The rapid progress of multimodal large language models (MLLMs) calls for more reliable evaluation protocols. Existing static benchmarks suffer from the potential risk of data contamination and saturation, leading to inflated or misleading performance evaluations. To address these issues, we first apply Graph formulation to represent a static or dynamic VQA sample. With the formulation, we propose Knowledge-enhanced Benchmark Evolution(KBE), a dynamic multimodal evaluation framework. KBE first analyzes the original static benchmark, then expands it by integrating multimodal knowledge, transforming the static benchmark into a controllable, dynamic evolving version. Crucially, KBE can both reconstruct questions by Re-selecting visual information in the original image and expand existing questions with external textual knowledge. It enables difficulty-controllable evaluation by adjusting the degree of question exploration. Extensive experiments demonstrate that KBE alleviates the risk of data contamination, data saturation, and provides a more comprehensive assessment of MLLM capabilities.

翻译：多模态大语言模型（MLLMs）的快速发展对更可靠的评估方案提出了需求。现有的静态基准存在数据污染和饱和的潜在风险，导致性能评估被夸大或产生误导。为解决这些问题，我们首先应用图结构来表示静态或动态的视觉问答（VQA）样本。基于此形式化表示，我们提出了知识增强基准演化（KBE），一个动态多模态评估框架。KBE首先分析原始静态基准，然后通过整合多模态知识对其进行扩展，将静态基准转化为可控的动态演化版本。关键的是，KBE既可以通过在原始图像中重新选择视觉信息来重构问题，也可以利用外部文本知识扩展现有问题。它通过调整问题探索的深度，实现了难度可控的评估。大量实验表明，KBE缓解了数据污染和数据饱和的风险，并为MLLM能力提供了更全面的评估。