# 1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World

arXiv: https://arxiv.org/abs/2602.18548 · HTML: https://mlsanity.com/paper/2602.18548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Builds on (most cited first, 30 of 31)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [ToolACE: Winning the Points of LLM Function Calling](https://mlsanity.com/paper/2409.00920/) (2024)
- [OmniParser for Pure Vision Based GUI Agent](https://mlsanity.com/paper/2408.00203/) (2024)
- [Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs](https://mlsanity.com/paper/2404.05719/) (2024)
- [Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering](https://mlsanity.com/paper/2403.03163/) (2024)
- [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https://mlsanity.com/paper/2509.13313/) (2025)
- [pix2code: Generating Code from a Graphical User Interface Screenshot](https://mlsanity.com/paper/1705.07962/) (2017)
- [Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library](https://mlsanity.com/paper/2506.06122/) (2025)
- [Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs](https://mlsanity.com/paper/2406.20098/) (2024)
- [Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus](https://mlsanity.com/paper/2209.14927/) (2022)
- [WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs](https://mlsanity.com/paper/2404.06369/) (2024)
- [AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation](https://mlsanity.com/paper/2412.18116/) (2024)
- [Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories](https://mlsanity.com/paper/2511.00197/) (2025)
- [Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping](https://mlsanity.com/paper/2410.16232/) (2024)
- [FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow](https://mlsanity.com/paper/2505.17399/) (2025)
- [UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback](https://mlsanity.com/paper/2406.07739/) (2024)
- [LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios](https://mlsanity.com/paper/2508.17692/) (2025)
- [MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs](https://mlsanity.com/paper/2412.15310/) (2024)
- [UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs](https://mlsanity.com/paper/2505.09904/) (2025)
- [MLLM-Based UI2Code Automation Guided by UI Layout Information](https://mlsanity.com/paper/2506.10376/) (2025)
- [IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web](https://mlsanity.com/paper/2409.18980/) (2024)
- [Advancing vision-language models in front-end development via data synthesis](https://mlsanity.com/paper/2503.01619/) (2025)
- [DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models](https://mlsanity.com/paper/2506.13663/) (2025)
- [WebVIA: A Web-based Vision-Language Agentic Framework for Interactive and Verifiable UI-to-Code Generation](https://mlsanity.com/paper/2511.06251/) (2025)
- [LaTCoder: Converting Webpage Design to Code with Layout-as-Thought](https://mlsanity.com/paper/2508.03560/) (2025)
- [Beyond Automation: How UI/UX Designers Perceive AI as a Creative Partner in the Divergent Thinking Stages](https://mlsanity.com/paper/2501.18778/) (2025)
- [Code Aesthetics with Agentic Reward Feedback](https://mlsanity.com/paper/2510.23272/) (2025)
- [WAFFLE: Finetuning Multi-Modal Models for Automated Front-End Development](https://mlsanity.com/paper/2410.18362/) (2024)
- [Designing with Language: Wireframing UI Design Intent with Generative Large Language Models](https://mlsanity.com/paper/2312.07755/) (2023)

## Built on by (most cited first, 1 of 1)

- [TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity](https://mlsanity.com/paper/2610.10374/) (2026)

## Abstract (arXiv)

Design-to-code translates high-fidelity UI designs into executable front-end implementations, but progress remains hard to compare due to inconsistent datasets, toolchains, and evaluation protocols. We introduce 1D-Bench, a benchmark grounded in real e-commerce workflows, where each instance provides a reference rendering and an exported intermediate representation that may contain extraction errors. 1D is short for one day, representing the efficient completion of design-to-code tasks in less than one day. Models take both as input, using the intermediate representation as structural cues while being evaluated against the reference rendering, which tests robustness to intermediate representation defects rather than literal adherence. 1D-Bench requires generating an executable React codebase under a fixed toolchain with an explicit component hierarchy, and defines a multi-round setting in which models iteratively apply component-level edits using execution feedback. Experiments on commercial and open-weight multimodal models show that iterative editing generally improves final performance by increasing rendering success and often improving visual similarity. We further conduct a pilot study on post-training with synthetic repair trajectories and reinforcement learning based editing, and observe limited and unstable gains that may stem from sparse terminal rewards and high-variance file-level updates. The data and scripts used in this study are available in an anonymized repository at https://anonymous.4open.science/r/d2c-benchmark-A9C4/.