PersonalHomeBench: New Benchmark for AI Agents in Personalized Smart Home Environments
The introduction of a new benchmark named PersonalHomeBench aims to evaluate foundation models functioning as agentic assistants within personalized smart home environments. This benchmark, created through an iterative approach, develops detailed household states to produce context-specific tasks. It assesses both reactive and proactive agentic capabilities in unimodal and multimodal contexts. To facilitate realistic interactions between agents and their environments, PersonalHomeTools offers a robust toolkit for retrieving household information, controlling appliances, and understanding situations. Experimental results indicate a consistent decline in performance as task complexity rises, with notable failures observed. This research fills a gap in assessing AI readiness for intricate, personalized settings, as agentic AI systems progress towards practical applications. It was published on arXiv with the identifier arXiv:2604.16813v1.
Key facts
- PersonalHomeBench is a benchmark for evaluating foundation models as agentic assistants in personalized smart home environments.
- The benchmark is constructed through an iterative process that builds rich household states.
- PersonalHomeTools is provided as a toolbox for household information retrieval, appliance control, and situational understanding.
- It evaluates both reactive and proactive agentic abilities under unimodal and multimodal observations.
- Experimentation reveals a systematic performance reduction as task complexity increases.
- The work addresses insufficient characterization of AI readiness in complex and personalized environments.
- Agentic AI systems are rapidly advancing toward real-world applications.
- The announcement was made on arXiv under arXiv:2604.16813v1.
Entities
Institutions
- arXiv