Seungwon Lim

Large language models can generate visually coherent web UIs from natural language requests, but they frequently violate Web Content Accessibility Guidelines (WCAG), excluding users with diverse needs and contexts. We address this by introducing A11yn, a post-training framework for web accessibility-aware web UI generation. A11yn converts off-the-shelf WCAG audits into a verifiable web accessibility reward, enabling accessibility alignment without data from dense human annotations. To support training and evaluation, we release UIReq-6.8K, a dataset of 6,800 diverse UI generation instructions, and RealUIReq-300, a benchmark of 300 realistic web UI generation tasks. Experimental results show that A11yn achieves the strongest web accessibility compliance among baselines, reducing the inaccessibility rate by 87.5\% and 58.1\% under two independent WCAG auditors relative to the base model. Unlike prompting-based post-hoc accessibility-correction baselines, A11yn achieves these gains in a single-pass generation setting while maintaining comparable semantic fidelity and visual quality. The code and dataset are available at \url{https://github.com/jeffrobot/A11yn}.

Recent advances in AI agents have expanded their applicability beyond simple task completion to increasingly complex real-world settings. In such settings, the challenge extends beyond solving difficult tasks; it also requires following the behavioral policies or operational procedures that govern how those tasks should be carried out. However, how to systematically train agents for such procedural compliance remains underexplored, especially along two dimensions: (1) how to construct environments for compliance training, and (2) how to provide learning signals that reward compliant behavior. In this work, we introduce a scalable, automated, and verifiable pipeline for jointly generating executable tool-use environments and tasks, each paired with a ground-truth execution path. We further investigate diverse reward design strategies inspired by sequence-matching algorithms to incentivize procedurally compliant behavior, with rewards defined at both the step and trajectory levels. In experiments across diverse benchmarks requiring procedural compliance in tool-use tasks, we find that step-level rewards improve credit assignment by enabling finer-grained advantage estimation in PPO, while trajectory-level rewards, as used in GRPO, yield strong group-relative learning signals when coupled with our environment-specific reward design. Collectively, our results establish that scalable environment construction and compliance-aware reward design are both essential for training agents that are procedurally grounded.