# TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity

arXiv: https://arxiv.org/abs/2610.10374 · HTML: https://mlsanity.com/paper/2610.10374/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Chart understanding and anomaly detection](https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/)

## Builds on (most cited first, 17 of 17)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset](https://mlsanity.com/paper/2403.09029/) (2024)
- [DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation](https://mlsanity.com/paper/2506.06251/) (2025)
- [UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback](https://mlsanity.com/paper/2406.07739/) (2024)
- [FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow](https://mlsanity.com/paper/2505.17399/) (2025)
- [Bridging Design and Development with Automated Declarative UI Code Generation](https://mlsanity.com/paper/2409.11667/) (2024)
- [Advancing vision-language models in front-end development via data synthesis](https://mlsanity.com/paper/2503.01619/) (2025)
- [Automated Reporting of GUI Design Violations for Mobile Apps](https://mlsanity.com/paper/1802.04732/) (2018)
- [UI2Code^N: UI-to-Code Generation as Interactive Visual Optimization](https://mlsanity.com/paper/2511.08195/) (2025)
- [WebVIA: A Web-based Vision-Language Agentic Framework for Interactive and Verifiable UI-to-Code Generation](https://mlsanity.com/paper/2511.06251/) (2025)
- [VisRefiner: Learning from Visual Differences for Screenshot-to-Code Generation](https://mlsanity.com/paper/2602.05998/) (2026)
- [Vision-Guided Iterative Refinement for Frontend Code Generation](https://mlsanity.com/paper/2604.05839/) (2026)
- [WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning](https://mlsanity.com/paper/2510.04097/) (2025)
- [Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation](https://mlsanity.com/paper/2608.24138/) (2026)
- [1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World](https://mlsanity.com/paper/2602.18548/) (2026)
- [UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization](https://mlsanity.com/paper/2606.31732/) (2026)

## Abstract (arXiv)

A key challenge for multimodal large language models (MLLMs) is moving beyond visual recognition to constraint-aware cross-modal reasoning. This involves combining visual cues with information from other modalities to understand elements' relationships under domain-specific rules. This challenge is acutely evident in industrial design-to-code (D2C), which converts user interface (UI) designs into code and requires MLLMs to connect design images with disorganized layer metadata, infer component and layout implementation requirements, and realize them in code under target-library constraints. However, these capabilities remain insufficiently evaluated in realistic industrial settings. To fill this gap, we present TaoD2C-Bench, a benchmark for evaluating MLLMs' ability to generate UI code that satisfies implementation requirements in industrial applications. The TaoD2C dataset consists of 2,861 production designs from 17 commercial platforms with 97,652 expert annotations across four categories: Component, Group, Alignment, and Position. These annotations distinguish required constraints from permitted implementation choices. TaoD2C-Bench defines three tasks: end-to-end UI code generation, requirement inference, and requirement realization. Evaluating eight MLLMs reveals substantial gaps in generating UI code that satisfies implementation requirements, alongside distinct performance profiles in inference and realization. We further show that MLLMs' visual reconstruction ability does not necessarily imply an ability to generate code that meets these requirements. We release TaoD2C to support research on industrial UI code generation.