做PPT的AI终于能记住你的偏好
现在的AI做PPT，每次都得重新说一遍你的偏好，改一页可能整份都乱掉。这篇把记忆拆成三层：长期存你的风格偏好、短期记这次改稿的临时要求、工具层存怎么改才不崩的操作经验。改某一页时只动那一页，不重做整份。测试里，它记住用户偏好的准确度明显更高。不是你明天能用上的，但方向对了：AI助手该学会分清楚哪些该忘、哪些该记。
📄 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.