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

Personalized presentation generation requires more than conditioning on a current prompt or template: an agent must preserve stable user preferences across tasks, retain newly introduced preferences during multi-turn revision, and carry out local edits without disturbing the rest of the deck. We present MemSlides, which organizes preference and execution memory by lifetime and role. User profile memory stores intent-conditioned preferences and personalizes the initial deck; working memory keeps session constraints active across later edits; and tool memory stores reusable execution experience for local revision. Before generation, intent-matched profiles are reconciled with the current request, with explicit instructions taking precedence over stored preferences. Stable preference signals are consolidated after each session for reuse across tasks. During revision, execution contracts define edit scope and targets, local snapshots guide targeted patches, and coverage checks verify completion before finalization. Across three model families, MemSlides outperforms DeepPresenter and SlideTailor on most persona-alignment dimensions while maintaining competitive general presentation quality. Human evaluation further supports improved preference alignment, while localized-revision experiments show that MemSlides more reliably completes requested changes without disturbing non-target content.