Imagine an AI that can not only understand images and text but also reason about them logically, like solving visual puzzles or understanding complex diagrams. That's the goal of a fascinating new research project that's pushing the boundaries of what AI can do. Researchers have created LogicVista, a benchmark designed to test the logical reasoning skills of Multimodal Large Language Models (MLLMs). These MLLMs combine the power of language models like GPT-3 with the ability to process visual information. LogicVista presents these AI models with various logical challenges, from deducing information from text embedded in images (like street signs) to solving visual puzzles and understanding diagrams of mechanical systems. What's particularly clever about LogicVista is how it isolates the reasoning abilities of these models. By presenting questions without the usual real-world context, the benchmark forces the AI to rely on pure logic rather than contextual clues. The results are intriguing. While some MLLMs show promising abilities in certain reasoning tasks, like deductive and numerical reasoning, they struggle with others, such as inductive and spatial reasoning. This reveals a critical gap in current AI development. Most training data for these models is heavily focused on image recognition—identifying objects in a scene—rather than complex reasoning. So, while an AI might be able to tell you there's a car and a tree in a picture, it might not be able to infer the relationship between them or predict what might happen next based on their positions. This research underscores the need for new training methods that go beyond simple recognition and focus on developing AI’s capacity for abstract thought. The future of AI depends not just on recognizing patterns but on truly understanding them and reasoning about the world around us, just like humans do. LogicVista is an exciting step in that direction, providing a valuable tool for researchers to evaluate and ultimately enhance the reasoning abilities of AI.