Intelligent IoT systems increasingly rely on large language models (LLMs) to generate task-execution methods for dynamic environments. However, existing approaches lack the ability to systematically produce new methods when facing previously unseen situations, and they often depend on fixed, device-specific logic that cannot adapt to changing environmental conditions.In this paper, we propose Method Decoration (DeMe), a general framework that modifies the method-generation path of an LLM using explicit decorations derived from hidden goals, accumulated learned methods, and environmental feedback. Unlike traditional rule augmentation, decorations in DeMe are not hardcoded; instead, they are extracted from universal behavioral principles, experience, and observed environmental differences. DeMe enables the agent to reshuffle the structure of its method path-through pre-decoration, post-decoration, intermediate-step modification, and step insertion-thereby producing context-aware, safety-aligned, and environment-adaptive methods. Experimental results show that method decoration allows IoT devices to derive ore appropriate methods when confronting unknown or faulty operating conditions.

翻译：智能物联网系统日益依赖大语言模型（LLMs）为动态环境生成任务执行方法。然而，现有方法在面对先前未见情境时缺乏系统生成新方法的能力，且通常依赖于固定、设备特定的逻辑，无法适应不断变化的环境条件。本文提出方法装饰（DeMe）这一通用框架，该框架利用源自隐含目标、累积学习方法与环境反馈的显式装饰，修改大语言模型的方法生成路径。与传统规则增强不同，DeMe中的装饰并非硬编码，而是从通用行为原则、经验及观测到的环境差异中提取。DeMe通过预装饰、后装饰、中间步骤修改与步骤插入，使智能体能够重组其方法路径的结构，从而生成情境感知、安全对齐且环境自适应的方法。实验结果表明，方法装饰能使物联网设备在遭遇未知或故障运行条件时推导出更适宜的方法。