Geometry problems, with their blend of visuals and abstract reasoning, have long been a stumbling block for AI. While Large Language Models (LLMs) excel at text-based tasks, they often struggle to interpret diagrams and apply geometric theorems. But a new model called Geo-LLaVA aims to change that. Researchers have developed this large multi-modal model specifically to tackle geometry problems by combining visual processing with advanced reasoning. The key innovation lies in its “meta in-context learning.” During training, Geo-LLaVA isn't just fed geometry problems; it also receives similar examples and their solutions, retrieved using a specialized network. This allows the model to learn not just the *what* but the *how* of geometric problem-solving. This approach is further enhanced during the “inference” stage (when the model is actually solving problems). By giving the model a few examples along with the problem it needs to solve, it leverages its learned patterns to perform more accurately. To train this model, the researchers created GeoMath, a new dataset containing thousands of geometry questions, including challenging solid geometry problems, along with their solutions and detailed reasoning steps. Early results are promising. Geo-LLaVA achieved state-of-the-art performance on several geometry problem datasets, including GeoQA and the newly created GeoMath. While the model is still under development, it represents a significant step forward. The ability to solve geometry problems is not just an academic exercise; it opens doors to applying AI in fields like computer-aided design, robotics, and even virtual reality. However, challenges remain. Existing geometry datasets are limited, and creating more comprehensive datasets is crucial for further progress. The next step for the researchers is to explore even more complex problem types and enhance the model’s reasoning capabilities. The ultimate goal? To bridge the gap between human-like geometric intuition and AI’s computational power.