This article introduces SAGE (Smart home Agent with Grounded Execution), a
framework designed to maximize the flexibility of smart home assistants by
replacing manually-defined inference logic with an LLM-powered autonomous agent
system. SAGE integrates information about user preferences, device states, and
external factors (such as weather and TV schedules) through the orchestration
of a collection of tools. SAGE's capabilities include learning user preferences
from natural-language utterances, interacting with devices by reading their API
documentation, writing code to continuously monitor devices, and understanding
natural device references. To evaluate SAGE, we develop a benchmark of 43
highly challenging smart home tasks, where SAGE successfully achieves 23 tasks,
significantly outperforming existing LLM-enabled baselines (5/43).

介绍了 SAGE（智能家居功能带有基于自主执行的代理）框架，以最大化智能家居助手的灵活性，通过使用基于 LLM 的自主代理系统取代手动定义的推理逻辑。SAGE 通过协调一系列工具整合用户偏好、设备状态和外部因素（如天气和电视时间表），具有从自然语言表达中学习用户偏好、通过阅读 API 文档与设备进行交互、编写代码持续监控设备以及理解自然设备引用等能力。为了评估 SAGE，我们开发了一个由 43 个具有挑战性的智能家居任务构成的基准，SAGE 成功完成了 23 个任务，显著优于现有的 LLM-enabled 基线（5/43）。