Human-Agent Collaborative Paper-to-Page Crafting

2026-07-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

AutoPage is a novel multi-agent system designed to automate the creation of interactive project webpages from academic papers, addressing the manual, repetitive task researchers face. This system employs a collaborative, hierarchical pipeline, moving from narrative planning to multimodal content generation and interactive rendering. It integrates "Checker" agents to combat AI hallucination by verifying each step against the source paper, alongside optional human checkpoints for author alignment. The research also introduces PageBench, the first benchmark for this task, comprising over 1,500 academic papers and their corresponding human-created project pages. Experiments demonstrate AutoPage's ability to generate high-quality, visually appealing pages efficiently, typically in under 15 minutes for less than \$0.1 using Gemini-2.5-Flash. It significantly enhances existing end-to-end methods and narrows performance gaps across various backbone models, achieving a user preference score of 7.16 out of 10.

Key takeaway

For research scientists or ML engineers tasked with creating project webpages, you should consider adopting multi-agent systems like AutoPage. This framework significantly reduces the manual effort and cost, generating high-quality, interactive pages in under 15 minutes for less than \$0.1. By integrating verification agents and optional human checkpoints, you can ensure factual accuracy and visual appeal, freeing up valuable research time while maintaining authorial control over dissemination.

Key insights

Automating dynamic webpage generation from papers requires a multi-agent, hierarchical, and human-collaborative approach for quality and cost-efficiency.

Principles

• Dynamic content generation benefits from collaborative, hierarchical processes.
• Dedicated "Checker" agents verify content against source material.
• Optional human checkpoints ensure authorial vision alignment.

Method

AutoPage uses a coarse-to-fine pipeline: narrative planning, multimodal content generation, and interactive rendering. Each phase includes LLM/VLM-based "Checker" agents for verification and optional human checkpoints.

In practice

• Implement multi-agent systems for complex content generation.
• Integrate verification agents to mitigate AI hallucination.
• Provide human feedback loops for authorial control.

Topics

• Multi-Agent Systems
• Automated Webpage Generation
• Scientific Communication
• LLM Agents
• PageBench Benchmark
• Human-Agent Collaboration

Code references

• langchain-ai/langchain

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.