# Agentic Web Accessibility Auditing: A Criterion-Specific Framework for Translating WCAG Requirements into Assessments

arXiv: https://arxiv.org/abs/2609.09379 · HTML: https://mlsanity.com/paper/2609.09379/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 9 of 9)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [AXNav: Replaying Accessibility Tests from Natural Language](https://mlsanity.com/paper/2310.02424/) (2023)
- [Accessibility in Software Practice: A Practitioner's Perspective](https://mlsanity.com/paper/2103.08778/) (2021)
- [AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Code](https://mlsanity.com/paper/2507.19549/) (2025)
- [ScreenAudit: Detecting Screen Reader Accessibility Errors in Mobile Apps Using Large Language Models](https://mlsanity.com/paper/2504.02110/) (2025)
- [Towards Scalable Web Accessibility Audit with MLLMs as Copilots](https://mlsanity.com/paper/2511.03471/) (2025)
- [TaskAudit: Detecting Functiona11ity Errors in Mobile Apps via Agentic Task Execution](https://mlsanity.com/paper/2510.12972/) (2025)
- [LLM Based Web Accessibility Repair: An Empirical Study of Detection, Remediation, and Cost](https://mlsanity.com/paper/2605.27716/) (2026)

## Abstract (arXiv)

Web accessibility auditing requires interpreting diverse requirements and examining interface behavior. Rule-based checks and noninteractive model assessments can miss barriers requiring contextual or interactive evidence. We present an agentic framework that assigns a vision-language agent to each accessibility requirement. Guided by tailored instructions, agents inspect webpages, operate controls, and record evidence supporting their findings. We implement the framework for 40 requirements from the Web Content Accessibility Guidelines (WCAG). To compare detection and cost, we construct a dataset of 250 page-criterion records derived from expert audits across 11 scholarly platforms. Agents recover 67 of 78 reported positive cases (86% recall), compared with 36% for axe-core, a rule-based checker, and 67% for an uncued, noninteractive vision-language model, at lower precision (56%). They recover nine of ten Keyboard and No Keyboard Trap cases missed by both baselines. Together, the framework, implementations, and dataset support automated accessibility auditing grounded in inspectable evidence.