协作搜索支持多个用户共同完成特定的搜索任务。研究发现，在即时通讯平台内设计轻量级协作搜索插件更符合用户的协作习惯。然而，由于多用户交互场景的复杂性，实现一个功能完整的轻量级协作搜索系统颇具挑战。因此，以往关于轻量级协作搜索的研究不得不依赖“ Wizard of Oz ”范式。近年来，大语言模型（LLMs）已被证明能够与用户自然交互，并通过基于LLM的智能体完成复杂的信息检索任务。因此，为了更好地支持协作搜索研究，在本演示中，我们提出了CoSearchAgent，一个由LLM驱动的轻量级协作搜索智能体。CoSearchAgent被设计为Slack插件，能够在该平台的多方对话中支持协作搜索。凭借理解多用户对话中的查询和上下文的能力，以及通过API搜索网络相关信息的能力，CoSearchAgent能够以基于相关搜索结果为依据的答案回应用户查询。当信息需求不明确时，它还可以提出澄清性问题。所提出的CoSearchAgent具有高度灵活性，将有助于支持协作搜索的进一步研究。代码和演示视频均可访问。
英文摘要：
Collaborative search supports multiple users working together to accomplish a specific search task. Research has found that designing lightweight collaborative search plugins within instant messaging platforms aligns better with users' collaborative habits. However, due to the complexity of multi-user interaction scenarios, it is challenging to implement a fully functioning lightweight collaborative search system. Therefore, previous studies on lightweight collaborative search had to rely on the Wizard of Oz paradigm. In recent years, large language models (LLMs) have been demonstrated to interact naturally with users and achieve complex information-seeking tasks through LLM-based agents. Hence, to better support the research in collaborative search, in this demo, we propose CoSearchAgent, a lightweight collaborative search agent powered by LLMs. CoSearchAgent is designed as a Slack plugin that can support collaborative search during multi-party conversations on this platform. Equipped with the capacity to understand the queries and context in multi-user conversations and the ability to search the Web for relevant information via APIs, CoSearchAgent can respond to user queries with answers grounded on the relevant search results. It can also ask clarifying questions when the information needs are unclear. The proposed CoSearchAgent is highly flexible and would be useful for supporting further research on collaborative search. The code and demo video are accessible.
A collaborative agent with two lightweight synergistic models for autonomous crystal materials research
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute for Advanced Study, Chengdu University（成都大学高等研究院）；Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing University of Posts and Telecommunications（重庆邮电大学教育部网络空间大数据智能安全重点实验室）
Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations
Department of Software \& Microelectronics, Peking University, Beijing, China；Economic Law School, China University of Political Science；Financial Media, Peking University, ChangSha, China
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Illinois at Chicago（伊利诺伊大学香槟分校）；Capital One AI Foundations（Capital One AI基金会）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Towards Fair and Comprehensive Evaluation of Routers in Collaborative LLM Systems
Southern University of Science and Technology（南方科技大学）；Institut Polytechnique de Paris（巴黎政治学院）；Peking University（北京大学）；Deepexi Technology Co. Ltd.（深醒科技有限公司）；University of Edinburgh（爱丁堡大学）；Beihang University（北航）；Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
MCCE: A Framework for Multi-LLM Collaborative Search in Discrete Spaces with Similarity-Filtered Preference Learning
Zhongguancun Academy（中关村学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of Manchester（曼彻斯特大学）；Tongji University（同济大学）
