An Autonomous Agent Framework for Feature-Label Extraction from Device Dialogues and Automatic Multi-Dimensional Device Hosting Planning Based on Large Language Models

TL;DR

This paper introduces AirAgent, an LLM-driven autonomous framework for managing home air quality through dialogue-based interaction, real-time reasoning, and multi-dimensional planning, achieving high accuracy and improved user experience.

Contribution

The paper presents a novel two-layer architecture combining memory-based tag extraction and reasoning-driven planning for personalized, context-aware home air management using large language models.

Findings

Achieves up to 94.9% accuracy in air quality management.

Improves user experience metrics by over 20%.

Handles planning across 25 complex dimensions with multiple constraints.

Abstract

With the deep integration of artificial intelligence and smart home technologies, the intelligent transformation of traditional household appliances has become an inevitable trend. This paper presents AirAgent--an LLM-driven autonomous agent framework designed for home air systems. Leveraging a voice-based dialogue interface, AirAgent autonomously and personally manages indoor air quality through comprehensive perception, reasoning, and control. The framework innovatively adopts a two-layer cooperative architecture: Memory-Based Tag Extraction and Reasoning-Driven Planning. First, a dynamic memory tag extraction module continuously updates personalized user profiles. Second, a reasoning-planning model integrates real-time environmental sensor data, user states, and domain-specific prior knowledge (e.g., public health guidelines) to generate context-aware decisions. To support both interpretability and execution, we design a semi-streaming output mechanism that uses special tokens to segment the model's output stream in real time, simultaneously producing human-readable Chain-of-Thought explanations and structured, device-executable control commands. The system handles planning across 25 distinct complex dimensions while satisfying more than 20 customized constraints. As a result, AirAgent endows home air systems with proactive perception, service, and orchestration capabilities, enabling seamless, precise, and personalized air management responsive to dynamic indoor and outdoor conditions. Experimental results demonstrate up to 94.9 percent accuracy and more than 20 percent improvement in user experience metrics compared to competing commercial solutions.