@inproceedings{ma-etal-2026-human,
title = "Human-Agent Collaborative Paper-to-Page Crafting",
author = "Ma, Qianli and
Wang, Siyu and
Yilin, Chen and
Tang, Yinhao and
Yang, Yixiang and
Guo, Chang and
Gao, Bingjie and
Xing, Zhening and
Sun, Yanan and
Zhang, Zhipeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1988/",
pages = "39963--39999",
ISBN = "979-8-89176-395-1",
abstract = "In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building project webpages to make their dense papers accessible. While automation has tackled static slides and posters, the dynamic, interactive nature of webpages has remained an unaddressed challenge. To bridge this gap, we reframe the problem, arguing that the solution lies not in a single command, but in a collaborative, hierarchical process. We introduce $\textbf{AutoPage}$, a novel multi-agent system that embodies this philosophy. AutoPage deconstructs paper-to-page creation into a coarse-to-fine pipeline from narrative planning to multimodal content generation and interactive rendering. To combat AI hallucination, dedicated ``Checker'' agents verify each step against the source paper, while optional human checkpoints ensure the final product aligns perfectly with the author{'}s vision, transforming the system from a mere tool into a powerful collaborative assistant. To rigorously validate our approach, we also construct $\textbf{PageBench}$, the first benchmark for this new task. Experiments show AutoPage not only generates high-quality, visually appealing pages but does so with remarkable efficiency in under 15 minutes for less than {\$}0.1. Code and data will be released."
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<abstract>In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building project webpages to make their dense papers accessible. While automation has tackled static slides and posters, the dynamic, interactive nature of webpages has remained an unaddressed challenge. To bridge this gap, we reframe the problem, arguing that the solution lies not in a single command, but in a collaborative, hierarchical process. We introduce AutoPage, a novel multi-agent system that embodies this philosophy. AutoPage deconstructs paper-to-page creation into a coarse-to-fine pipeline from narrative planning to multimodal content generation and interactive rendering. To combat AI hallucination, dedicated “Checker” agents verify each step against the source paper, while optional human checkpoints ensure the final product aligns perfectly with the author’s vision, transforming the system from a mere tool into a powerful collaborative assistant. To rigorously validate our approach, we also construct PageBench, the first benchmark for this new task. Experiments show AutoPage not only generates high-quality, visually appealing pages but does so with remarkable efficiency in under 15 minutes for less than $0.1. Code and data will be released.</abstract>
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%0 Conference Proceedings
%T Human-Agent Collaborative Paper-to-Page Crafting
%A Ma, Qianli
%A Wang, Siyu
%A Yilin, Chen
%A Tang, Yinhao
%A Yang, Yixiang
%A Guo, Chang
%A Gao, Bingjie
%A Xing, Zhening
%A Sun, Yanan
%A Zhang, Zhipeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ma-etal-2026-human
%X In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building project webpages to make their dense papers accessible. While automation has tackled static slides and posters, the dynamic, interactive nature of webpages has remained an unaddressed challenge. To bridge this gap, we reframe the problem, arguing that the solution lies not in a single command, but in a collaborative, hierarchical process. We introduce AutoPage, a novel multi-agent system that embodies this philosophy. AutoPage deconstructs paper-to-page creation into a coarse-to-fine pipeline from narrative planning to multimodal content generation and interactive rendering. To combat AI hallucination, dedicated “Checker” agents verify each step against the source paper, while optional human checkpoints ensure the final product aligns perfectly with the author’s vision, transforming the system from a mere tool into a powerful collaborative assistant. To rigorously validate our approach, we also construct PageBench, the first benchmark for this new task. Experiments show AutoPage not only generates high-quality, visually appealing pages but does so with remarkable efficiency in under 15 minutes for less than $0.1. Code and data will be released.
%U https://aclanthology.org/2026.findings-acl.1988/
%P 39963-39999
Markdown (Informal)
[Human-Agent Collaborative Paper-to-Page Crafting](https://aclanthology.org/2026.findings-acl.1988/) (Ma et al., Findings 2026)
ACL
- Qianli Ma, Siyu Wang, Chen Yilin, Yinhao Tang, Yixiang Yang, Chang Guo, Bingjie Gao, Zhening Xing, Yanan Sun, and Zhipeng Zhang. 2026. Human-Agent Collaborative Paper-to-Page Crafting. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39963–39999, San Diego, California, United States. Association for Computational Linguistics.