ViviDoc: Generating Interactive Documents through Human-Agent Collaboration
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Interactive documents help readers engage with complex ideas through dynamic visualization, interactive animations, and exploratory interfaces. However, creating such documents remains costly, as it requires both domain expertise and web development skills. Recent Large Language Model (LLM)-based agents can automate content creation, but directly applying them to interactive document generation often produces outputs that are difficult to control. To address this, we present ViviDoc, to the best of our knowledge the first work to systematically address interactive document generation. ViviDoc introduces a multi-agent pipeline (Planner, Styler, Executor, Evaluator). To make the generation process controllable, we provide three levels of human control: (1) the Document Specification (DocSpec) with SRTC Interaction Specifications (State, Render, Transition, Constraint) for structured planning, (2) a content-aware Style Palette for customizing writing and interaction styles, and (3) chat-based editing for iterative refinement. We also construct ViviBench, a benchmark of 101 topics derived from real-world interactive documents across 11 domains, along with a taxonomy of 8 interaction types and a 4-dimensional automated evaluation framework validated against human ratings (Pearson r > 0.84). Experiments show that ViviDoc achieves the highest content richness and interaction quality in both automated and human evaluation. A 12-person user study confirms that the system is easy to use, provides effective control over the generation process, and produces documents that satisfy users.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 28 references that are in the map. First posted on arXiv 30 Mar 2026.
Builds on
12 of 28 references.
Closest ancestors · 1
Same direction, newest first
- 2026Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation12 Feb
Other references · 11
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
- 2025PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slidescited by 86
- 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Surveycited by 29
- 2025Code2Video: A Code-centric Paradigm for Educational Video Generationcited by 27
- 2024HAIChart: Human and AI Paired Visualization Systemcited by 26
- 2025SlideGen: Collaborative Multimodal Agents for Scientific Slide Generationcited by 25
- 2025Creativity in LLM-based Multi-Agent Systems: A Surveycited by 23
- 2026IGenBench: Benchmarking the Reliability of Text-to-Infographic Generationcited by 17
- 2025VisJudge-Bench: Aesthetics and Quality Assessment of Visualizationscited by 16
- 2024DataLab: A Unified Platform for LLM-Powered Business Intelligencecited by 8
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Citing papers per year: 2026: 2.
2 papers.
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Close in wording, no citation either way
- 2026Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration
- 2026I-WebGenBench : Evaluating Interactivity in LLM-Generated Scientific Web Applications
- 2026PaperVoyager : Building Interactive Web with Visual Language Models
- 2026StoryState: Agent-Based State Control for Consistent and Editable Storybooks
- 2024AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image Generation
- 2026InteractWeb-Bench: Can Multimodal Agent Escape Blind Execution in Interactive Website Generation?
- 2026MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation
- 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping