做PPT的AI终于能记住你上次改了什么
现在的AI做PPT，每次对话都像失忆：你刚说“标题用蓝色”，下一轮它又变回默认色。这篇把记忆拆成三层——用户画像（长期偏好）、工作记忆（本轮约束）、工具记忆（怎么改的步骤），让AI在多次修改中不丢设定，且只改你指的那一页，不用重做整个PPT。它不是你明天能用上的，但方向对了：AI助手得学会“记住你”而不是“猜你”。
📄 原文摘要(英文)
Personalized presentation generation requires more than conditioning on a current prompt or template: agents must preserve stable user preferences across tasks, retain newly introduced preferences and constraints during multi-turn revision, and carry out local edits reliably. We propose MemSlides, a hierarchical memory framework for personalized presentation agents that separates long-term memory from working memory and further divides long-term memory into user profile memory and tool memory. User profile memory stores intent-conditioned profiles for round-0 personalization, working memory carries active preferences and session constraints across revision rounds, and tool memory stores reusable execution experience for reliable localized editing. MemSlides pairs this memory design with scoped slide-local revision, so targeted updates act on the smallest affected region instead of repeatedly regenerating the full deck. In controlled experiments, user profile memory improves persona-alignment judgments on a multi-persona, multi-intent profile bank, tool-memory injection improves closed-loop modify behavior in diagnostic matched-pair settings, and qualitative cases illustrate working memory's ability to carryover preferences. Taken together, these results suggest that effective personalization in presentation authoring depends on separating persistent user profiles, session-level working memory, and reusable execution experience across generation and localized revision.