While Large Language Models (LLMs) can now automate many parts of scientific writing, architectural figure generation faces a critical modality bottleneck because image outputs are inherently difficult to edit, verify, and integrate into text-native LLM workflows. To enable fully text-driven synthesis, we propose a new task: generating architectural figures as pure HTML/SVG source code. Furthermore, we introduce FigureCode, a novel benchmark that evaluates structural fidelity and visual properties through code-based evaluation. Our benchmark improves agreement with human judgments by 0.35 Kendall’s τ over prior VLM-based methods. Moreover, the figure-as-code paradigm improves edit-instruction success rate by 30% and retrieval F1 score by 25.3% over baselines, while achieving 66.1% WCAG/ARIA accessibility coverage.

In the pursuit of developing Large Language Models (LLMs) that adhere to societal standards, it is imperative to detect the toxicity in the generated text. The majority of existing toxicity metrics rely on encoder models trained on specific toxicity datasets, which are susceptible to out-of-distribution (OOD) problems and depend on the dataset’s definition of toxicity. In this paper, we introduce a robust metric grounded on LLMs to flexibly measure toxicity according to the given definition. We first analyze the toxicity factors, followed by an examination of the intrinsic toxic attributes of LLMs to ascertain their suitability as evaluators. Finally, we evaluate the performance of our metric with detailed analysis. Our empirical results demonstrate outstanding performance in measuring toxicity within verified factors, improving on conventional metrics by 12 points in the F1 score. Our findings also indicate that upstream toxicity significantly influences downstream metrics, suggesting that LLMs are unsuitable for toxicity evaluations within unverified factors.