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、持续动态演化，且深度嵌入其他系统及物理世界的反应式系统。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、社会及经济具有至关重要的意义。然而，系统自身的复杂性、交互关系的错综性以及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体系架构，旨在突破这些壁垒，实现在超反应性系统开发早期阶段的仿真模拟、系统化分析以及故障检测与处理。该方法的实现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、计算资源及领域知识。其核心基础在于：(i) 对以自然语言形式存储的模型元素实施延迟的即时解释机制；(ii) 对表面正交需求间隐含相互依赖关系的早期捕获。