APD-Agents：一种由大语言模型驱动的多智能体协作框架，用于自动化页面设计
APD-Agents: A Large Language Model-Driven Multi-Agents Collaborative Framework for Automated Page Design
AI总结：
针对移动应用页面设计耗时、协作难等问题，提出基于LLM的多智能体协作框架APD-agents，通过五个智能体分工协作完成页面设计任务，在RICO数据集上实现了最先进性能。
摘要：
布局设计是开发移动应用页面的关键步骤。然而，设计师制作满意的设计非常耗时：他们需要考虑页面上呈现哪些控件和内容，然后反复调整其大小、位置和样式以获得更好的美观性和结构。尽管现在许多设计软件可以帮助执行这些重复任务，但有效使用它们需要大量培训。此外，跨应用页面的协作设计需要额外时间来对齐标准并确保样式一致。在这项工作中，我们提出了APD-agents，一种由大语言模型（LLM）驱动的多智能体框架，用于移动应用中的自动化页面设计。我们的框架包含OrchestratorAgent、SemanticParserAgent、PrimaryLayoutAgent、TemplateRetrievalAgent和RecursiveComponentAgent。在接收到用户对页面的描述后，OrchestratorAgent可以动态指导其他智能体完成用户的设计任务。具体而言，SemanticParserAgent负责将用户对页面内容的描述转换为结构化数据；PrimaryLayoutAgent可以生成该页面的初始粗粒度布局；TemplateRetrievalAgent可以获取语义相关的少样本示例并提高布局生成的质量；此外，RecursiveComponentAgent可用于决定如何为布局中的每个元素递归生成其包含的所有细粒度子元素。我们的工作充分利用了大模型驱动的多智能体系统的自动协作能力。在RICO数据集上的实验结果表明，我们的APD-agents实现了最先进的性能。
英文摘要：
Layout design is a crucial step in developing mobile app pages. However, crafting satisfactory designs is time-intensive for designers: they need to consider which controls and content to present on the page, and then repeatedly adjust their size, position, and style for better aesthetics and structure. Although many design software can now help to perform these repetitive tasks, extensive training is needed to use them effectively. Moreover, collaborative design across app pages demands extra time to align standards and ensure consistent styling. In this work, we propose APD-agents, a large language model (LLM) driven multi-agent framework for automated page design in mobile applications. Our framework contains OrchestratorAgent, SemanticParserAgent, PrimaryLayoutAgent, TemplateRetrievalAgent, and RecursiveComponentAgent. Upon receiving the user's description of the page, the OrchestratorAgent can dynamically can direct other agents to accomplish users' design task. To be specific, the SemanticParserAgent is responsible for converting users' descriptions of page content into structured data. The PrimaryLayoutAgent can generate an initial coarse-grained layout of this page. The TemplateRetrievalAgent can fetch semantically relevant few-shot examples and enhance the quality of layout generation. Besides, a RecursiveComponentAgent can be used to decide how to recursively generate all the fine-grained sub-elements it contains for each element in the layout. Our work fully leverages the automatic collaboration capabilities of large-model-driven multi-agent systems. Experimental results on the RICO dataset show that our APD-agents achieve state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
