让AI助手主动懂你，通过模拟互动持续学习优化推荐。
ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation
- 构建用户-助手仿真环境，用反馈动态训练助手
- 32种不同人格的用户测试中，满意度稳步提升
- 适合想打造自适应智能助手的研究者和开发者
随着大语言模型日益融入日常生活，人们对既主动又个性化的AI助手需求不断增长。尽管近期研究分别推进了主动性与个性化，但二者的结合仍待深入。为此，我们提出ProPerSim——一种用于开发在真实家庭场景中及时、个性化推荐的新型任务与仿真框架。在该仿真环境中，具备丰富人格特征的用户代理与助手交互，并对每条建议是否符合其偏好与情境打分。助手的目标是利用这些评分，持续学习并改进以获得更高得分。基于ProPerSim，我们设计了ProPerAssistant，一种通过检索增强与偏好对齐实现持续学习与自适应的助手。在32种多样化人格的实验中，ProPerAssistant展现出策略调整能力，用户满意度稳步提升，验证了主动性与个性化融合的潜力。
原文摘要 · Abstract (English)
As large language models (LLMs) become increasingly integrated into daily life, there is growing demand for AI assistants that are not only reactive but also proactive and personalized. While recent advances have pushed forward proactivity and personalization individually, their combination remains underexplored. To bridge this gap, we introduce ProPerSim, a new task and simulation framework for developing assistants capable of making timely, personalized recommendations in realistic home scenarios. In our simulation environment, a user agent with a rich persona interacts with the assistant, providing ratings on how well each suggestion aligns with its preferences and context. The assistant's goal is to use these ratings to learn and adapt to achieve higher scores over time. Built on ProPerSim, we propose ProPerAssistant, a retrieval-augmented, preference-aligned assistant that continually learns and adapts through user feedback. Experiments across 32 diverse personas show that ProPerAssistant adapts its strategy and steadily improves user satisfaction, highlighting the promise of uniting proactivity and personalization.
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