# MiCU: End-to-End Smart Home Command Understanding with Large Language Model

arXiv: https://arxiv.org/abs/2606.01099 · HTML: https://mlsanity.com/paper/2606.01099/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 16 of 16)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
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- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Semantics-aware BERT for Language Understanding](https://mlsanity.com/paper/1909.02209/) (2019)
- [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://mlsanity.com/paper/2305.06984/) (2023)
- [LongAlign: A Recipe for Long Context Alignment of Large Language Models](https://mlsanity.com/paper/2401.18058/) (2024)
- [Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering](https://mlsanity.com/paper/2305.11541/) (2023)
- [Large Language Model Instruction Following: A Survey of Progresses and Challenges](https://mlsanity.com/paper/2303.10475/) (2023)
- [ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding](https://mlsanity.com/paper/2010.12283/) (2020)
- [Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification](https://mlsanity.com/paper/2108.02598/) (2021)
- [Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Models](https://mlsanity.com/paper/2305.09802/) (2023)
- [Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding](https://mlsanity.com/paper/2210.08536/) (2022)
- [Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions](https://mlsanity.com/paper/2604.00499/) (2026)
- [Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management](https://mlsanity.com/paper/2605.06472/) (2026)
- [FuxiShuffle: An Adaptive and Resilient Shuffle Service for Distributed Data Processing on Alibaba Cloud](https://mlsanity.com/paper/2602.22580/) (2026)

## Abstract (arXiv)

Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perform well on precise utterances (e.g., "turn on the bedroom light"), they struggle with ambiguous or misaligned commands (e.g., "make the bedroom cozy"). Large language models (LLMs) generalize well across various domains and can outperform traditional rule-based systems on such tasks, but their effectiveness is often constrained by scarce domain-specific data, insufficient task-specific adaptation, and high computational costs. In this paper, we propose an automated training data synthesis workflow using user logs and LLMs; then we build MiCU, a domain-specific LLM that excels at command understanding. Specifically, we employ curriculum learning to inject domain knowledge into the base LLM, then we enhance its reasoning ability via cold-start training combined with reinforcement learning (RL) guided by domain-specific thinking rules. Additionally, we introduce a token compression technique that condenses device description into a single special token, substantially reducing inference overhead and enabling model-fast, an efficient variant optimized for long inputs. Extensive experiments show that MiCU significantly outperforms baselines, with an average accuracy gain of 20.01% across all device categories. We have deployed MiCU in the Xiaomi Home app, receiving approximately 1.7 million page views per day. Production evaluations show that MiCU reduces user correction rate by 1.57% and increases human audited accuracy by 32.05%. Our data and code are available at https://github.com/xiaomi-research/iotspecllm