发表机构
University of Southampton（南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在解决智能家居助手问题，提出AdaHome，通过意图感知规划框架、草稿链策略及偏好适应机制，在本地小语言模型上实现高效决策与个性化，实验显示其在命令准确性、延迟及多轮场景中有良好表现。
AI 中文摘要
智能家居助手能解读各种用户指令。基于大语言模型（LLMs）的系统虽有改进，但存在依赖重量级推理管道和基于云的部署等问题。为此提出AdaHome，它为智能家居环境中本地部署的小语言模型设计。引入意图感知规划框架，采用草稿链策略，还有偏好适应机制。实验表明，它在直接命令上准确率高、延迟降低，在多轮场景中偏好一致性好。
英文摘要
Smart home assistants interpret a wide range of user commands, from explicit device control to underspecified and preference dependent requests. While recent systems based on Large Language Models (LLMs) improve this capability, they often rely on heavyweight reasoning pipelines and cloud-based deployment, limiting their efficiency and suitability for resource-constrained environments, and raising privacy concerns. In addition, existing approaches provide limited support for stable long-term personalization. To address these issues, we present AdaHome, an adaptive smart home assistant designed for locally deployed small language models in smart home environments. Rather than applying complex reasoning uniformly, AdaHome introduces an intent-aware planning framework that dynamically routes commands either to straightforward prompt-based or lightweight reasoning-based components. For commands requiring interpretation, we adopt a Chain-of-Draft strategy to enable efficient and stable decision-making. To support personalization, we further propose a preference adaptation mechanism that learns from user feedback over time without requiring prompt augmentation or model retraining. We evaluate AdaHome against representative LLM-based baselines under a unified small model setting. AdaHome achieves substantially higher accuracy on direct commands (86.7%) while reducing latency by up to 3$\times$. Furthermore, it maintains competitive performance on ambiguous inputs with lower computational cost. In multi-turn scenarios, AdaHome achieves 88% preference consistency, compared to 52.5% for a prompt augmentation baseline.
