{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:54:10Z","timestamp":1781546050733,"version":"3.54.5"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2026,6,15]]},"abstract":"<jats:p>Personalized services in smart environments based on historical context have become an important demand. A promising direction is to leverage the strong reasoning abilities of Large Language Models (LLMs). However, enabling personalization requires persistent context memory. The key challenge lies in how to efficiently organize long-term device and sensor logs, and how to extract user behavior patterns and preferences from them. To address this challenge, we propose MemAura, a memory management system for smart environments. MemAura consists of three core modules: (1) Memory Graph Manager, which efficiently organizes contextual memory, supports fast retrieval, and provides user patterns and preference information to the LLM; (2) Periodic Pattern Predictor, which predicts periodic behavioral patterns and preferences at a given timestamp; (3) User Profiling Scheme, which transforms commands into explict intent units and incrementally updates and forgets high-level profiles over time.<\/jats:p>\n                  <jats:p>We evaluate MemAura against multiple baselines across two command datasets. With GPT-4o as the backbone, it achieves 100% accuracy and 38%-43% personalization rate. When deployed with lightweight local models, MemAura still achieves up to 95% accuracy, with an average personalization rate of up to 37.5%. However, when using the same lightweight LLM, vanilla only achieves 28%-56% accuracy, with a personalization rate of at most 21%. Vector-RAG achieves 54-83% accuracy and up to 25% personalization with lightweight models. While it outperforms vanilla, it still lags behind MemAura. The results show that MemAura maintains high accuracy and personalization across LLMs of different sizes, with practical token usage and latency. We also conduct a series of experiments to validate the robustness of MemAura. A user study with 8 participants further confirms its usability. The participants comprehensively experienced and evaluated its superior performance and service quality. Our work enables LLMs to better understand user intent and efficiently extract patterns and preferences from contextual memory, thereby making smart living spaces more efficient, context-aware, and user-centric.<\/jats:p>","DOI":"10.1145\/3810196","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:06:41Z","timestamp":1781543201000},"page":"1-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MemAura: Structured Context Memory for Personalized LLM Reasoning in Smart Environments"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4710-196X","authenticated-orcid":false,"given":"Siyuan","family":"Liu","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0313-4421","authenticated-orcid":false,"given":"Huangxun","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Amazon. 2025. alexa. https:\/\/www.amazon.com\/alexaplus"},{"key":"e_1_2_1_2_1","unstructured":"Apple. 2025. apple-siri. https:\/\/www.apple.com\/siri\/"},{"key":"e_1_2_1_3_1","volume-title":"NeurIPS 2023 workshop on instruction tuning and instruction following.","author":"Asai Akari","year":"2023","unstructured":"Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-rag: Self-reflective retrieval augmented generation. In NeurIPS 2023 workshop on instruction tuning and instruction following."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1080\/10447310802205776","article-title":"An empirical evaluation of the system usability scale","volume":"24","author":"Bangor Aaron","year":"2008","unstructured":"Aaron Bangor, Philip T Kortum, and James T Miller. 2008. An empirical evaluation of the system usability scale. Intl. Journal of Human-Computer Interaction 24, 6 (2008), 574\u2013594.","journal-title":"Journal of Human-Computer Interaction"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3699747","article-title":"Sensor2text: Enabling natural language interactions for daily activity tracking using wearable sensors","volume":"8","author":"Chen Wenqiang","year":"2024","unstructured":"Wenqiang Chen, Jiaxuan Cheng, Leyao Wang, Wei Zhao, and Wojciech Matusik. 2024. Sensor2text: Enabling natural language interactions for daily activity tracking using wearable sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, 4 (2024), 1\u201326.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_6_1","volume-title":"Chi, and Minmin Chen","author":"Christakopoulou Konstantina","year":"2023","unstructured":"Konstantina Christakopoulou, Alberto Lalama, Cj Adams, Iris Qu, Yifat Amir, Samer Chucri, Pierce Vollucci, Fabio Soldo, Dina Bseiso, Sarah Scodel, Lucas Dixon, Ed H. Chi, and Minmin Chen. 2023. Large Language Models for User Interest Journeys. arXiv:2305.15498 [cs.CL] https:\/\/arxiv.org\/abs\/2305.15498"},{"key":"e_1_2_1_7_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs\/1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs\/1810.04805 (2018). arXiv:1810.04805 http:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_2_1_8_1","volume-title":"2011 33rd International Conference on Software Engineering (ICSE)","author":"Dumitru Horatiu","year":"2011","unstructured":"Horatiu Dumitru, Marek Gibiec, Negar Hariri, Jane Cleland-Huang, Bamshad Mobasher, Carlos Castro-Herrera, and Mehdi Mirakhorli. 2011. On-demand feature recommendations derived from mining public product descriptions. 2011 33rd International Conference on Software Engineering (ICSE) (2011), 181\u2013190. https:\/\/api.semanticscholar.org\/CorpusID:2779250"},{"key":"e_1_2_1_9_1","unstructured":"Jizhan Fang Xinle Deng Haoming Xu Ziyan Jiang Yuqi Tang Ziwen Xu Shumin Deng Yunzhi Yao Mengru Wang Shuofei Qiao et al. 2025. LightMem: Lightweight and Efficient Memory-Augmented Generation. arXiv preprint arXiv:2510.18866 (2025)."},{"key":"e_1_2_1_10_1","volume-title":"Modular rag: Transforming rag systems into lego-like reconfigurable frameworks. arXiv preprint arXiv:2407.21059","author":"Gao Yunfan","year":"2024","unstructured":"Yunfan Gao, Yun Xiong, Meng Wang, and Haofen Wang. 2024. Modular rag: Transforming rag systems into lego-like reconfigurable frameworks. arXiv preprint arXiv:2407.21059 (2024)."},{"key":"e_1_2_1_11_1","volume-title":"In-context autoencoder for context compression in a large language model. arXiv preprint arXiv:2307.06945","author":"Ge Tao","year":"2023","unstructured":"Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, and Furu Wei. 2023. In-context autoencoder for context compression in a large language model. arXiv preprint arXiv:2307.06945 (2023)."},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3390\/designs8030043","article-title":"Designing home automation routines using an LLM-based chatbot","volume":"8","author":"Giudici Mathyas","year":"2024","unstructured":"Mathyas Giudici, Luca Padalino, Giovanni Paolino, Ilaria Paratici, Alexandru Ionut Pascu, and Franca Garzotto. 2024. Designing home automation routines using an LLM-based chatbot. Designs 8, 3 (2024), 43.","journal-title":"Designs"},{"key":"e_1_2_1_13_1","unstructured":"Google. 2025. Google home-gemini. https:\/\/home.google.com\/get-inspired\/bring-home-the-magic-of-gemini\/"},{"key":"e_1_2_1_14_1","volume-title":"Lightrag: Simple and fast retrieval-augmented generation. arXiv preprint arXiv:2410.05779","author":"Guo Zirui","year":"2024","unstructured":"Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. 2024. Lightrag: Simple and fast retrieval-augmented generation. arXiv preprint arXiv:2410.05779 (2024)."},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems. 158\u2013171","author":"Hu Jiawei","year":"2025","unstructured":"Jiawei Hu, Hong Jia, Mahbub Hassan, Lina Yao, Brano Kusy, and Wen Hu. 2025. Lightllm: A Versatile Large Language Model for Predictive Light Sensing. In Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems. 158\u2013171."},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.1109\/TMC.2021.3121332","article-title":"A Portable and Convenient System for Unknown Liquid Identification With Smartphone Vibration","volume":"22","author":"Huang Yongzhi","year":"2023","unstructured":"Yongzhi Huang, Kaixin Chen, Yandao Huang, Lu Wang, and Kaishun Wu. 2023. A Portable and Convenient System for Unknown Liquid Identification With Smartphone Vibration. IEEE Transactions on Mobile Computing 22 (2023), 1894\u20131911. https:\/\/api.semanticscholar.org\/CorpusID:239284340","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_2_1_17_1","volume-title":"Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 1\u20133.","author":"Huang Ziheng","year":"2023","unstructured":"Ziheng Huang, Sebastian Gutierrez, Hemanth Kamana, and Stephen MacNeil. 2023. Memory sandbox: Transparent and interactive memory management for conversational agents. In Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 1\u20133."},{"key":"e_1_2_1_18_1","unstructured":"ICATUS. 2023. Time-use activities classificationRegional Workshop on Time Use Statistics: Methods and Uses."},{"key":"e_1_2_1_19_1","unstructured":"IFTTT. 2024. IFTTT:Automation for Business and Home. https:\/\/ifttt.com\/ Accessed:2024\u201309-06."},{"key":"e_1_2_1_20_1","volume-title":"An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks. CoRR abs\/2007.15951","author":"Iwana Brian Kenji","year":"2020","unstructured":"Brian Kenji Iwana and Seiichi Uchida. 2020. An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks. CoRR abs\/2007.15951 (2020). arXiv:2007.15951 https:\/\/arxiv.org\/abs\/2007.15951"},{"key":"e_1_2_1_21_1","volume-title":"Companion Proceedings of the ACM on Web Conference","author":"Jin Jiajie","year":"2025","unstructured":"Jiajie Jin, Yutao Zhu, Zhicheng Dou, Guanting Dong, Xinyu Yang, Chenghao Zhang, Tong Zhao, Zhao Yang, and Ji-Rong Wen. 2025. Flashrag: A modular toolkit for efficient retrieval-augmented generation research. In Companion Proceedings of the ACM on Web Conference 2025. 737\u2013740."},{"key":"e_1_2_1_22_1","volume-title":"ACM Conference on Health, Inference, and Learning. https:\/\/api.semanticscholar.org\/CorpusID:266998674","author":"Kim Yu Han","year":"2024","unstructured":"Yu Han Kim, Xuhai Xu, Daniel McDuff, Cynthia Breazeal, and Hae Won Park. 2024. Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data. In ACM Conference on Health, Inference, and Learning. https:\/\/api.semanticscholar.org\/CorpusID:266998674"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3643505","article-title":"Sasha: Creative Goal-oriented Reasoning in Smart Homes with Large Language Models","volume":"8","author":"King Evan","year":"2024","unstructured":"Evan King, Haoxiang Yu, Sangsu Lee, and Christine Julien. 2024. Sasha: Creative Goal-oriented Reasoning in Smart Homes with Large Language Models. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, 1 (2024), 1\u201338.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_24_1","volume-title":"LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction. ArXiv abs\/2507.04748","author":"Lee Sungmin","year":"2025","unstructured":"Sungmin Lee, Minju Kang, Joonhee Lee, Seungyong Lee, Dongju Kim, Jingi Hong, Jun Shin, Pei Zhang, and JeongGil Ko. 2025. LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction. ArXiv abs\/2507.04748 (2025). https:\/\/api.semanticscholar.org\/CorpusID:280148125"},{"key":"e_1_2_1_25_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. Advances in neural information processing systems 33 (2020) 9459\u20139474."},{"key":"e_1_2_1_26_1","unstructured":"Hao Li Chenghao Yang An Zhang Yang Deng Xiang Wang and Tat-Seng Chua. 2025. Hello Again! LLM-powered Personalized Agent for Long-term Dialogue. arXiv:2406.05925 [cs.CL] https:\/\/arxiv.org\/abs\/2406.05925"},{"key":"e_1_2_1_27_1","unstructured":"Zhiyu Li Shichao Song Chenyang Xi Hanyu Wang Chen Tang Simin Niu Ding Chen Jiawei Yang Chunyu Li Qingchen Yu et al. 2025. MemOS: A Memory OS for AI System. arXiv preprint arXiv:2507.03724 (2025)."},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems. 213\u2013225","author":"Liu Kaiwei","year":"2025","unstructured":"Kaiwei Liu, Bufang Yang, Lilin Xu, Yunqi Guo, Guoliang Xing, Xian Shuai, Xiaozhe Ren, Xin Jiang, and Zhenyu Yan. 2025. TaskSense: A Translation-like Approach for Tasking Heterogeneous Sensor Systems with LLMs. In Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems. 213\u2013225."},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems","author":"Liu Kaiwei","year":"2025","unstructured":"Kaiwei Liu, Bufang Yang, Lilin Xu, Yunqi Guo, Guoliang Xing, Xian Shuai, Xiaozhe Ren, Xin Jiang, and Zhenyu Yan. 2025. TaskSense: A Translation-like Approach for Tasking Heterogeneous Sensor Systems with LLMs. Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems (2025). https:\/\/api.semanticscholar.org\/CorpusID:278326090"},{"key":"e_1_2_1_30_1","unstructured":"Nelson F. Liu Kevin Lin John Hewitt Ashwin Paranjape Michele Bevilacqua Fabio Petroni and Percy Liang. 2023. Lost in the Middle: How Language Models Use Long Contexts. arXiv:2307.03172 [cs.CL] https:\/\/arxiv.org\/abs\/2307.03172"},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.3390\/diagnostics11061067","article-title":"Smartphone-enabled personalized diagnostics: Current status and future prospects","volume":"11","author":"Merazzo Karla Jaimes","year":"2021","unstructured":"Karla Jaimes Merazzo, Joseba Totoricaguena-Gorri\u00f1o, Eduardo Fern\u00e1ndez-Mart\u00edn, F Javier Del Campo, and Eva Baldrich. 2021. Smartphone-enabled personalized diagnostics: Current status and future prospects. Diagnostics 11, 6 (2021), 1067.","journal-title":"Diagnostics"},{"key":"e_1_2_1_32_1","unstructured":"Microsoft. 2023. phi-2. https:\/\/huggingface.co\/microsoft\/phi-2"},{"key":"e_1_2_1_33_1","volume-title":"Towards lifelong dialogue agents via timeline-based memory management. arXiv preprint arXiv:2406.10996","author":"Ong Kai","year":"2024","unstructured":"Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak, Hyungjoo Chae, Taeyoon Kwon, Yohan Jo, Seung-won Hwang, Dongha Lee, and Jinyoung Yeo. 2024. Towards lifelong dialogue agents via timeline-based memory management. arXiv preprint arXiv:2406.10996 (2024)."},{"key":"e_1_2_1_34_1","unstructured":"OpenAI. 2025. Hello GPT-4o. https:\/\/openai.com\/index\/hello-gpt-4o\/ Accessed:2025\u201304-26."},{"key":"e_1_2_1_35_1","unstructured":"Charles Packer Vivian Fang Shishir_G Patil Kevin Lin Sarah Wooders and Joseph_E Gonzalez. 2023. MemGPT: Towards LLMs as Operating Systems. (2023)."},{"key":"e_1_2_1_36_1","volume-title":"Vito Walter Anelli, Fedelucio Narducci, Tommaso Di Noia, and Eugenio Di Sciascio.","author":"Palma Dario Di","year":"2024","unstructured":"Dario Di Palma, Giovanni Maria Biancofiore, Vito Walter Anelli, Fedelucio Narducci, Tommaso Di Noia, and Eugenio Di Sciascio. 2024. Evaluating ChatGPT as a Recommender System: A Rigorous Approach. arXiv:2309.03613 [cs.IR] https:\/\/arxiv.org\/abs\/2309.03613"},{"key":"e_1_2_1_37_1","unstructured":"Zhuoshi Pan Qianhui Wu Huiqiang Jiang Xufang Luo Hao Cheng Dongsheng Li Yuqing Yang Chin-Yew Lin H. Vicky Zhao Lili Qiu and Jianfeng Gao. 2025. On Memory Construction and Retrieval for Personalized Conversational Agents. arXiv:2502.05589 [cs.CL] https:\/\/arxiv.org\/abs\/2502.05589"},{"key":"e_1_2_1_38_1","volume-title":"Conference on Robot Learning. PMLR, 881\u2013891","author":"Patel Maithili","year":"2023","unstructured":"Maithili Patel and Sonia Chernova. 2023. Proactive Robot Assistance via Spatio-Temporal Object Modeling. In Conference on Robot Learning. PMLR, 881\u2013891."},{"key":"e_1_2_1_39_1","volume-title":"2007 IEEE International Fuzzy Systems Conference","author":"P\u00e9rez Luis G.","year":"2007","unstructured":"Luis G. P\u00e9rez, Manuel J. Barranco, and Luis Mart\u00ednez-L\u00f3pez. 2007. Building User profiles for Recommender Systems from incomplete preference relations. 2007 IEEE International Fuzzy Systems Conference (2007), 1\u20136. https:\/\/api.semanticscholar.org\/CorpusID:567609"},{"key":"e_1_2_1_40_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8494\u20138502","author":"Puig Xavier","year":"2018","unstructured":"Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba. 2018. Virtualhome: Simulating Household Activities via Programs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8494\u20138502."},{"key":"e_1_2_1_41_1","volume-title":"Memorag: Moving towards next-gen rag via memory-inspired knowledge discovery. arXiv preprint arXiv:2409.05591 1","author":"Qian Hongjin","year":"2024","unstructured":"Hongjin Qian, Peitian Zhang, Zheng Liu, Kelong Mao, and Zhicheng Dou. 2024. Memorag: Moving towards next-gen rag via memory-inspired knowledge discovery. arXiv preprint arXiv:2409.05591 1 (2024)."},{"key":"e_1_2_1_42_1","volume-title":"AIoT Smart Home via Autonomous LLM Agents","author":"Rivkin Dmitriy","year":"2024","unstructured":"Dmitriy Rivkin, Francois Hogan, Amal Feriani, Abhisek Konar, Adam Sigal, Xue Liu, and Gregory Dudek. 2024. AIoT Smart Home via Autonomous LLM Agents. IEEE Internet of Things Journal (2024)."},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Scott Sanner Krisztian Balog Filip Radlinski Ben Wedin and Lucas Dixon. 2023. Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences. arXiv:2307.14225 [cs.IR] https:\/\/arxiv.org\/abs\/2307.14225","DOI":"10.1145\/3604915.3608845"},{"key":"e_1_2_1_44_1","volume-title":"Chi, Nathanael Sch\u00e4rli, and Denny Zhou","author":"Shi Freda","year":"2023","unstructured":"Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Sch\u00e4rli, and Denny Zhou. 2023. Large Language Models Can Be Easily Distracted by Irrelevant Context. arXiv:2302.00093 [cs.CL] https:\/\/arxiv.org\/abs\/2302.00093"},{"key":"e_1_2_1_45_1","volume-title":"Agentic reasoning and tool integration for llms via reinforcement learning. arXiv preprint arXiv:2505.01441","author":"Singh Joykirat","year":"2025","unstructured":"Joykirat Singh, Raghav Magazine, Yash Pandya, and Akshay Nambi. 2025. Agentic reasoning and tool integration for llms via reinforcement learning. arXiv preprint arXiv:2505.01441 (2025)."},{"key":"e_1_2_1_46_1","unstructured":"Qwen Team. 2025. Qwen3 Technical Report. arXiv:2505.09388 [cs.CL] https:\/\/arxiv.org\/abs\/2505.09388"},{"key":"e_1_2_1_47_1","volume-title":"Proceedings of the International Conference on Modeling, Natural Language Processing and Machine Learning. 91\u201397","author":"Wang Cangqing","year":"2024","unstructured":"Cangqing Wang, Yutian Yang, Ruisi Li, Dan Sun, Ruicong Cai, Yuzhu Zhang, and Chengqian Fu. 2024. Adapting llms for efficient context processing through soft prompt compression. In Proceedings of the International Conference on Modeling, Natural Language Processing and Machine Learning. 91\u201397."},{"key":"e_1_2_1_48_1","unstructured":"Lei Wang and Ee-Peng Lim. 2023. Zero-Shot Next-Item Recommendation using Large Pretrained Language Models. arXiv:2304.03153 [cs.IR] https:\/\/arxiv.org\/abs\/2304.03153"},{"key":"e_1_2_1_49_1","volume-title":"AAAI workshop on activity context representation: techniques and languages","author":"Weiss Gary M","year":"2012","unstructured":"Gary M Weiss and Jeffrey W Lockhart. 2012. The impact of personalization on smartphone-based activity recognition. In AAAI workshop on activity context representation: techniques and languages. Toronto., 98\u2013104."},{"key":"e_1_2_1_50_1","unstructured":"Xiaomi. 2025. Xiaomi Xiaoai. https:\/\/xiaoai.mi.com\/"},{"key":"e_1_2_1_51_1","volume-title":"Invisible Prompts","author":"Xiong Junjie","year":"2025","unstructured":"Junjie Xiong, Changjia Zhu, Shuhang Lin, Chong Zhang, Yongfeng Zhang, Yao Liu, and Lingyao Li. 2025. Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models. arXiv preprint arXiv:2505.16957 (2025)."},{"key":"e_1_2_1_52_1","volume-title":"Ngai","author":"Xu Jinfeng","year":"2024","unstructured":"Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Hewei Wang, and Edith C. H. Ngai. 2024. MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation. arXiv:2402.19407 [cs.IR] https:\/\/arxiv.org\/abs\/2402.19407"},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems","author":"Xu Lilin","year":"2023","unstructured":"Lilin Xu, Chaojie Gu, Rui Tan, Shibo He, and Jiming Chen. 2023. MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels. Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems (2023). https:\/\/api.semanticscholar.org\/CorpusID:268857170"},{"key":"e_1_2_1_54_1","volume-title":"A-mem: Agentic memory for llm agents. arXiv preprint arXiv:2502.12110","author":"Xu Wujiang","year":"2025","unstructured":"Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, and Yongfeng Zhang. 2025. A-mem: Agentic memory for llm agents. arXiv preprint arXiv:2502.12110 (2025)."},{"key":"e_1_2_1_55_1","first-page":"10","volume-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8, 2, Article 87 (May","author":"Xu Zhenyu","year":"2024","unstructured":"Zhenyu Xu, Hailin Xu, Zhouyang Lu, Yingying Zhao, Rui Zhu, Yujiang Wang, Mingzhi Dong, Yuhu Chang, Qin Lv, Robert P. Dick, Fan Yang, Tun Lu, Ning Gu, and Li Shang. 2024. Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground. Proc. ACM Interact. Mob. 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