The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable methods to accommodate diverse preferences and resolve conflicting directives. Drawing on conflict resolution theory, we introduce a user-centered workflow for multi-user personalization comprising three stages: Reflection, Analysis, and Feedback. We then present MAP -- a \textbf{M}ulti-\textbf{A}gent system for multi-user \textbf{P}ersonalization -- to operationalize this workflow. By delegating subtasks to specialized agents, MAP (1) retrieves and reflects on relevant user information, while enhancing reliability through agent-to-agent interactions, (2) provides detailed analysis for improved transparency and usability, and (3) integrates user feedback to iteratively refine results. Our user study findings (n=12) highlight MAP's effectiveness and usability for conflict resolution while emphasizing the importance of user involvement in resolution verification and failure management. This work highlights the potential of multi-agent systems to implement user-centered, multi-user personalization workflows and concludes by offering insights for personalization in multi-user contexts.

翻译：大型语言模型（LLM）及基于LLM的智能体在多用户场景中的广泛应用，凸显了对可靠、可用方法的需求，以适应用户的多样化偏好并解决相互冲突的指令。借鉴冲突解决理论，我们提出了一种以用户为中心的多用户个性化工作流程，该流程包含三个阶段：反思、分析与反馈。随后，我们提出了MAP——一个用于多用户个性化的多智能体系统——以实施此工作流程。通过将子任务分配给专门的智能体，MAP能够（1）检索并反思相关用户信息，同时通过智能体间的交互提升可靠性；（2）提供详细分析以提高透明度和可用性；（3）整合用户反馈以迭代优化结果。我们的用户研究结果（n=12）突显了MAP在解决冲突方面的有效性和可用性，同时强调了用户参与解决方案验证与故障管理的重要性。这项工作揭示了多智能体系统在实施以用户为中心的多用户个性化工作流程方面的潜力，并最终为多用户情境下的个性化提供了见解。