# MemSlides：面向个性化幻灯片生成的多轮局部修订分层记忆驱动智能体框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-15 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmqoxqlh60591slx6jnszzw37
- 原文链接：https://arxiv.org/abs/2606.17162

## AI 摘要

MemSlides提出分层记忆框架，将长期记忆与工作记忆分离。长期记忆再分为用户画像记忆（存储面向意图的配置，支持初始个性化）和工具记忆（存储可复用执行经验，支持可靠局部编辑）；工作记忆在多轮修订中承载当前偏好与会话约束。框架采用范围限定的幻灯片局部修订机制，仅更新最小影响区域。实验表明：用户画像记忆提升多人物多意图场景的人物对齐效果，工具记忆改善闭环修改行为，工作记忆能有效传递偏好。

## 正文

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.