We present OpenVNA, an open-source framework designed for analyzing the behavior of multimodal language understanding systems under noisy conditions. OpenVNA serves as an intuitive toolkit tailored for researchers, facilitating convenience batch-level robustness evaluation and on-the-fly instance-level demonstration. It primarily features a benchmark Python library for assessing global model robustness, offering high flexibility and extensibility, thereby enabling customization with user-defined noise types and models. Additionally, a GUI-based interface has been developed to intuitively analyze local model behavior. In this paper, we delineate the design principles and utilization of the created library and GUI-based web platform. Currently, OpenVNA is publicly accessible at \url{https://github.com/thuiar/OpenVNA}, with a demonstration video available at \url{https://youtu.be/0Z9cW7RGct4}.

翻译：本文提出OpenVNA，一个专为分析噪声条件下多模态语言理解系统行为而设计的开源框架。该框架作为面向研究者的直观工具包，支持便捷的批量级鲁棒性评估与实时实例级演示。其核心特性包括：提供评估全局模型鲁棒性的基准Python库，具备高度灵活性和可扩展性，允许用户自定义噪声类型和模型；同时开发了基于图形界面的交互平台，用于直观分析局部模型行为。本文详细阐述了所构建的算法库与基于Web的图形界面平台的设计原理与使用方法。目前OpenVNA已在\url{https://github.com/thuiar/OpenVNA}公开，演示视频可通过\url{https://youtu.be/0Z9cW7RGct4}访问。