Dynamic Generative UI Framework: A Token-Based Architecture for AI-Native, Personalized, and Statistically Safe Interfaces

Abstract Graphical user interfaces remain dominated by static view hierarchies, rigid component libraries, and application-specific layouts. Large language models can generate content and code, but most deployed interfaces still treat presentation as a fixed artifact rather than a runtime object. We propose the Dynamic Generative UI Framework (D-GUI), an architecture in which the interface is synthesized as a constrained stream of UI Tokens and compiled locally into native surfaces under explicit policy control. The framework separates three planes that are usually entangled in modern UI systems: intent planning (what the user wants), layout synthesis (how the interface should be composed), and interaction state (what the user has already entered or selected). A cloud-based Conductor model generates token programs from user intent and bounded context, while an on-device Decoder compiles those tokens into renderable UI with semantic diffing, cached macro-layouts, progressive rendering, and native pass-through regions for continuous media streams. We formalize a controlled binding relation between visual and interaction state, define a rollback buffer for safe visual undo, and introduce a local privacy boundary that maps raw device signals to abstract accessibility and environment categories before any cloud exchange. Safety is enforced by a mandatory policy layer, sandboxed extensions, deterministic fallback rendering, and audit logs. The result is an implementation-oriented architecture for adaptive interfaces that are more private, more resilient, and more efficient than schema-driven generative UI pipelines. AcknowledgementsThe development of this preprint was significantly assisted by advanced AI systems. Special thanks to Grok (built by xAI), ChatGPT, DeepSeek, and Gemini for their valuable contributions to the conceptual refinement, formalization of the safety envelope, mathematical apparatus, structural improvements, and iterative drafting of the current dense version. Keywords: generative UI, AI-native interfaces, token-based architecture, dynamic UI, privacy-preserving UI, safety envelope, semantic diff rendering, on-device decoding.