Personalization as Inverse Planning: Learning Latent Design Intents for Agentic Slide Generation via Structural Denoising

Slide design requires personalizing both deck themes andpage layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design. Solely relying on prespecified templates oruser verbose instructions, they fail to capture latent design intents, leav-ing Page-level Slide Personalization (PSP) unresolved. To close this gap,this work formulates PSP as an inverse planning problem. We proposeto learn a design intent without assuming any knowledge of the spe-cific executing tools (e.g., PowerPoint, Beamer) being used. However,relinquishing control over these tools makes the problem intractable tooptimize end-to-end. To overcome this, we propose Spire, a principledframework to solve PSP approximately. By intentionally corrupting thevisual structures of clean slides, Spire creates a verifiable task to de-noise the corruption, whereby two agents learn to collaboratively refineexecutable designs via reinforcement learning (RL). We present a proofthat structural denoising is a consistent surrogate for PSP, and that themulti-agent formulation strictly reduces policy gradient variance in RL.Extensive experiments demonstrate the superiority of Spire.