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、可用的方法来协调多元偏好并解决冲突指令的需求。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——以实现该工作流程的运作。通过将子任务分配给专用智能体，MAP能够（1）检索并反思相关用户信息，同时通过智能体间交互提升可靠性；（2）提供详细分析以增强透明度和可用性；（3）整合用户反馈以迭代优化结果。我们的用户研究（n=12）结果表明，MAP在冲突解决方面具有显著的有效性和可用性，同时强调了用户在解决方案验证与故障管理过程中参与的重要性。本研究揭示了多智能体系统在实现以用户为中心的多用户个性化工作流程方面的潜力，并最终为多用户场景下的个性化实践提供了见解。