Artificial Intelligence
arXiv
Yuhang Chen, Tianpeng Lv, Siyi Zhang, Yixiang Yin, Yao Wan, Philip S. Yu, Dongping Chen
17 Oct 2025 • 3 min read
AI-generated image, based on the article abstract
Quick Insight
Turn Your Research Paper into a Living Webpage
What if your scientific paper could talk to you? Paper2Web does exactly that, turning a static PDF into a friendly, interactive webpage anyone can explore. Think of a textbook that suddenly sprouts videos, clickable diagrams, and quick‑look summaries—like a museum exhibit that guides you through each artifact. The new too…
Artificial Intelligence
arXiv
Yuhang Chen, Tianpeng Lv, Siyi Zhang, Yixiang Yin, Yao Wan, Philip S. Yu, Dongping Chen
17 Oct 2025 • 3 min read
AI-generated image, based on the article abstract
Quick Insight
Turn Your Research Paper into a Living Webpage
What if your scientific paper could talk to you? Paper2Web does exactly that, turning a static PDF into a friendly, interactive webpage anyone can explore. Think of a textbook that suddenly sprouts videos, clickable diagrams, and quick‑look summaries—like a museum exhibit that guides you through each artifact. The new tool, PWAgent, reads a paper, pulls out the main ideas, and builds a clean, multimedia‑rich homepage without any coding. It makes sure every section is linked and nothing is missing, much like a chef tasting a dish before it’s served. The result is a site that’s easier to read and helps you remember the key findings, like a short quiz after a lesson. This interactive breakthrough could change how knowledge spreads, making science as easy to browse as a social‑media post. Imagine the world where every discovery lives online, ready to inspire the next curious mind. It’s a new way to share research with the world.
Article Short Review
Revolutionizing Academic Web Presence: A Deep Dive into Paper2Web and PWAgent
The scientific community constantly seeks more effective ways to disseminate research, moving beyond static documents to dynamic, interactive platforms. This paper addresses significant limitations in current methods for generating academic project websites. It introduces Paper2Web, a novel benchmark and multi-dimensional evaluation framework specifically designed for academic webpage generation. The research also unveils PWAgent, an innovative autonomous pipeline engineered to transform scientific papers into engaging, multimedia-rich academic homepages. This agent iteratively refines content and layout using advanced Model Context Protocol (MCP) tools. Experimental results demonstrate that PWAgent consistently outperforms existing baselines, achieving superior completeness, connectivity, and holistic quality while maintaining cost-efficiency.
Critical Evaluation: Advancing Scientific Communication
Strengths: Pioneering Comprehensive Evaluation and Automation
A primary strength lies in the introduction of Paper2Web, a much-needed comprehensive benchmark and evaluation framework. This framework provides a robust, multi-dimensional approach to assessing academic webpage quality, incorporating rule-based metrics like Connectivity and Completeness, alongside human-verified LLM-as-a-Judge evaluations for interactivity and aesthetics, and PaperQuiz for knowledge retention. This holistic suite significantly elevates research standards. The PWAgent pipeline itself represents a substantial methodological innovation. Its autonomous, iterative refinement process, powered by Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs), effectively tackles the complex challenge of converting dense scientific content into engaging, layout-aware web experiences. PWAgent’s demonstrated superior performance against various baselines, coupled with its cost-efficiency, highlights its practical utility and potential to revolutionize academic project presentation.
Weaknesses: Considerations for Future Development
While compelling, certain aspects warrant further consideration. The reliance on LLM-as-a-Judge for holistic evaluation, though innovative, introduces subjectivity and potential bias, impacting generalizability of aesthetic and interactivity assessments. Although PWAgent demonstrates superior performance, the complexity of its multi-agent framework and iterative refinement process, involving Model Context Protocol (MCP) tools, might present challenges in interpretability or fine-tuning for highly specialized academic domains. Future research could explore long-term maintenance and update mechanisms for dynamically generated pages as underlying papers evolve. Additionally, while cost-efficiency is highlighted, a deeper dive into computational resources for large-scale deployment would be beneficial.
Conclusion: A Paradigm Shift in Academic Web Presence
This paper makes a substantial contribution to scientific communication by addressing a critical gap in effective research dissemination. By introducing the Paper2Web benchmark and the highly effective PWAgent pipeline, the authors provide both a robust evaluation standard and a powerful tool for creating interactive, multimedia-rich academic homepages. PWAgent’s demonstrated ability to consistently outperform existing methods in terms of quality and cost-efficiency positions it as a game-changer for researchers seeking to enhance their online presence and engage a broader audience. This work not only pushes the boundaries of automated content generation but also sets a new precedent for how scientific projects can be presented, ultimately fostering greater accessibility and impact of academic research.
Article Comprehensive Review
Revolutionizing Academic Research Dissemination: A Deep Dive into Paper2Web and PWAgent
The effective dissemination of scientific research is paramount for advancing knowledge and fostering collaboration. However, current methods for creating academic project websites often fall short, struggling to present core content clearly, enable intuitive navigation, or offer interactive experiences. Traditional approaches, whether through direct Large Language Model (LLM) generation, rigid templates, or simple HTML conversions, frequently lack the sophistication required for layout-aware and engaging online presences. This challenge has highlighted a significant gap: the absence of a comprehensive evaluation suite to properly assess the quality and effectiveness of academic webpage generation. Into this void steps a groundbreaking initiative, introducing both a novel benchmark and an autonomous pipeline designed to transform how scientific papers are presented online. This work not only defines a new standard for academic web content but also offers a practical, high-performing solution to a long-standing problem in scientific communication.
Overview
This pivotal research introduces Paper2Web, a pioneering benchmark dataset and a multi-dimensional evaluation framework specifically designed for academic webpage generation. The core objective is to overcome the inherent limitations of existing methods, such as those relying on direct LLM outputs, PDF conversions, or basic HTML transformations, which often fail to produce interactive and aesthetically balanced academic sites. Complementing this framework, the paper presents PWAgent, an innovative autonomous pipeline engineered to convert scientific papers into dynamic, multimedia-rich academic homepages. PWAgent employs an iterative refinement process, leveraging Model Context Protocol (MCP) tools to meticulously enhance content emphasis, visual balance, and overall presentation quality. The evaluation framework itself is robust, incorporating rule-based metrics like Connectivity and Completeness, alongside a human-verified LLM-as-a-Judge component that assesses interactivity, aesthetics, and informativeness, culminating in PaperQuiz for knowledge retention. Experimental results unequivocally demonstrate that PWAgent consistently outperforms various end-to-end baselines, including template-based webpages and arXiv/alphaXiv versions, achieving superior quality at a remarkably low cost, thereby establishing a new Pareto-front in academic webpage generation.
Critical Evaluation
The introduction of Paper2Web and PWAgent marks a significant advancement in the realm of scientific communication and digital scholarship. This work addresses a critical need within the academic community, offering both a robust framework for assessment and a high-performing solution for content generation. A thorough critical evaluation reveals numerous strengths, alongside potential weaknesses and important implications for future research and practice.
Strengths
One of the most compelling strengths of this research lies in its comprehensive approach to a previously underserved area: the automated generation of high-quality academic project websites. The introduction of Paper2Web as a benchmark dataset and multi-dimensional evaluation framework is a monumental contribution. Prior to this, a standardized method for assessing the effectiveness of academic webpage generation was conspicuously absent, leaving researchers to rely on subjective judgments or incomplete metrics. Paper2Web fills this void by providing a structured, objective means to evaluate critical aspects such as content completeness, navigational connectivity, and overall user experience. This framework is particularly strong due to its multi-faceted nature, integrating rule-based metrics with sophisticated AI-driven and human-verified assessments.
The evaluation suite’s design is another significant strength, encompassing three crucial dimensions. Firstly, Connectivity and Completeness are assessed using Large Language Model analysis, ensuring that the generated pages contain all essential information and link coherently. Secondly, a Holistic Evaluation is performed via a Human/Multimodal Large Language Model-as-a-Judge, which provides nuanced feedback on interactivity, aesthetic appeal, and overall informativeness—qualities often overlooked by purely quantitative metrics. This hybrid approach mitigates the subjectivity inherent in human judgment while leveraging the advanced analytical capabilities of modern LLMs. Thirdly, the inclusion of PaperQuiz, which measures paper-level knowledge retention, directly addresses the ultimate goal of academic dissemination: effective knowledge transfer. This innovative metric provides a tangible measure of how well the generated webpage communicates the core findings of the research, moving beyond superficial evaluations to assess genuine understanding.
The innovative architecture of PWAgent stands out as a core strength. Unlike simpler, direct conversion methods, PWAgent is an autonomous, multi-agent framework that employs an iterative refinement process. This sophisticated pipeline involves Paper Decomposition, Multi-modal Content Platform (MCP) Ingestion, and Agent-driven Iterative Refinement using both LLMs and MLLMs. This iterative approach allows PWAgent to dynamically adjust and optimize both the content and the layout, ensuring that the final output is not only informative but also visually appealing and well-structured. The use of MCP tools specifically designed to enhance emphasis, balance, and presentation quality demonstrates a deep understanding of effective web design principles, moving beyond mere content extraction to genuine content curation and presentation.
Furthermore, the empirical evidence supporting PWAgent’s performance is exceptionally strong. The experiments show that PWAgent consistently and significantly outperforms a wide array of end-to-end baselines, including advanced models like GPT-4o, Gemini-2.5-Flash, DeepSeek-V3.2-Exp, and Qwen3-Coder-480B-A35B, as well as traditional template-based webpages and arXiv/alphaXiv versions. This superior performance is observed across critical metrics such as completeness, connectivity, and holistic quality. The fact that PWAgent achieves this while maintaining a low operational cost is a remarkable achievement, positioning it at the Pareto-front in academic webpage generation. This combination of high quality and cost-efficiency makes PWAgent a highly practical and attractive solution for researchers and institutions alike.
Finally, the explicit focus on generating interactive and multimedia-rich academic homepages directly addresses a key limitation of current academic dissemination practices. By enabling dynamic content and engaging visual elements, PWAgent enhances user engagement and facilitates a deeper understanding of complex research. The ability to produce pages with superior structural integrity and aesthetic balance, as highlighted in the analysis, ensures that the generated websites are not just functional but also professional and inviting, thereby maximizing their potential impact.
Weaknesses and Caveats
Despite its numerous strengths, the proposed framework and agent also present certain weaknesses and areas that warrant further consideration. One potential caveat lies in the inherent reliance on Large Language Models and Multimodal Large Language Models for both content generation and evaluation. While these models offer unprecedented capabilities, their performance is intrinsically linked to their training data and architectural biases. This dependency could introduce subtle inaccuracies, hallucinations, or perpetuate existing biases present in the vast datasets they were trained on. The “LLM-as-a-Judge” component, while human-verified, still operates within the confines of the LLM’s understanding and interpretation, which might not always perfectly align with human nuances, especially concerning subjective aspects like aesthetics and informativeness.
Another area for consideration is the generalizability of the Paper2Web benchmark dataset. While comprehensive, the specific composition of the dataset and its representativeness across the vast diversity of academic fields and paper types could be a limitation. Different disciplines have varying conventions for presenting research, and a benchmark primarily trained or evaluated on a specific subset of papers might not perform optimally for highly specialized or interdisciplinary research. Further validation across a broader spectrum of academic domains would strengthen its universal applicability.
The complexity of PWAgent’s multi-agent framework, while a strength in terms of capability, could also pose challenges. An autonomous pipeline involving Paper Decomposition, MCP Ingestion, and iterative refinement, while efficient in its final output, might be computationally intensive during its development and initial setup phases. Although the paper emphasizes low operational cost, the resources required for training, fine-tuning, and maintaining such a sophisticated system could be substantial, potentially limiting its accessibility for smaller research groups or institutions without significant computational infrastructure.
Furthermore, while the paper focuses on generating “academic project homepages,” the scope of this definition might be somewhat narrow. It is unclear how well PWAgent’s capabilities would translate to broader applications, such as generating comprehensive institutional websites, personal academic portfolios, or dynamic research group portals that require more extensive customization, integration with external databases, or long-term content management systems. The iterative refinement process is excellent for initial generation, but the long-term maintenance and update mechanisms for these generated pages as research evolves or new findings emerge are not explicitly detailed. Manual intervention might still be necessary for ongoing content updates, which could diminish the “autonomous” advantage over time.
Finally, while the PaperQuiz metric is innovative for assessing knowledge retention, the methodology for its design and validation would benefit from further elaboration. Ensuring that the quiz questions are truly representative of the paper’s core knowledge and are not susceptible to superficial understanding or LLM-generated answers is crucial for its reliability as an evaluation tool. The robustness of this metric against potential gaming or misinterpretation by the LLM-as-a-Judge component needs continuous scrutiny.
Implications and Future Directions
The implications of Paper2Web and PWAgent are profound, promising to significantly reshape the landscape of academic communication and research dissemination. The most immediate impact is the potential for enhanced research dissemination and accessibility. By enabling researchers to effortlessly create high-quality, interactive, and multimedia-rich project websites, this work can dramatically improve how academic findings are presented and consumed. This increased accessibility can foster broader engagement from both the scientific community and the general public, accelerating the pace of scientific discovery and its societal impact.
This research also heralds a significant step towards the democratization of web development for academics. Many researchers lack the specialized skills or resources required to build professional-grade websites. PWAgent effectively lowers this barrier, allowing academics to focus on their core research while an autonomous agent handles the complexities of web design and content presentation. This could empower individual researchers and smaller labs to establish a strong online presence, leveling the playing field with larger, better-funded institutions.
The introduction of the Paper2Web benchmark could establish new standards for academic web presence. As the framework gains traction, it could become a widely accepted metric for evaluating the quality and effectiveness of academic project websites. This standardization would encourage developers and researchers to strive for higher quality in their online dissemination efforts, ultimately benefiting the entire academic ecosystem. It also opens avenues for competitive development, where future AI agents could be benchmarked against Paper2Web to continually push the boundaries of automated web generation.
Furthermore, PWAgent showcases the immense potential of advanced AI in scientific communication. The multi-agent framework, iterative refinement, and sophisticated use of LLMs and MLLMs demonstrate how artificial intelligence can automate complex, creative tasks that traditionally required significant human effort and expertise. This paradigm shift could extend beyond project websites to other forms of scientific communication, such as automated report generation, interactive data visualizations, or even personalized learning modules derived directly from research papers.
Finally, this work has significant implications for the Open Science movement. By making research findings more accessible, interactive, and engaging through high-quality web presentations, PWAgent directly contributes to the principles of open science, promoting transparency, collaboration, and broader public understanding of scientific endeavors. Future research could explore integrating PWAgent with open-access repositories and publishing platforms to create a seamless pipeline from paper submission to interactive web presence, further solidifying the commitment to open and accessible knowledge.
Conclusion
The introduction of Paper2Web and PWAgent represents a transformative leap forward in the field of academic research dissemination. By meticulously addressing the limitations of existing methods for creating academic project websites, this research provides both a robust, multi-dimensional evaluation framework and an exceptionally high-performing autonomous solution. The Paper2Web benchmark offers a much-needed standardized approach to assessing the quality of academic web content, incorporating innovative metrics that span connectivity, completeness, holistic user experience, and crucial knowledge retention. This comprehensive evaluation suite is a significant contribution in itself, setting a new bar for how academic online presences are judged.
At the heart of this innovation is PWAgent, an autonomous pipeline that leverages a sophisticated multi-agent architecture and iterative refinement processes to convert scientific papers into interactive, multimedia-rich homepages. Its ability to consistently outperform a wide array of advanced baselines across multiple quality metrics, all while maintaining remarkable cost-efficiency, positions it as a groundbreaking tool. PWAgent’s capacity to generate aesthetically balanced, structurally sound, and highly informative websites directly tackles the challenges of engaging content presentation and intuitive navigation that have long plagued academic web development.
Ultimately, this work holds immense value for the scientific community. It not only streamlines the process of creating compelling online representations of research but also significantly enhances the accessibility and impact of academic work. By democratizing high-quality web development and setting new standards for digital scholarship, Paper2Web and PWAgent are poised to revolutionize how scientific knowledge is shared, understood, and engaged with, fostering a more connected and informed global academic landscape. This research is a testament to the power of AI in transforming scientific communication, paving the way for a future where every academic project can have a dynamic, engaging, and easily discoverable online presence.