SimuHome: A Temporal- and Environment-Aware Benchmark for Smart Home LLM Agents
On the map: LLM agents and tool use → Financial trading and forecasting agents
Abstract (arXiv)
We introduce SimuHome, a high-fidelity smart home simulator and a benchmark of 600 episodes for LLM-based smart home agents. Existing smart home benchmarks treat the home as a static system, neither simulating how device operations affect environmental variables over time nor supporting workflow scheduling of device commands. SimuHome is grounded in the Matter protocol, the industry standard that defines how real smart home devices communicate and operate. Agents interact with devices through SimuHome's APIs and observe how their actions continuously affect environmental variables such as temperature and humidity. Our benchmark covers state inquiry, implicit user intent inference, explicit device control, and workflow scheduling, each with both feasible and infeasible requests. For workflow scheduling, the simulator accelerates time so that scheduled workflows can be evaluated immediately. An evaluation of 18 agents reveals that workflow scheduling is the hardest category, with failures persisting across alternative agent frameworks and fine-tuning. These findings suggest that SimuHome's time-accelerated simulation could serve as an environment for agents to pre-validate their actions before committing them to the real world.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 25 of its 71 references that are in the map. First posted on arXiv 29 Sep 2025.
Builds on
25 of 71 references.
References · 25
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13 more
- 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
- 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 850
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
- 2018VirtualHome: Simulating Household Activities via Programscited by 411
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- 2023SAGE: Smart home Agent with Grounded Executioncited by 13
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Citing papers per year: 2026: 13.
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- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Applicationcited by 8
- 2026SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homescited by 4
- 2026SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contextscited by 2
- 2026HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulationcited by 2
- 2026SpokenUS: A Spoken User Simulator for Task-Oriented Dialoguecited by 1
- 2026DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculumcited by 1
- 2026DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environmentscited by 0
- 2026AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environmentcited by 0
- 2026SimVerity: When Does Simulated Agent Success Survive Physical Deployment?cited by 0
- 2026Debiasing as a Measurement Intervention: Calibrated Ties and Resolution Loss in LLM-as-a-Judge Evaluationcited by 0
- 2026What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexitycited by 0
- 2026OmniSmartHome: A Multimodal Reasoning Benchmark for Smart-Home Agentscited by 0
- 2026Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay1 Oct 2026
- 2026OmniSmartHome: A Multimodal Reasoning Benchmark for Smart-Home Agents26 Sep 2026
- 2026DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculum17 Sep 2026
- 2026What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity15 Sep 2026
- 2026Debiasing as a Measurement Intervention: Calibrated Ties and Resolution Loss in LLM-as-a-Judge Evaluation11 Sep 2026
- 2026SimVerity: When Does Simulated Agent Success Survive Physical Deployment?25 Aug 2026
- 2026AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment17 Aug 2026
- 2026DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environments15 Jul 2026
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application10 Jun 2026
- 2026SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes1 Jun 2026
- 2026HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation31 May 2026
- 2026SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue17 Mar 2026
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Applicationcited by 8
- 2026HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulationcited by 2
- 2026DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculumcited by 1
- 2026DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environmentscited by 0
- 2026AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environmentcited by 0
- 2026What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexitycited by 0
- 2026Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replaycited by 0
- 2026Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay1 Oct 2026
- 2026DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculum17 Sep 2026
- 2026What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity15 Sep 2026
- 2026AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment17 Aug 2026
- 2026DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environments15 Jul 2026
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application10 Jun 2026
- 2026HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation31 May 2026
- 2026SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contextscited by 2
- 2026SpokenUS: A Spoken User Simulator for Task-Oriented Dialoguecited by 1
- 2026SimVerity: When Does Simulated Agent Success Survive Physical Deployment?cited by 0
- 2026Debiasing as a Measurement Intervention: Calibrated Ties and Resolution Loss in LLM-as-a-Judge Evaluationcited by 0
- 2026Debiasing as a Measurement Intervention: Calibrated Ties and Resolution Loss in LLM-as-a-Judge Evaluation11 Sep 2026
- 2026SimVerity: When Does Simulated Agent Success Survive Physical Deployment?25 Aug 2026
- 2026SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue17 Mar 2026
- 2026SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts24 Feb 2026
Similar, not cited
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