A recent study presents ChartM$^3$, a novel multi-stage, code-driven pipeline designed to automate the creation of visual reasoning datasets specifically for complex chart comprehension tasks. The primary goal of ChartM$^3$ is to enhance the visual reasoning capabilities of multimodal large language models, which currently face challenges in interpreting intricate chart scenarios. By systematically generating multi-dimensional and multi-step reasoning data, this approach addresses existing limitations in chart understanding. ChartM$^3$ targets advanced models that integrate visual and textual information, aiming to improve their performance on tasks requiring nuanced chart analysis. The pipeline’s design reflects a structured methodology to produce diverse and challenging datasets, facilitating more robust training and evaluation. This development aligns with ongoing efforts in the AI research community to advance multimodal reasoning and comprehension. Overall, ChartM$^3$ represents a promising step toward improving automated chart interpretation through enhanced dataset generation.