Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification

• URL: http://arxiv.org/abs/2603.26648v2
• Date: Wed, 01 Apr 2026 15:06:02 GMT
• Title: Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification
• Abstract summary: Vision2Web is a hierarchical benchmark for visual website development spanning from static UI-to-code generation to full-stack development.<n>The benchmark comprises a total of 193 tasks across 16 categories, with 918 prototype images and 1,255 test cases.<n>We evaluate multiple visual language models instantiated under different coding-agent frameworks, revealing substantial performance gaps at all task levels.
• Score: 27.677130026020006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models have improved the capabilities of coding agents, yet systematic evaluation of complex, end-to-end website development remains limited. To address this gap, we introduce Vision2Web, a hierarchical benchmark for visual website development, spanning from static UI-to-code generation, interactive multi-page frontend reproduction, to long-horizon full-stack website development. The benchmark is constructed from real-world websites and comprises a total of 193 tasks across 16 categories, with 918 prototype images and 1,255 test cases. To support flexible, thorough and reliable evaluation, we propose workflow-based agent verification paradigm based on two complementary components: a GUI agent verifier and a VLM-based judge. We evaluate multiple visual language models instantiated under different coding-agent frameworks, revealing substantial performance gaps at all task levels, with state-of-the-art models still struggling on full-stack development.

Related papers

• CAP: A Scalable Benchmark for Evaluating Cross-Site Browser Agents with Complex Actions and Perception [22.620664364045286]
  Large language models are increasingly deployed as autonomous agents that interact with the web through browsers.<n>We introduce CAP, a scalable benchmark for evaluating browser agents on cross-site, human-like web tasks.<n>We construct 420 tasks across 108 real-world websites and 24 domains under careful quality control.
  arXiv  Detail & Related papers  (2026-08-09T01:08:19Z)
• GTA: Generating Long-Horizon Tasks for Web Agents at Scale [82.43869456830664]
  We introduce a scalable framework, GTA, that integrates crawling, retrieval-based seeding, in-context generation, and automated quality control.<n>We instantiate the pipeline on over 50 websites covering e-commerce, government, forums, and news, with multilingual and multi-hop coverage.<n>Our contributions are three-fold: (i) formalizing multi-hop web-agent task generation, (ii) proposing an efficient and validated pipeline for automatic data creation, and (iii) releasing a dynamic benchmark with reproducible evaluation.
  arXiv  Detail & Related papers  (2026-05-28T01:05:50Z)
• MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation [99.19991374550729]
  MM-WebAgent is a hierarchical agentic framework for multimodal webpage generation.<n>It coordinates AIGC-based element generation through hierarchical planning and iterative self-reflection.
  arXiv  Detail & Related papers  (2026-04-16T17:59:49Z)
• FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedback [92.67587639164908]
  We present FronTalk, a benchmark for front-end code generation with multi-modal feedback.<n>We focus on the front-end development task and curate FronTalk, a collection of 100 multi-turn dialogues.<n> Evaluation of 20 models reveals two key challenges that are under-explored systematically in the literature.
  arXiv  Detail & Related papers  (2025-12-05T23:28:09Z)
• WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation [12.981748587257194]
  WebGen-V is a new benchmark and framework for instruction-to-HTML generation that enhances data quality and evaluation.<n>WebGen-V contributes three key innovations: (1) an unbounded and agentic crawling framework that continuously collects real-world webpages; (2) a structured, section-wise data representation that integrates metadata, localized UI screenshots, andformatted text and image assets; and (3) a section-level multimodal evaluation protocol aligning text, layout, and visuals for high-granularity assessment.
  arXiv  Detail & Related papers  (2025-10-17T04:37:37Z)
• Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development [34.560333810255464]
  We introduce TDDev, the first test-driven development framework for end-to-end full-stack web application generation.<n>Given a natural language description or design image, TDDev automatically derives executable test cases, generates front-end and back-end code, and simulates user interactions.<n>Our framework addresses key challenges in full-stack automation, including underspecified user requirements, complex interdependencies among multiple files, and the need for both functional correctness and visual fidelity.
  arXiv  Detail & Related papers  (2025-09-29T16:18:19Z)
• IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video? [56.33950760097989]
  IWR-Bench is a novel benchmark for evaluating the capabilities of Large Vision-Language Models (LVLMs) in interactive webpage reconstruction from video.<n>IWR-Bench comprises 113 meticulously curated tasks from 100 real-world websites, with 1,001 actions.<n>This benchmark evaluates models on two fundamental challenges: comprehensive multi-modal reasoning to infer interaction logic from video and assets, and advanced code generation to translate this logic into functional code.
  arXiv  Detail & Related papers  (2025-09-29T12:38:06Z)
• WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code [57.45181837786448]
  Multimodal Large Language Models (MLLMs) have the potential to act as AI software engineers capable of executing complex web application development.<n>Existing benchmarks usually fail to provide an assessment of sub-capabilities and focus solely on webpage generation outcomes.<n>We propose WebUIBench, a benchmark systematically designed to evaluate MLLMs in four key areas: WebUI Perception, HTML Programming,WebUI-HTML Understanding, and WebUI-to-Code.
  arXiv  Detail & Related papers  (2025-06-09T14:46:02Z)
• VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks [93.85005277463802]
  VisualWebArena is a benchmark designed to assess the performance of multimodal web agents on realistic tasks. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives.
  arXiv  Detail & Related papers  (2024-01-24T18:35:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.