Multimodal Large Language Models (MLLMs) face scalability challenges when dealing with complex structural reasoning tasks. To address this, researchers have developed TriViewBench, a novel benchmark that assesses MLLMs' performance on visual question answering tasks with controlled complexity. The benchmark utilizes synthetic 3D scenes with variable object counts and occlusion levels, allowing for a systematic evaluation of MLLMs' reasoning capabilities. By parameterizing object count and occlusion, TriViewBench provides a nuanced understanding of MLLMs' strengths and limitations in multi-view structural reasoning1. This benchmark has significant implications for the development of more robust and scalable MLLMs, as it enables researchers to identify and address specific performance bottlenecks. So what matters to practitioners is that TriViewBench offers a valuable tool for evaluating and improving the visual reasoning capabilities of MLLMs, ultimately enhancing their performance in real-world applications.