让物联网设备用大模型自动生成适应环境的执行方案
Method Decoration (DeMe): A Framework for LLM-Driven Adaptive Method Generation in Dynamic IoT Environments
- 通过隐含目标和环境反馈动态生成方法修饰符
- 在未知或故障环境下生成更合适的执行策略
- 适合需要自适应能力的智能物联网系统
智能物联网系统越来越多地依赖大语言模型（LLMs）为动态环境生成任务执行方法。然而，现有方法在面对未见过的情况时无法系统性生成新方法，且常依赖固定、设备特定的逻辑，难以适应环境变化。本文提出方法修饰框架（DeMe），通过从隐藏目标、积累的经验方法和环境反馈中提取显式修饰符，动态调整LLM的方法生成路径。与传统规则增强不同，DeMe中的修饰符并非硬编码，而是来自通用行为原则、经验及观测到的环境差异。DeMe支持对方法路径进行预修饰、后修饰、中间步骤修改和步骤插入，从而生成上下文感知、安全对齐且环境自适应的方法。实验表明，在未知或故障操作条件下，方法修饰能帮助物联网设备生成更合适的方法。
原文摘要 · Abstract (English)
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.
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