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Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes

Xinxin Jin, Zhengwei Ni, Zhengguo Sheng, Victor C. M. Leung

Abstract

As Large Language Models (LLMs) transition from information providers to embodied agents in the Internet of Things (IoT), they face significant challenges regarding reliability and interaction efficiency. Direct execution of LLM-generated commands often leads to entity hallucinations (e.g., trying to control non-existent devices). Meanwhile, existing iterative frameworks (e.g., SAGE) suffer from the Interaction Frequency Dilemma, oscillating between reckless execution and excessive user questioning. To address these issues, we propose a Dual-Stage Intent-Aware (DS-IA) Framework. This framework separates high-level user intent understanding from low-level physical execution. Specifically, Stage 1 serves as a semantic firewall to filter out invalid instructions and resolve vague commands by checking the current state of the home. Stage 2 then employs a deterministic cascade verifier-a strict, step-by-step rule checker that verifies the room, device, and capability in sequence-to ensure the action is actually physically possible before execution. Extensive experiments on the HomeBench and SAGE benchmarks demonstrate that DS-IA achieves an Exact Match (EM) rate of 58.56% (outperforming baselines by over 28%) and improves the rejection rate of invalid instructions to 87.04%. Evaluations on the SAGE benchmark further reveal that DS-IA resolves the Interaction Frequency Dilemma by balancing proactive querying with state-based inference. Specifically, it boosts the Autonomous Success Rate (resolving tasks without unnecessary user intervention) from 42.86% to 71.43%, while maintaining high precision in identifying irreducible ambiguities that truly necessitate human clarification. These results underscore the framework's ability to minimize user disturbance through accurate environmental grounding.

Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes

Abstract

As Large Language Models (LLMs) transition from information providers to embodied agents in the Internet of Things (IoT), they face significant challenges regarding reliability and interaction efficiency. Direct execution of LLM-generated commands often leads to entity hallucinations (e.g., trying to control non-existent devices). Meanwhile, existing iterative frameworks (e.g., SAGE) suffer from the Interaction Frequency Dilemma, oscillating between reckless execution and excessive user questioning. To address these issues, we propose a Dual-Stage Intent-Aware (DS-IA) Framework. This framework separates high-level user intent understanding from low-level physical execution. Specifically, Stage 1 serves as a semantic firewall to filter out invalid instructions and resolve vague commands by checking the current state of the home. Stage 2 then employs a deterministic cascade verifier-a strict, step-by-step rule checker that verifies the room, device, and capability in sequence-to ensure the action is actually physically possible before execution. Extensive experiments on the HomeBench and SAGE benchmarks demonstrate that DS-IA achieves an Exact Match (EM) rate of 58.56% (outperforming baselines by over 28%) and improves the rejection rate of invalid instructions to 87.04%. Evaluations on the SAGE benchmark further reveal that DS-IA resolves the Interaction Frequency Dilemma by balancing proactive querying with state-based inference. Specifically, it boosts the Autonomous Success Rate (resolving tasks without unnecessary user intervention) from 42.86% to 71.43%, while maintaining high precision in identifying irreducible ambiguities that truly necessitate human clarification. These results underscore the framework's ability to minimize user disturbance through accurate environmental grounding.
Paper Structure (35 sections, 5 equations, 4 figures, 5 tables, 1 algorithm)

This paper contains 35 sections, 5 equations, 4 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLM-based Planning for Smart Homes
  4. Ambiguity Resolution and Human Interaction
  5. Security, Privacy, and Efficiency
  6. Methodology
  7. Problem Formulation
  8. Stage 1: Global Intent Analysis and Routing
  9. Stage 2: Hierarchical Grounding Verification
  10. Mixed Intent Resolution Strategy
  11. A Running Example of the DS-IA Framework
  12. Prompt Engineering Strategy
  13. Experimental Setup
  14. Datasets
  15. HomeBench (Robustness)
  16. ...and 20 more sections

Figures (4)

  • Figure 1: Failure mechanisms in existing reactive frameworks. (a) Task Omission: The agent loses global context when trapped in multi-turn interactions. (b) Forced Hallucination: The agent forcibly maps an unexecutable command to an incorrect entity due to the lack of a proactive rejection mechanism.
  • Figure 2: Workflow of the Dual-Stage Intent-Aware (DS-IA) Framework. This image shows the modular architecture: Stage 1 proactively analyzes intent, and Stage 2 utilizes a cascade verifier shield to ensure physical feasibility.
  • Figure 3: Visual comparison of handling a non-existent device instruction on HomeBench. SAGE hallucinates a target (Forced Grounding), while DS-IA correctly rejects the command (Early Rejection).
  • Figure 4: The complete constraint-aware prompt templates utilized in the DS-IA framework. (a) The Stage 1 prompt acts as a semantic firewall to extract diagnostic reasoning. (b) The Stage 2 prompt generates the final execution sequence, integrating strict negative constraints (e.g., utilizing error_input for unexecutable actions) to enforce physical grounding.