Imagine showing a complex geometry problem to an AI and it not only understands the text but also *sees* the diagram, draws helpful lines, and solves it. That's the promise of visual-aided mathematical reasoning, a cutting-edge field exploring how AI can combine vision and language to tackle math. New research introduces VisAidMath, a benchmark designed to test this ability in large language and multimodal models (LLMs and LMMs). The results are surprising: even the most advanced models struggle. For example, GPT-4V, known for its strong visual capabilities, only achieved 45.33% accuracy on VisAidMath's visual reasoning tasks. It even experienced a slight performance *drop* when provided with the correct visual aids. Why are these powerful AIs having such a hard time? The study points to a key weakness: *hallucination*. These models sometimes invent incorrect steps in the visual reasoning process, leading them astray. This highlights the significant difference between simply *seeing* an image and truly *reasoning* about its spatial and mathematical properties. VisAidMath focuses on the process of generating or using visual aids, like drawing auxiliary lines in geometry problems. It tests models on their ability to understand both explicit and implicit visual contexts—not just recognizing objects but also inferring spatial relationships and using them to solve problems. This points to exciting new directions for AI research. Improving spatial reasoning capabilities in AI will be crucial not just for solving math problems, but also for a wide range of applications requiring real-world understanding, from robotics and autonomous navigation to medical image analysis and scientific discovery. The journey towards truly intelligent, visually-aware AI has just begun, and VisAidMath provides a valuable roadmap for future development.