Venus: Compiling an Aesthetic Standard into Supervision for Web Coding

Web coding models now meet basic functional requirements, yet still struggle to turn a user's subjective aesthetic wishes, a premium look or visual impact, into concrete design. Improving design ability with high-quality web data is bottlenecked by obtaining professional aesthetic supervision at scale: the criteria for design quality are usually implicit in a designer's experience and resist conversion into executable rules, while page-by-page human review is reliable but too costly for the data scale training demands. We present Venus, a pipeline for constructing web-coding training data driven by professional design knowledge. Rather than convert aesthetic intuition fully into automatic rules, Venus decomposes it into a multi-dimensional, graded, verifiable design standard and uses that standard to drive a two-stage process of data expansion and supervision construction, so that limited human effort supports building professional aesthetic data at scale. First, high-quality seed pages are expanded along each dimension and a small amount of human review keeps the good ones. Then, guided by the same standard, these pages are degraded in a controlled way, each degradation along one aesthetic dimension, which yields large-scale aesthetic comparison data without any new pairwise human annotation. From this data we distill a local aesthetic evaluation model whose automated review builds a larger high-quality corpus, on which we train a web coding model. On WebDevJudge, a public benchmark of real human preferences over web pages, the Venus evaluation model attains the highest order consistency in the panel and a strict accuracy within the band of the frontier closed-source judges we compare against, and in a blind study it agrees with independent designers at a rate approaching their agreement with one another. An open Qwen-family base trained on the Venus corpus produces web pages of substantially higher design quality than the same base and competitive with frontier systems on public benchmarks. We will open-source the ten-dimension web design standard and the trained models.