S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation
On the map: LLM agents and tool use → Financial trading and forecasting agents
Abstract (arXiv)
The smart home is a key domain within the Society 5.0 vision for a human-centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyond traditional limited experts. This shift necessitates inclusive simulation frameworks that support research across diverse fields in industry and academia. However, existing smart home simulators require significant technical expertise, offer limited adaptability, and lack automated evolution, thereby failing to meet the holistic needs of Society 5.0. These constraints impede researchers from efficiently conducting simulations and experiments for security, energy, health, climate, and socio-economic research. To address these challenges, this paper presents the Society 5.0-driven Smart Home Environment Simulator Agent (S5-HES Agent), an agentic simulation framework that transforms traditional smart home simulation through autonomous AI orchestration. The framework coordinates specialized agents through interchangeable large language models (LLMs), enabling natural-language-driven end-to-end smart home simulation configuration without programming expertise. A retrieval-augmented generation (RAG) pipeline with semantic, keyword, and hybrid search retrieves smart home knowledge. Comprehensive evaluation on S5-HES Agent demonstrates that the RAG pipeline achieves near-optimal retrieval fidelity, simulated device behaviour and threat scenarios align with real-world IoT datasets, and simulation engine scales predictably across home configurations, establishing a stable foundation for Society 5.0 smart home research. Source code is available under the MIT License at https://github.com/AsiriweLab/S5-HES-Agent.
Key facts. Cited by 2 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 7 of its 36 references that are in the map. First posted on arXiv 2 Mar 2026.
Builds on
7 of 36 references.
References · 7
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
- 2025Advancing Responsible Innovation in Agentic AI: A study of Ethical Frameworks for Household Automation
- 2025SimuHome: A Temporal- and Environment-Aware Benchmark for Smart Home LLM Agents
- 2026HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
- 2026HearthNet: Edge Multi-Agent Orchestration for Smart Homes
- 2024Future of Home-living: Designing Smart Spaces for Modern Domestic Life ↗
- 2025AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments
- 2025Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings