WebViSE：从开放网页抽取视觉技能，提升 LLM 生成网站的设计质量
WebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation
这篇把全网网站的视觉设计自动抽成可复用技能库，接上 Qwen3.8-Max 这类模型就能让生成的网页好看不少，做网页生成方向的可以看看开源代码。
WebViSE 是一个框架，把成熟网站实现自动抽象为可复用的 Visual Skills 并构建结构化技能库。技能库以多父 DAG 组织，通过 Skill-conditioned Potential Queries 联合演化技能描述与 DAG 结构。用 Qwen3.8-Max 时，自动演化的检索结构仅花约 39.09% 的成本就胜过非层级 LLM 检索基线。WebViSE 可即插即用地注入任意模型，也能为开源模型构建数据合成管线；多项网页开发基准与人工评估显示其提升了生成页面的视觉质量与交互还原度，代码和技能库已在 GitHub 开源。
WebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation
Large language models have made remarkable strides in website development, enabling complete and interactive web experiences to be built from natural-language instructions with far less effort. Yet, non-expert users often struggle to articulate sophisticated visual and interaction designs in natural language. Meanwhile, the open web already contains a vast reservoir of such design knowledge, embodied in mature website implementations. To this end, we introduce Website-derived Visual Skill Extraction (WebViSE), a framework that automatically abstracts these implementations into reusable Visual Skills and builds a structured Skill library. To scale the library, WebViSE organizes its Skills in a multi-parent DAG and jointly evolves Skill descriptions and DAG structure using Skill-conditioned Potential Queries, enabling efficient matching between diverse user instructions and relevant Skills. The auto-evolved structure outperforms non-hierarchical LLM retrieval baselines at only 39.09% of the estimated retrieval cost with Qwen3.8-Max. For any models, WebViSE can be directly injected as plug-and-play guidance, enabling diverse models to produce more visually compelling and aesthetically refined web pages. For open-source models, WebViSE can be used to build a data-synthesis pipeline that internalizes visual design intelligence into model weights. Experiments across multiple web development benchmarks and human evaluation consistently show that WebViSE improves the visual quality and interaction fidelity of artifacts. Overall, our work establishes an automated path for extracting, organizing, and transferring design intelligence from the open web to website-building models. We release all extracted Visual Skills, the associated Skill DAG, and our code at https://github.com/YifeiSun-0710/WebViSE.