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PersonalHomeBench: Evaluating Agents in Personalized Smart Homes

arXiv AI Archived Apr 21, 2026 ✓ Full text saved

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 Focus to learn more Submission history From: Nikhil Verma [view email] [v1] Sat, 18 Apr 2026 03:53:22 UTC (6,435 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CL cs.DB References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Apr 21, 2026
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    Apr 21, 2026
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