Human-Agent Collaborative Paper-to-Page Crafting
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Abstract (arXiv)
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 dataset will be released at hrefhttps://mqleet.github.io/AutoPageProjectPage/Webpage.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 28 of its 60 references that are in the map. First posted on arXiv 22 Oct 2025.
Builds on
28 of 60 references.
References · 28
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- 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 351
16 more
- 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 320
- 2024MinerU: An Open-Source Solution for Precise Document Content Extractioncited by 255
- 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 202
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Built on by
Citing papers per year: 2025: 1; 2026: 6.
7 papers.
- 2025RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scalingcited by 9
- 2026DECKBench: Benchmarking Multi-Agent Frameworks for Academic Slide Generation and Editingcited by 9
- 2026Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistancecited by 8
- 2026PaperX: A Unified Framework for Multimodal Academic Presentation Generation with Scholar DAGcited by 3
- 2026Breaking Dual Bottlenecks: Evolving Unified Multimodal Models into Self-Adaptive Interleaved Visual Reasonerscited by 3
- 2026AgentDisCo: Towards Disentanglement and Collaboration in Open-ended Deep Research Agentscited by 2
- 2026From Transient Prompts to Persistent Control: Scientific Poster Generation via Recursive Semantic-Geometric Contractscited by 0
- 2026From Transient Prompts to Persistent Control: Scientific Poster Generation via Recursive Semantic-Geometric Contracts15 Sep 2026
- 2026Breaking Dual Bottlenecks: Evolving Unified Multimodal Models into Self-Adaptive Interleaved Visual Reasoners14 May 2026
- 2026AgentDisCo: Towards Disentanglement and Collaboration in Open-ended Deep Research Agents12 May 2026
- 2026DECKBench: Benchmarking Multi-Agent Frameworks for Academic Slide Generation and Editing10 Feb 2026
- 2026PaperX: A Unified Framework for Multimodal Academic Presentation Generation with Scholar DAG30 Jan 2026
- 2026Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance20 Jan 2026
- 2025RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling23 Oct 2025