Recent advances in large language models (LLMs) have enabled increasingly capable chatbots. However, most existing systems focus on single-user settings and do not generalize well to multi-user group chat interactions, where agents require more proactive and accurate intervention under complex, evolving contexts. Existing approaches typically rely on LLMs for both intervention reasoning and response generation, leading to high token consumption, limited scalability, and potential privacy risks. To address these challenges, we propose GroupGPT, a token-efficient and privacy-preserving agentic framework for multi-user chat assistant. GroupGPT adopts an edge-cloud model collaboration architecture to decouple intervention timing from response generation, enabling efficient and accurate decision-making while preserving user privacy through on-device processing of sensitive information. The framework also supports multimodal inputs, including memes, images, videos, and voice messages.To support evaluation of timing accuracy and response quality, we further introduce MUIR, a benchmark dataset for multi-user chat assistant intervention reasoning. MUIR contains 2,500 annotated group chat segments with intervention labels and rationales. We evaluate a range of models on MUIR, spanning from open-source to proprietary variants, including both LLMs and their smaller counterparts. Extensive experiments demonstrate that GroupGPT generates accurate and well-timed responses, achieving an average score of 4.72/5.0 in LLM-based evaluation, and is well-received by users across diverse group chat scenarios. Moreover, GroupGPT reduces the token usage by up to 3 times compared to baselines, while providing privacy sanitization of user messages before cloud transmission. Code is available at: https://github.com/Eliot-Shen/GroupGPT .
Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPT
Department of Computer Science（计算机科学系）；Department of Biomedical and Health Information Sciences（生物医学与健康信息科学系）；Department of Medicine（医学系）；Department of Communications（通讯系）
Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant
Czech Technical University in Prague（布拉格捷克技术大学）；National Institute of Mental Health in Prague（布拉格国家精神卫生研究所）；The University of Hong Kong（香港大学）；Shenzhen Mental Health Centre（深圳市精神卫生中心）
ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis
University of California, Berkeley（加州大学伯克利分校）；Kavli Energy Nanoscience Institute, University of California, Berkeley（加州大学伯克利分校卡夫利能源纳米科学研究所）；King Abdulaziz City for Science and Technology（阿卜杜勒阿齐兹国王科技城）
Assessing Web Search Credibility and Response Groundedness in Chat Assistants
Faculty of Information Technology, Brno University of Technology（布拉格技术学院）；Kempelen Institute of Intelligent Technologies（智能技术研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Centre for European Research in Trusted AI (CERTAIN)（可信AI欧洲研究中心）
Personalized Large Language Model Assistant with Evolving Conditional Memory
The Hong Kong Polytechnic University（香港理工大学）；INF Technology (Shanghai) co., Ltd.（INF科技（上海）有限公司）；Institute of Artificial Intelligence, Soochow University, China（苏州大学人工智能研究院）
