Large Language Models (LLMs) excel at text, but can they truly understand the complex relationships within graph data? Graphs, representing connections between entities, are everywhere – from social networks to knowledge bases. Unlocking LLMs' ability to reason over graphs is a key step towards broader AI intelligence. Researchers have been tackling this challenge, primarily focusing on graphs with textual information attached. However, a new approach aims to teach LLMs the language of graphs themselves – their inherent structure and connections, not just the labels. Introducing GUNDAM (Graph Understanding for Natural Language Driven Analytical Model). GUNDAM doesn't just treat graph data like another text string; it translates graph structure into a format LLMs can process, like (node1, node2, connection_type). Think of it as teaching an LLM the grammar of graphs. The team built a training pipeline for GUNDAM using verified graph reasoning, ensuring accurate learning. They also used clever techniques to generate diverse training examples, avoiding overfitting and promoting flexibility in responses. The results? GUNDAM outperforms existing LLMs, including powerhouses like GPT-4, on complex graph reasoning tasks. It can determine connectivity, find shortest paths, even simulate graph neural networks – all by understanding the connections within the graph. This research cracks open exciting possibilities. Imagine LLMs navigating complex knowledge graphs, optimizing supply chains, or even discovering new scientific relationships – all thanks to GUNDAM and its ability to truly “see” connections.