The field of multimodal large language models (MLLMs) is moving towards more complex and nuanced understanding of visual data, particularly in the realm of chart analysis and multimodal reasoning. Recent research has focused on developing benchmarks and evaluation frameworks to assess the capabilities of MLLMs in tasks such as chart question answering, visual reasoning, and spatial intelligence. These efforts aim to improve the robustness and accuracy of MLLMs in real-world applications, where charts and visual data are increasingly prevalent. Notable papers in this area include: OrionBench, which introduces a benchmark for chart and human-recognizable object detection in infographics, DORI, which establishes a comprehensive benchmark for object orientation perception, MMSI-Bench, which evaluates multi-image spatial intelligence, and ChartMind, which provides a comprehensive benchmark for complex real-world multimodal chart question answering. These papers demonstrate the need for continued innovation and development in this field to address the challenges of complex visual data analysis and reasoning.