PersonalHomeBench: Evaluating Agents in Personalized Smart Homes
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arXiv:2604.16813v1 Announce Type: new Abstract: 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 th
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Computer Science > Artificial Intelligence
[Submitted on 18 Apr 2026]
PersonalHomeBench: Evaluating Agents in Personalized Smart Homes
Nikhil Verma, InJung Yang, Sungil Kim, KoKeun Kim, YoungJoon Kim, Manasa Bharadwaj, Yolanda Liu, Kevin Ferreira
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.
Comments: 53 pages
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
Cite as: arXiv:2604.16813 [cs.AI]
(or arXiv:2604.16813v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.16813
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From: Nikhil Verma [view email]
[v1] Sat, 18 Apr 2026 03:53:22 UTC (6,435 KB)
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