论文测了五个多模态模型做截图转代码填空，发现它们都按重复UI模式猜答案，最强Codex-5.3文字准确率也只有13.89%。
论文提出首个视觉模式完成偏差基准，基于Design2Code数据集的30个网页生成1440张测试截图。评估5个多模态大语言模型，平均偏差率在卡片宽度扰动上达69.78%，文字字号上达80.22%，准确率仅21.17%和7.89%。Codex-5.3表现最佳，但准确率从卡片任务的68.61%降至文字任务的13.89%，Flash-3.0在文字任务上偏差率高达96.11%。噪声、更细微扰动和边界位置会进一步提高偏差率。
Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation
Multimodal large language models (MLLMs) are increasingly used to translate webpage screenshots into front-end code, but repeated UI patterns may sway them toward visually incorrect yet pattern-consistent outputs. In this work, we test how repeated webpage patterns hurt MLLM accuracy on an objective screenshot-to-code fill-in-the-blank task. We introduce the first benchmark for visual pattern-completion bias, where one localized element in a repeated UI pattern is perturbed and the model must recover the masked width or font-size value from the screenshot and HTML context. Starting from 30 webpages curated from the Design2Code dataset, we build 1,440 evaluated screenshots spanning structural card and text-style patterns under standard and noise-overlaid conditions. We evaluate five frontier MLLMs and find that all are strongly biased toward the repeated baseline. Mean bias rate reaches 69.78% on card-width perturbations and 80.22% on text font-size perturbations, while mean accuracy is only 21.17% and 7.89%, respectively. Codex-5.3 performs best but still drops from 68.61% accuracy on cards to 13.89% on text, while Flash-3.0 reaches 96.11% bias on text. Noise, subtler perturbations, and boundary positions further increase bias rate. Reasoning analysis further shows that greater reasoning effort correlates with lower bias, yet qualitative evidence reveals that models can identify the anomalous element and still override it with the pattern-consistent answer. Our results identify a concrete failure mode in multimodal code generation and show that its severity is strongly associated with visual saliency