# WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation

arXiv: https://arxiv.org/abs/2510.15306 · HTML: https://mlsanity.com/paper/2510.15306/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [pix2code: Generating Code from a Graphical User Interface Screenshot](https://mlsanity.com/paper/1705.07962/) (2017)
- [OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs](https://mlsanity.com/paper/2504.04030/) (2025)
- [ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation](https://mlsanity.com/paper/2507.04952/) (2025)
- [Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworks](https://mlsanity.com/paper/2505.07473/) (2025)
- [Capabilities of GPT-5 on Multimodal Medical Reasoning](https://mlsanity.com/paper/2508.08224/) (2025)
- [WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks](https://mlsanity.com/paper/2506.01952/) (2025)
- [FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow](https://mlsanity.com/paper/2505.17399/) (2025)
- [Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems](https://mlsanity.com/paper/2502.11098/) (2025)
- [WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation](https://mlsanity.com/paper/2508.16763/) (2025)
- [MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation](https://mlsanity.com/paper/2505.17123/) (2025)
- [OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising](https://mlsanity.com/paper/2412.03577/) (2024)
- [OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent](https://mlsanity.com/paper/2507.02353/) (2025)
- [Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agents](https://mlsanity.com/paper/2507.03326/) (2025)

## Built on by (most cited first, 3 of 3)

- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation](https://mlsanity.com/paper/2604.15309/) (2026)
- [I-WebGenBench : Evaluating Interactivity in LLM-Generated Scientific Web Applications](https://mlsanity.com/paper/2606.00750/) (2026)

## Abstract (arXiv)

Witnessed by the recent advancements on leveraging LLM for coding and multimodal understanding, we present WebGen-V, a new benchmark and framework for instruction-to-HTML generation that enhances both data quality and evaluation granularity. WebGen-V contributes three key innovations: (1) an unbounded and extensible agentic crawling framework that continuously collects real-world webpages and can leveraged to augment existing benchmarks; (2) a structured, section-wise data representation that integrates metadata, localized UI screenshots, and JSON-formatted text and image assets, explicit alignment between content, layout, and visual components for detailed multimodal supervision; and (3) a section-level multimodal evaluation protocol aligning text, layout, and visuals for high-granularity assessment. Experiments with state-of-the-art LLMs and ablation studies validate the effectiveness of our structured data and section-wise evaluation, as well as the contribution of each component. To the best of our knowledge, WebGen-V is the first work to enable high-granularity agentic crawling and evaluation for instruction-to-HTML generation, providing a unified pipeline from real-world data acquisition and webpage generation to structured multimodal assessment.