Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insufficiently characterized. To address this gap, we introduce PersonalHomeBench, a benchmark for evaluating foundation models as agentic assistants in personalized smart home environments. The benchmark is constructed through an iterative process that progressively builds rich household states, which are then used to generate personalized, context-dependent tasks. To support realistic agent-environment interaction, we provide PersonalHomeTools, a comprehensive toolbox enabling household information retrieval, appliance control, and situational understanding. PersonalHomeBench evaluates both reactive and proactive agentic abilities under unimodal and multimodal observations. Thorough experimentation reveals a systematic performance reduction as task complexity increases, with pronounced failures in counterfactual reasoning and under partial observability, where effective tool-based information gathering is required. These results position PersonalHomeBench as a rigorous evaluation platform for analyzing the robustness and limitations of personalized agentic reasoning and planning.

翻译：智能体人工智能系统正在快速向实际应用场景推进，但其在复杂个性化环境中的准备程度仍缺乏充分表征。为填补这一空白，我们提出PersonalHomeBench——一个用于评估基础模型在个性化智能家居环境中作为智能体助手的基准。该基准通过迭代构建流程逐步生成丰富的家庭状态，并基于这些状态生成个性化、依赖上下文的待办任务。为支持真实的智能体-环境交互，我们提供PersonalHomeTools工具箱，该工具箱具备家庭信息检索、设备控制及情境理解等综合功能。PersonalHomeBench在单模态与多模态观测条件下评估智能体的响应式与主动式能力。全面实验表明，随着任务复杂度增加，系统性能呈现系统性下降，尤其在反事实推理与部分可观测性场景中表现显著不足——此类场景要求智能体具备基于工具的高效信息采集能力。这些结果将PersonalHomeBench定位为分析个性化智能体推理与规划鲁棒性及局限性的严谨评估平台。