# WebAccessVL: Violation-Aware VLM for Web Accessibility Source: [WebAccessVL: Violation-Aware VLM for Web Accessibility]() Collection index: [llms.txt]() Data updated: 2026-03-13T05:00:04.336Z Authors: Amber Yijia Zheng, Jae Joong Lee, Bedrich Benes, Raymond A. Yeh Published: 2025-12-19T01:53:22.000Z ## Abstract We present a vision-language model (VLM) that automatically edits website HTML to address violations of the Web Content Accessibility Guidelines 2 (WCAG2) while preserving the original design. We formulate this as a supervised image-conditioned program synthesis task, where the model learns to correct HTML given both the code and its visual rendering. We create WebAccessVL, a website dataset with manually corrected accessibility violations. We then propose a violation-conditioned VLM that further takes the detected violations' descriptions from a checker as input. This conditioning enables an iterative checker-in-the-loop refinement strategy at test time. We conduct extensive evaluation on both open API and open-weight models. Empirically, our method achieves 0.211 violations per website, a 96.0\\% reduction from the 5.34 violations in raw data and 87\\% better than GPT-5. A perceptual study also confirms that our edited websites better maintain the original visual appearance and content. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Website Accessibility Correction on WebAccessVL (test)](): Website Accessibility Correction; WebAccessVL (test); Violations: 0.439. ## Original sources and implementation [Original paper]() [PDF]()