DataEvolver：自我演化多智能體數據建構用於富含文字的圖像生成

摘要

富文本图像生成是图像生成中最具挑战性的场景之一，因为模型必须同时生成视觉上逼真的图像，并呈现清晰可读、语义对齐且布局一致的文本。现有的数据管道通常遵循静态爬取-过滤-冻结范式：收集候选样本，进行一次过滤，然后将通过的数据冻结用于训练。然而，被拒绝的样本通常被丢弃，尽管它们往往包含有用的失败信号，例如OCR错误和语义不匹配。因此，后续的构建轮次可能会重复相同的失败模式。为解决这些局限，我们提出DataEvolver——一个用于富文本图像数据构建的自我演化多智能体框架。DataEvolver将数据构建视为反饋驱动的构建策略演化：检索器收集候选样本，验证器分配质量评分与拒绝原因，评论员将轮次级反馈总结为语义反馈，生成器通过目标合成填补覆盖不足的区域。更新后的反馈记忆随后指导下一轮构建。在富文本图像生成基准上的实验表明，在匹配数据预算下，DataEvolver比固定数据集基线产生更有用的训练数据。在PixArt-alpha的0.75M规模下，DataEvolver在TextScenesHQ上将OCR-F1分数相较于最强基线提升85.3%，在LongTextBench上提升35.3%。这些改进在两个评估基准上保持一致，并能迁移至Show-o2，表明DataEvolver的优势不依赖于单一下游生成器。这些结果表明，被拒绝的样本能为改进富文本图像数据构建提供可操作的反馈。

English

Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic images and render legible, semantically aligned, and layout-consistent text. Existing data pipelines usually follow a static crawl-filter-freeze paradigm. They collect candidate samples, filter them once, and freeze the accepted data for training. However, rejected samples are usually discarded, although they often contain useful failure signals such as OCR errors and semantic mismatches. As a result, later construction rounds may repeat the same failure modes. To address these limitations, we propose DataEvolver, a self-evolving multi-agent framework for text-rich image data construction. DataEvolver treats data construction as feedback-driven construction policy evolution. A Retriever collects candidate samples, a Verifier assigns quality scores and rejection causes, a Critic summarizes round-level feedback into semantic feedback, and a Generator completes under-covered regions through targeted synthesis. The updated feedback memory then guides the next construction round. Experiments on text-rich image generation benchmarks show that DataEvolver produces more useful training data than fixed-dataset baselines under matched data budgets. At the 0.75M scale on PixArt-alpha, DataEvolver improves OCR-F1 over the strongest baseline by 85.3 percent on TextScenesHQ and 35.3 percent on LongTextBench. The improvements are consistent across both evaluated benchmarks and also transfer to Show-o2, indicating that the benefit of DataEvolver is not tied to a single downstream generator. These results suggest that rejected samples can provide actionable feedback for improving text-rich image data construction.