# Generative UI: LLMs are Effective UI Generators

arXiv: https://arxiv.org/abs/2604.09577 · HTML: https://mlsanity.com/paper/2604.09577/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [pix2code: Generating Code from a Graphical User Interface Screenshot](https://mlsanity.com/paper/1705.07962/) (2017)
- [UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs](https://mlsanity.com/paper/2505.09904/) (2025)

## Built on by (most cited first, 8 of 8)

- [BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery](https://mlsanity.com/paper/2606.20997/) (2026)
- [Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA](https://mlsanity.com/paper/2608.09819/) (2026)
- [EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants](https://mlsanity.com/paper/2608.29387/) (2026)
- [MAIC-UI: Making Interactive Courseware with Generative UI](https://mlsanity.com/paper/2604.25806/) (2026)
- [Beyond a Single Judge: The Evidence-Grounded, Social-Weighted Persona Panel for Generative UI Evaluation](https://mlsanity.com/paper/2607.28439/) (2026)
- [SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluation](https://mlsanity.com/paper/2608.22390/) (2026)
- [Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?](https://mlsanity.com/paper/2609.26208/) (2026)
- [Task-State Governance: A Virtual Machine for Verifiable and Recoverable GUI Agent Execution](https://mlsanity.com/paper/2610.20100/) (2026)

## Abstract (arXiv)

AI models excel at creating content, but typically render it with static, predefined interfaces. Specifically, the output of LLMs is often a markdown "wall of text". Generative UI is a long standing promise, where the model generates not just the content, but the interface itself. Until now, Generative UI was not possible in a robust fashion. We demonstrate that when properly prompted and equipped with the right set of tools, a modern LLM can robustly produce high quality custom UIs for virtually any prompt. When ignoring generation speed, results generated by our implementation are overwhelmingly preferred by humans over the standard LLM markdown output. In fact, while the results generated by our implementation are worse than those crafted by human experts, they are at least comparable in 50% of cases. We show that this ability for robust Generative UI is emergent, with substantial improvements from previous models. We also create and release PAGEN, a novel dataset of expert-crafted results to aid in evaluating Generative UI implementations, as well as the results of our system for future comparisons. Interactive examples can be seen at https://generativeui.github.io