Arjun Mishra | Searcharxiv

arXiv2026

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

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.

cs.HC↗

arXiv2024

Contrastive Adversarial Training for Unsupervised Domain Adaptation

Domain adversarial training has shown its effective capability for finding domain invariant feature representations and been successfully adopted for various domain adaptation tasks. However, recent advances of large models (e.g., vision transformers) and emerging of complex adaptation scenarios (e.g., DomainNet) make adversarial training being easily biased towards source domain and hardly adapted to target domain. The reason is twofold: relying on large amount of labelled data from source domain for large model training and lacking of labelled data from target domain for fine-tuning. Existing approaches widely focused on either enhancing discriminator or improving the training stability for the backbone networks. Due to unbalanced competition between the feature extractor and the discriminator during the adversarial training, existing solutions fail to function well on complex datasets. To address this issue, we proposed a novel contrastive adversarial training (CAT) approach that leverages the labeled source domain samples to reinforce and regulate the feature generation for target domain. Typically, the regulation forces the target feature distribution being similar to the source feature distribution. CAT addressed three major challenges in adversarial learning: 1) ensure the feature distributions from two domains as indistinguishable as possible for the discriminator, resulting in a more robust domain-invariant feature generation; 2) encourage target samples moving closer to the source in the feature space, reducing the requirement for generalizing classifier trained on the labeled source domain to unlabeled target domain; 3) avoid directly aligning unpaired source and target samples within mini-batch. CAT can be easily plugged into existing models and exhibits significant performance improvements.

cs.LG↗