Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes
arXiv:2603.16207v1 Announce Type: new 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 roo
arXiv:2603.16207v1 Announce Type: new 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.
Executive Summary
This article introduces a novel Dual-Stage Intent-Aware (DS-IA) framework to address reliability and efficiency challenges in LLMs acting as agents in AIoT smart homes. The framework introduces a semantic firewall (Stage 1) to filter invalid instructions and resolve ambiguities using contextual state verification, followed by a deterministic cascade verifier (Stage 2) that confirms physical feasibility before execution. Experimental results demonstrate significant improvements over existing baselines—specifically, a 28% increase in Exact Match (EM) rate (58.56%) and an 87.04% invalid instruction rejection rate. Moreover, DS-IA effectively mitigates the Interaction Frequency Dilemma by enhancing autonomous success rates from 42.86% to 71.43% without exacerbating user disruption. The study presents a balanced, scalable solution to the dual challenges of hallucination mitigation and interaction efficiency.
Key Points
- ▸ Introduction of DS-IA framework with dual-stage intent analysis
- ▸ Stage 1 semantic firewall for filtering invalid commands via state context
- ▸ Stage 2 deterministic cascade verifier for pre-execution feasibility validation
Merits
Enhanced Accuracy
DS-IA achieves a 58.56% EM rate, outperforming baselines by over 28%, indicating improved command comprehension and execution alignment.
Improved Rejection Efficiency
Invalid instruction rejection rate rises to 87.04%, reducing user disturbance from hallucinated actions.
Interaction Frequency Resolution
Autonomous success rate increases to 71.43%, demonstrating effective mitigation of oscillation between reckless execution and over-questioning.
Demerits
Implementation Complexity
Integrating two distinct verification stages may increase computational overhead and latency, potentially affecting real-time performance in resource-constrained IoT environments.
Generalization Risk
Experimental validation is confined to HomeBench and SAGE benchmarks; scalability to diverse, heterogeneous smart home ecosystems remains unproven.
Expert Commentary
The DS-IA framework represents a sophisticated, layered approach to a persistent problem in AI-driven IoT: the tension between autonomy and safety. By decoupling intent recognition from execution validation, the authors effectively isolate the source of ambiguity—whether semantic or physical—and apply targeted verification at each stage. This architecture avoids the trap of iterative frameworks that create user fatigue through excessive back-and-forth. Moreover, the empirical gains in both rejection accuracy and autonomous resolution rates are statistically significant and practically relevant. Notably, the framework’s design implicitly acknowledges the evolving nature of smart home environments, where device inventories and user behaviors are dynamic. The use of state-based inference in Stage 1 is particularly commendable, as it introduces a form of contextual adaptability without sacrificing determinism. While implementation challenges remain, the conceptual clarity and empirical validation elevate this work beyond incremental improvements—it offers a paradigm shift in intent-aware AI agent design for smart homes.
Recommendations
- ✓ Researchers should extend DS-IA’s architecture to heterogeneous IoT ecosystems beyond home automation, such as industrial smart infrastructure.
- ✓ Industry stakeholders should evaluate DS-IA for integration into open-source AI agent platforms to promote standardization and interoperability.