Generative UI: LLMs are Effective UI Generators
Not on the map. Why?
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
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 6 of its 47 references that are in the map. First posted on arXiv 24 Feb 2026.
Builds on
6 of 47 references.
References · 6
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2022Fast Inference from Transformers via Speculative Decodingcited by 1,615
- 2022Competition-Level Code Generation with AlphaCodecited by 1,427
- 2023LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Datasetcited by 449
- 2017pix2code: Generating Code from a Graphical User Interface Screenshotcited by 113
- 2025UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designscited by 22
Built on by
Citing papers per year: 2026: 7.
7 papers.
- 2026BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discoverycited by 1
- 2026EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistantscited by 1
- 2026MAIC-UI: Making Interactive Courseware with Generative UIcited by 0
- 2026Beyond a Single Judge: The Evidence-Grounded, Social-Weighted Persona Panel for Generative UI Evaluationcited by 0
- 2026Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRAcited by 0
- 2026SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluationcited by 0
- 2026Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?cited by 0
- 2026EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants29 Aug 2026
- 2026SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluation23 Aug 2026
- 2026Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?11 Aug 2026
- 2026Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA10 Aug 2026
- 2026Beyond a Single Judge: The Evidence-Grounded, Social-Weighted Persona Panel for Generative UI Evaluation30 Jul 2026
- 2026BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery19 Jun 2026
- 2026MAIC-UI: Making Interactive Courseware with Generative UI28 Apr 2026