Paper2Web：讓您的論文煥發生機！

摘要

學術項目網站若能清晰呈現核心內容並實現直觀的導航與互動，將更有效地傳播研究成果。然而，現有方法如直接使用大型語言模型（LLM）生成、模板化或直接HTML轉換，均難以產出佈局合理、互動性強的網站，且針對此任務的全面評估體系尚屬空白。本文提出Paper2Web，一個用於評估學術網頁生成的基準數據集與多維度評價框架。該框架整合了基於規則的指標（如連通性、完整性）、經人工驗證的LLM-as-a-Judge（涵蓋互動性、美觀度與信息量），以及PaperQuiz，用於衡量論文層面的知識保留度。我們進一步介紹了PWAgent，這是一條將科學論文轉化為富含多媒體的互動式學術主頁的自動化流程。該代理通過MCP工具迭代優化內容與佈局，提升重點突出、平衡性與展示質量。實驗表明，PWAgent在保持低成本的同時，大幅超越基於模板的網頁及arXiv/alphaXiv版本等端到端基線，實現了學術網頁生成中的帕累托前沿。

English

Academic project websites can more effectively disseminate research when they clearly present core content and enable intuitive navigation and interaction. However, current approaches such as direct Large Language Model (LLM) generation, templates, or direct HTML conversion struggle to produce layout-aware, interactive sites, and a comprehensive evaluation suite for this task has been lacking. In this paper, we introduce Paper2Web, a benchmark dataset and multi-dimensional evaluation framework for assessing academic webpage generation. It incorporates rule-based metrics like Connectivity, Completeness and human-verified LLM-as-a-Judge (covering interactivity, aesthetics, and informativeness), and PaperQuiz, which measures paper-level knowledge retention. We further present PWAgent, an autonomous pipeline that converts scientific papers into interactive and multimedia-rich academic homepages. The agent iteratively refines both content and layout through MCP tools that enhance emphasis, balance, and presentation quality. Our experiments show that PWAgent consistently outperforms end-to-end baselines like template-based webpages and arXiv/alphaXiv versions by a large margin while maintaining low cost, achieving the Pareto-front in academic webpage generation.

Paper2Web：讓您的論文煥發生機！

Paper2Web: Let's Make Your Paper Alive!

摘要

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