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.

翻译：个性化演示文稿生成不仅需要基于当前提示或模板的条件控制，智能体还需跨任务保持用户的稳定偏好、在多轮修订中保留新增的偏好与约束，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分层记忆框架，将长期记忆与工作记忆分离，并进一步将长期记忆划分为用户档案记忆与工具记忆。用户档案记忆存储基于意图的用户档案，用于第0轮个性化；工作记忆在修订轮次间携带活跃偏好与会话约束；工具记忆存储可复用的执行经验，确保可靠的局部编辑。该记忆框架与基于范围的幻灯片局部修订机制相结合，使目标更新仅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，用户档案记忆提升了多角色、多意图档案库中的角色对齐判断；工具记忆注入改进了诊断式配对设置下的闭环修改行为；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：在演示文稿创作中实现有效个性化，关键在于分离持久用户档案、会话级工作记忆以及可复用的执行经验，并兼顾生成与局部修订过程。