MemSlides：一种面向个性化幻灯片生成的分层记忆驱动代理框架，支持多轮局部修订

摘要

个性化演示生成需要的不仅仅是以当前提示或模板为条件：智能体必须跨任务保持稳定的用户偏好，在多轮修订过程中保留新引入的偏好与约束，并可靠地执行局部编辑。我们提出MemSlides，一种面向个性化演示智能体的层次化记忆框架，它将长期记忆与工作记忆分离，并进一步将长期记忆分为用户画像记忆与工具记忆。用户画像记忆存储基于意图的条件化画像，用于第0轮个性化；工作记忆在修订轮次间承载活跃偏好与会话约束；工具记忆存储可复用的执行经验，以支持可靠的局部编辑。MemSlides将这一记忆设计与有范围的幻灯片局部修订相结合，使得定向更新作用于最小的受影响区域，而非反复重新生成整个演示文稿。在受控实验中，用户画像记忆在多人物、多意图画像库上改善了人物一致性判断；工具记忆注入在诊断性配对设置中改善了闭环修改行为；定性案例展示了工作记忆承载偏好的能力。综合来看，这些结果表明，演示文稿撰写中的有效个性化取决于跨生成与局部修订过程分离持久用户画像、会话级工作记忆以及可复用执行经验。

English

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.