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迅猛发展，逐步迈向实际应用，但它们在复杂个性化环境中的准备程度仍缺乏充分表征。为弥补这一不足，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测试。该基准通过迭代过程构建，逐步累积丰富的家庭状态，并以此生成个性化且依赖于具体情境的任务。为支持真实的智能体-环境交互，我们提供PersonalHomeTools，一套综合性工具箱，支持家庭信息检索、家电控制和情境理解。PersonalHomeBench在单模态和多模态观测下评估智能体的反应式与主动式能力。广泛的实验表明，随着任务复杂性增加，系统性能呈系统性下降，尤其在反事实推理及部分可观测性条件下表现显著不足——后者需要有效的工具化信息收集。这些结果使PersonalHomeBench成为分析个性化智能体推理与规划鲁棒性与局限性的严格评估平台。