{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:40:12Z","timestamp":1786120812393,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62225113"],"award-info":[{"award-number":["62225113"]}]},{"name":"National Natural Science Foundation of China","award":["62472327"],"award-info":[{"award-number":["62472327"]}]},{"name":"Innovative Research Group Project of Hubei Province","award":["2024AFA017"],"award-info":[{"award-number":["2024AFA017"]}]},{"name":"Key RD Program of Hubei Province","award":["2023BAB077"],"award-info":[{"award-number":["2023BAB077"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3818446","type":"proceedings-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:08:33Z","timestamp":1786064913000},"page":"7311-7321","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MiCU: End-to-End Smart Home Command Understanding with Large Language Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5201-3893","authenticated-orcid":false,"given":"Haowei","family":"Han","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2525-2147","authenticated-orcid":false,"given":"Kexin","family":"Hu","sequence":"additional","affiliation":[{"name":"Xiaomi Corporation, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7346-2885","authenticated-orcid":false,"given":"Weiwei","family":"Cai","sequence":"additional","affiliation":[{"name":"Xiaomi Corporation, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1403-407X","authenticated-orcid":false,"given":"Debiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xiaomi Corporation, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1774-6041","authenticated-orcid":false,"given":"Bin","family":"Qin","sequence":"additional","affiliation":[{"name":"Xiaomi Corporation, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5483-8322","authenticated-orcid":false,"given":"Yuxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0051-0046","authenticated-orcid":false,"given":"Jiawei","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2122-915X","authenticated-orcid":false,"given":"Xiao","family":"Yan","sequence":"additional","affiliation":[{"name":"Institute for Math and AI, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0059-8458","authenticated-orcid":false,"given":"Bo","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Yushi Bai Xin Lv Jiajie Zhang Yuze He Ji Qi Lei Hou Jie Tang Yuxiao Dong and Juanzi Li. 2024. LongAlign: A Recipe for Long Context Alignment of Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP.","DOI":"10.18653\/v1\/2024.findings-emnlp.74"},{"key":"e_1_3_2_1_2_1","volume-title":"Compositional semantic parsing with large language models. arXiv preprint arXiv:2209.15003","author":"Drozdov Andrew","year":"2022","unstructured":"Andrew Drozdov, Nathanael Sch\u00e4rli, Ekin Aky\u00fcrek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2022. Compositional semantic parsing with large language models. arXiv preprint arXiv:2209.15003 (2022)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3678585"},{"key":"e_1_3_2_1_4_1","unstructured":"Aaron Grattafiori Abhimanyu Dubey Abhinav Jauhri Abhinav Pandey Abhishek Kadian Ahmad Al-Dahle Aiesha Letman Akhil Mathur Alan Schelten Alex Vaughan et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)."},{"key":"e_1_3_2_1_5_1","unstructured":"Daya Guo Dejian Yang Haowei Zhang Junxiao Song Ruoyu Zhang Runxin Xu Qihao Zhu Shirong Ma Peiyi Wang Xiao Bi et al. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 (2025)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737614"},{"key":"e_1_3_2_1_7_1","unstructured":"Aaron Hurst Adam Lerer Adam P Goucher Adam Perelman Aditya Ramesh Aidan Clark AJ Ostrow Akila Welihinda Alan Hayes Alec Radford et al. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276 (2024)."},{"key":"e_1_3_2_1_8_1","volume-title":"Knowledge distillation from bert transformer to speech transformer for intent classification. arXiv preprint arXiv:2108.02598","author":"Jiang Yidi","year":"2021","unstructured":"Yidi Jiang, Bidisha Sharma, Maulik Madhavi, and Haizhou Li. 2021. Knowledge distillation from bert transformer to speech transformer for intent classification. arXiv preprint arXiv:2108.02598 (2021)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.307"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414558"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3643505"},{"key":"e_1_3_2_1_12_1","unstructured":"Patrick Lewis Ethan Perez Aleksandra Piktus Fabio Petroni Vladimir Karpukhin Naman Goyal Heinrich K\u00fcttler Mike Lewis Wen-tau Yih Tim Rockt\u00e4schel et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Yuhao Lin Zhipeng Tang Jiayan Tong Junqing Xiao Bin Lu Yuhang Li Chao Li Zhiguo Zhang Junhua Wang Hao Luo et al. 2026. FuxiShuffle: An Adaptive and Resilient Shuffle Service for Distributed Data Processing on Alibaba Cloud. arXiv preprint arXiv:2602.22580 (2026).","DOI":"10.1145\/3788853.3803085"},{"key":"e_1_3_2_1_14_1","unstructured":"Aixin Liu Aoxue Mei Bangcai Lin Bing Xue Bingxuan Wang Bingzheng Xu Bochao Wu Bowei Zhang Chaofan Lin Chen Dong et al. 2025. Deepseek-v3.2: Pushing the frontier of open large language models. arXiv preprint arXiv:2512.02556 (2025)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1162\/coli_a_00523"},{"key":"e_1_3_2_1_16_1","volume-title":"Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems (NeurIPS)","author":"Madaan Aman","year":"2023","unstructured":"Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al., 2023. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems (NeurIPS) (2023)."},{"key":"e_1_3_2_1_17_1","volume-title":"VISH: Does Your Smart Home Dialogue System Also Need Training Data?","author":"Noura Mahda","year":"2020","unstructured":"Mahda Noura, Sebastian Heil, and Martin Gaedke. 2020. VISH: Does Your Smart Home Dialogue System Also Need Training Data?. In Web Engineering, Maria Bielikova, Tommi Mikkonen, and Cesare Pautasso (Eds.). 171-187."},{"key":"e_1_3_2_1_18_1","volume-title":"Sage: Smart home agent with grounded execution. arXiv preprint arXiv:2311.00772","author":"Rivkin Dmitriy","year":"2023","unstructured":"Dmitriy Rivkin, Francois Hogan, Amal Feriani, Abhisek Konar, Adam Sigal, Steve Liu, and Greg Dudek. 2023. Sage: Smart home agent with grounded execution. arXiv preprint arXiv:2311.00772 (2023)."},{"key":"e_1_3_2_1_19_1","volume-title":"Benchclamp: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing. In Advances in Neural Information Processing Systems (NeurIPS).","author":"Roy Subhro","year":"2023","unstructured":"Subhro Roy, Samuel Thomson, Tongfei Chen, Richard Shin, Adam Pauls, Jason Eisner, and Benjamin Van Durme. 2023. Benchclamp: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_20_1","volume-title":"Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha.","author":"Sahoo Pranab","year":"2024","unstructured":"Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024. A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv preprint arXiv:2402.07927 (2024)."},{"key":"e_1_3_2_1_21_1","volume-title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300","author":"Shao Zhihong","year":"2024","unstructured":"Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al., 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 (2024)."},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the NeurIPS 2022 Workshop on Foundation Models for Decision Making.","author":"Silver Tom","year":"2022","unstructured":"Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tom\u00e1s Lozano-P\u00e9rez, and Leslie Pack Kaelbling. 2022. PDDL planning with pretrained large language models. In Proceedings of the NeurIPS 2022 Workshop on Foundation Models for Decision Making."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v25i1.7979"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.207"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3770854.3783925"},{"key":"e_1_3_2_1_26_1","volume-title":"LLM4TAP: LLM-Enhanced TAP Rule Recommendation","author":"Wu Gang","year":"2025","unstructured":"Gang Wu, Liang Hu, Yuxiao Hu, Xingbo Xiong, and Feng Wang. 2025. LLM4TAP: LLM-Enhanced TAP Rule Recommendation. IEEE Internet of Things Journal, V12, 10 (2025), 13157-13169."},{"key":"e_1_3_2_1_27_1","volume-title":"Translating natural language to planning goals with large-language models. arXiv preprint arXiv:2302.05128","author":"Xie Yaqi","year":"2023","unstructured":"Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. 2023. Translating natural language to planning goals with large-language models. arXiv preprint arXiv:2302.05128 (2023)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.542"},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of International Conference on Learning Representations (ICLR).","author":"Xu Can","year":"2023","unstructured":"Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. In Proceedings of International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_30_1","unstructured":"An Yang Anfeng Li Baosong Yang Beichen Zhang Binyuan Hui Bo Zheng Bowen Yu Chang Gao Chengen Huang Chenxu Lv et al. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388 (2025)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-industry.29"},{"key":"e_1_3_2_1_32_1","unstructured":"Jianxin Yang. 2023. Firefly. https:\/\/github.com\/yangjianxin1\/Firefly."},{"key":"e_1_3_2_1_33_1","volume-title":"Dapo: An open-source llm reinforcement learning system at scale. arXiv preprint arXiv:2503.14476","author":"Yu Qiying","year":"2025","unstructured":"Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, et al., 2025. Dapo: An open-source llm reinforcement learning system at scale. arXiv preprint arXiv:2503.14476 (2025)."},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).","author":"John","unstructured":"John M. Zelle and Raymond J. Mooney. 1993. Learning semantic grammars with constructive inductive logic programming. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3777411"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6510"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","unstructured":"Xue Zhao Hanyu Zhao Sha Yuan and Yequan Wang. 2022. WuDaoCorpora Text. doi:10.57760\/sciencedb.o00126.00004","DOI":"10.57760\/sciencedb.o00126.00004"},{"key":"e_1_3_2_1_38_1","volume-title":"2026 a. Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management. arXiv preprint arXiv:2605.06472","author":"Zheng Haoyu","year":"2026","unstructured":"Haoyu Zheng, Fangcheng Fu, Jia Wu, Binhang Yuan, Yongqiang Zhang, Hao Wang, Yuanyuan Zhu, Xiao Yan, and Jiawei Jiang. 2026 a. Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management. arXiv preprint arXiv:2605.06472 (2026)."},{"key":"e_1_3_2_1_39_1","volume-title":"2026 b. Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions. arXiv preprint arXiv:2604.00499","author":"Zheng Haoyu","year":"2026","unstructured":"Haoyu Zheng, Yongqiang Zhang, Fangcheng Fu, Xiaokai Zhou, Hao Luo, Hongchao Zhu, Yuanyuan Zhu, Hao Wang, Xiao Yan, and Jiawei Jiang. 2026 b. Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions. arXiv preprint arXiv:2604.00499 (2026)."},{"key":"e_1_3_2_1_40_1","volume-title":"Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911","author":"Zhou Jeffrey","year":"2023","unstructured":"Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911 (2023)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:28:11Z","timestamp":1786066091000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3818446"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":40,"alternative-id":["10.1145\/3770855.3818446","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3818446","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}