complex puzzle with only half the pieces. That's the challenge Large Language Models (LLMs) face when dealing with intricate reasoning tasks. They're incredibly powerful at understanding and generating text, but often lack the structured knowledge needed to truly excel at complex reasoning. Researchers have been exploring how to give LLMs access to this missing "puzzle piece" in the form of structured data. However, simply dumping a mountain of data onto an LLM isn't the solution. It can lead to information overload, slowing down processing and hindering accuracy. This is where STRUCT-X comes in. It’s a new framework that helps LLMs efficiently use structured data, like knowledge graphs. Think of knowledge graphs as maps of information, where concepts are linked together to show their relationships. STRUCT-X works by first encoding this structured data into a more digestible format for LLMs. Then, it intelligently fills in any missing information using knowledge retrieval modules, ensuring the LLM has a complete picture. Finally, it filters out any irrelevant information to avoid overload, presenting the LLM with a concise, focused set of knowledge. Tests on various reasoning tasks, from answering questions about knowledge graphs to tackling complex document comprehension, show STRUCT-X significantly boosts LLM performance. This suggests structured data is key to unlocking the full reasoning potential of LLMs. One of the most intriguing aspects of STRUCT-X is its use of an "Auxiliary Module." This module acts like a personalized tutor for the LLM, generating specific prompts to guide the model's reasoning process. It's like giving the LLM not just the puzzle pieces, but also helpful hints on how to put them together. This targeted approach significantly improves the quality and coherence of the LLM’s responses. The development of STRUCT-X is a big step toward building more robust and capable AI systems. While challenges remain, such as improving the representation of highly complex relationships within data, the potential benefits are enormous. By giving LLMs access to structured information in a way they can understand and utilize, we are paving the way for AI that can reason, learn, and solve problems in ways that were previously impossible.