用参考页面生成更精准的落地页，避免模板化和虚假宣传。
LandingAgent: A Reference-Annotated Dataset and Agentic Generation Framework for Landing Pages
- 通过分析真实页面抽象出可复用的布局与风格模式。
- 三阶段智能流程：定位目标、构建参考式草图、批判性优化页面。
- 相比直接提示，生成内容更贴合目标、结构更丰富、视觉更优。
落地页是目标导向的网页界面，需清晰传达特定价值主张，并合理组织信息流、视觉层次与行动号召（CTA）。尽管大语言模型能从自然语言提示生成看似合理的网页代码，但直接生成常导致通用模板和无依据的说服性陈述。本文研究基于目标和参考的落地页生成，即系统需利用真实页面中的可复用模式，为新目标生成可执行页面，而不直接复制。我们提出LandingBench，一个参考-画像数据集，将真实落地页抽象为段落序列、布局模式、语气描述、视觉重点和CTA结构。基于此，我们设计LandingAgent，一种三阶段智能体框架：先分析目标画像，再构建参考引导的页面草图，最后通过批判性反馈进行精炼优化。我们在忠实度、简洁性、可读性、美学性和结构多样性五个维度评估该框架，结果表明其显著提升目标对齐度、呈现质量与布局多样性。代码已开源于https://github.com/IAURAI/LandingAgent。
原文摘要 · Abstract (English)
Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hierarchy, and calls to action (CTA). Although large language models can generate plausible webpage code from natural-language prompts, direct generation often yields generic templates and unsupported persuasive claims. We study target-grounded, reference-guided landing-page generation, where a system must create an executable page for a new target by adapting reusable patterns from real pages without copying them. We introduce LandingBench, a reference-profile dataset that abstracts real landing pages into section sequences, layout patterns, tone descriptors, visual emphasis, and CTA structure. Building on LandingBench, we propose LandingAgent, a three-phase agentic framework that profiles the target, constructs a reference-guided wireframe, and refines the page through critique-guided polishing. We evaluate LandingAgent against direct prompting on faithfulness, conciseness, readability, aesthetics, and structural diversity. Experiments show improved target grounding, presentation quality, and layout diversity. Code is available at https://github.com/IAURAI/LandingAgent.
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