Towards Self-Adaptive GenUI Experiences: An Approach, Benchmark & Evaluation Suite

Generative UI (GenUI) requires producing complete interactive experiences rather than static webpages: so the interfaces must remain functional, stateful, and usable across views and interactions. While frontier models generate visually plausible HTML, their outputs frequently fail under interaction, and these failures recur across tasks. We introduce SAGE-Create, a training-free framework that converts verified GUI repairs into reusable multimodal memory. Its Reflect–Refine–Remember loop infers a task specification, generates and executes candidate interfaces, and applies focused repairs that are admitted to memory only when before-and-after replay confirms that the failure is resolved and required behavior is preserved. Central to this loop is a multimodal memory that records how each rule fixed a concrete failure through code patches, screenshots, and interaction traces, together with a learning curriculum that governs rule creation, promotion, and retirement. To evaluate GenUI experiences, we introduce SAGE-Bench, 300 tasks spanning an intent-based taxonomy of 11 experience families and 48 subfamilies, and SAGE-Eval, which measures perceptual alignment, visual layout, and data integrity alongside specification fidelity and execution reliability. SAGE-Create achieves 74.12 on SAGE-Bench, surpassing the strongest baseline by 4.86 points and the no-memory variant by 2.08 points; its frozen memory transfers to other generators and improves WebGen-Bench performance from 54.56 to 57.94. Qualitative examples show clearer task structure and improved responsive layouts. Together, these results support reusable repair memory as a way to improve GenUI experiences across tasks.