MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision

• URL: http://arxiv.org/abs/2606.17162v1
• Date: Mon, 15 Jun 2026 18:02:55 GMT
• Title: MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision
• Abstract summary: We propose MemSlides, a hierarchical memory framework for personalized presentation agents.<n>User profile memory stores intent-conditioned profiles for round-0 personalization, working memory carries active preferences and session constraints.<n>MemSlides pairs this memory design with scoped slide-local revision, so targeted updates act on the smallest affected region.
• Score: 50.30742645266505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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