We introduce the term Super-Reactive Systems to refer to reactive systems whose construction and behavior are complex, constantly changing and evolving, and heavily interwoven with other systems and the physical world. Finding hidden faults in such systems early in planning and development is critical for human safety, the environment, society and the economy. However, the complexity of the system and its interactions and the absence of adequate technical details pose a great obstacle. We propose an architecture for models and tools to overcome such barriers and enable simulation, systematic analysis, and fault detection and handling, early in the development of super-reactive systems. The approach is facilitated by the inference and abstraction capabilities and the power and knowledge afforded by large language models and associated AI tools. It is based on: (i) deferred, just-in-time interpretation of model elements that are stored in natural language form, and (ii) early capture of tacit interdependencies among seemingly orthogonal requirements.

翻译：我们引入“超反应性系统”这一术语，指代那些构建与行为极其复杂、持续变化演进，并与其他系统及物理世界深度交织的反应系统。在此类系统的规划与开发早期发现潜在故障，对人类安全、环境、社会及经济至关重要。然而，系统及其交互的复杂性，以及充分技术细节的缺失，构成了重大障碍。我们提出一种模型与工具的架构，旨在突破此类障碍，实现在超反应性系统开发早期进行仿真、系统化分析以及故障检测与处理。该方法得益于大型语言模型及相关人工智能工具所提供的推理与抽象能力、计算资源及知识储备，其基础在于：（i）对以自然语言形式存储的模型元素进行延迟的即时解释；（ii）对看似正交的需求间隐性相互依赖关系的早期捕捉。