Jiwan Chung

When thinking with images, humans rarely rely on a single glance: they revisit visual evidence while reasoning. In contrast, most Multimodal Language Models encode an image once to key-value cache and then reason purely in text, making it hard to re-ground intermediate steps. We empirically confirm this: as reasoning chains lengthen, models progressively lose focus on relevant regions. We introduce v1, a lightweight extension for active visual referencing via point-and-copy: the model selects relevant image patches and copies their embeddings back into the reasoning stream. Crucially, our point-and-copy mechanism retrieves patches using their semantic representations as keys, ensuring perceptual evidence remains aligned with the reasoning space. To train this behavior, we build v1g, a dataset of 300K multimodal reasoning traces with interleaved grounding annotations. Across multimodal mathematical reasoning benchmarks, v1 consistently outperforms comparable baselines.

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}.

Web agents act through long interaction sequences, yet existing benchmarks evaluate only terminal success, discarding all process information and offering little guidance on improvement. In this work, we conduct a process-level analysis of web agents. We introduce WebStep, a benchmark of 1,800 task instances with controlled difficulty and automatic semantic state tracking. Each website exposes a deterministic semantic MDP alongside the GUI: the agent operates on the interface, while the environment records high-level states and transitions in the background, enabling fine-grained analysis without manual annotation. Based on the semantic trajectory, we first show that process metrics reveal differences invisible to outcome evaluation: three agents whose success rates cluster within 34-37% diverge in exploration reach versus execution accuracy. Then, decomposing by skill characterizes the nature of these differences, exposing opposite per-skill rankings hidden within the same website: e.g., on Q&A, Claude CUA outperforms OpenAI CUA by 30% on navigation actions yet underperforms it by 6.7% on inspection, pinpointing a concrete skill to improve even within a domain. Bifurcation analysis further localizes the decisive error that loses the task and shows that this error is agent-specific rather than shared. Finally, these differences widen as tasks grow harder: success rate is similar on easy tasks but separates sharply as exploration becomes more demanding. Our process-level analysis opens a new avenue in web agent evaluation, providing fine-grained and actionable insight into where and how each agent should be improved. Project page: https://jiwanchung.github.io/webstep