MiLDEdit: 基于推理的多层设计文档编辑
MiLDEdit: Reasoning-Based Multi-Layer Design Document Editing
AI总结：
MiLDEdit通过引入基于推理的多层文档编辑代理，实现了对多层设计文档的精准编辑，显著超越现有方法，建立了该领域的首个强基线。
摘要：
现实中的设计文档（例如海报）本质上是多层的，结合了装饰、文本和图像。从自然语言指令中编辑它们需要细粒度、层意识的推理来识别相关层并协调修改。先前的工作大多忽略了多层设计文档编辑，而是专注于单层图像编辑或多层生成，这些方法假设一个扁平画布，缺乏确定要修改什么和在哪里所需的推理能力。为了解决这一缺口，我们引入了多层文档编辑代理（MiLDEAgent），这是一个基于推理的框架，结合了通过强化学习训练的多模态推理器用于逐层理解，以及图像编辑器用于针对性的修改。为了系统地评估这一设置，我们引入了MiLDEBench，一个包含超过20,000个设计文档及其多样编辑指令的人工参与语料库。该基准由特定任务的评估协议MiLDEEval补充，涵盖四个维度，包括指令遵循、布局一致性、美观和文本渲染。在14个开源和2个闭源模型上的大量实验表明，现有方法无法泛化：开源模型经常无法完成多层文档编辑任务，而闭源模型则容易出现格式违规。相比之下，MiLDEAgent实现了强大的层意识推理和精确的编辑，显著优于所有开源基线，并达到与闭源模型相当的性能，从而建立了多层文档编辑的第一个强基线。
英文摘要：
Real-world design documents (e.g., posters) are inherently multi-layered, combining decoration, text, and images. Editing them from natural-language instructions requires fine-grained, layer-aware reasoning to identify relevant layers and coordinate modifications. Prior work largely overlooks multi-layer design document editing, focusing instead on single-layer image editing or multi-layer generation, which assume a flat canvas and lack the reasoning needed to determine what and where to modify. To address this gap, we introduce the Multi-Layer Document Editing Agent (MiLDEAgent), a reasoning-based framework that combines an RL-trained multimodal reasoner for layer-wise understanding with an image editor for targeted modifications. To systematically benchmark this setting, we introduce the MiLDEBench, a human-in-the-loop corpus of over 20K design documents paired with diverse editing instructions. The benchmark is complemented by a task-specific evaluation protocol, MiLDEEval, which spans four dimensions including instruction following, layout consistency, aesthetics, and text rendering. Extensive experiments on 14 open-source and 2 closed-source models reveal that existing approaches fail to generalize: open-source models often cannot complete multi-layer document editing tasks, while closed-source models suffer from format violations. In contrast, MiLDEAgent achieves strong layer-aware reasoning and precise editing, significantly outperforming all open-source baselines and attaining performance comparable to closed-source models, thereby establishing the first strong baseline for multi-layer document editing.
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