# MapStory: Prototyping Editable Map Animations with LLM Agents

arXiv: https://arxiv.org/abs/2505.21966 · HTML: https://mlsanity.com/paper/2505.21966/

## Key facts

- Cited by 3 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 21 of its 86 references that are in the map.
- First posted on arXiv 28 May 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 21 of 21)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [GPT4GEO: How a Language Model Sees the World's Geography](https://mlsanity.com/paper/2306.00020/) (2023)
- [Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models](https://mlsanity.com/paper/2304.09337/) (2023)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [GeoLLM: Extracting Geospatial Knowledge from Large Language Models](https://mlsanity.com/paper/2310.06213/) (2023)
- [PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement](https://mlsanity.com/paper/2403.04014/) (2024)
- [LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing](https://mlsanity.com/paper/2402.10294/) (2024)
- [DirectGPT: A Direct Manipulation Interface to Interact with Large Language Models](https://mlsanity.com/paper/2310.03691/) (2023)
- [Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts](https://mlsanity.com/paper/2308.03921/) (2023)
- [B-Script: Transcript-based B-roll Video Editing with Recommendations](https://mlsanity.com/paper/1902.11216/) (2019)
- [ExpressEdit: Video Editing with Natural Language and Sketching](https://mlsanity.com/paper/2403.17693/) (2024)
- [RealityTalk: Real-Time Speech-Driven Augmented Presentation for AR Live Storytelling](https://mlsanity.com/paper/2208.06350/) (2022)
- [LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing Animation](https://mlsanity.com/paper/2405.07065/) (2024)
- [CrossTalk: Intelligent Substrates for Language-Oriented Interaction in Video-Based Communication and Collaboration](https://mlsanity.com/paper/2308.03311/) (2023)
- [Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagrams](https://mlsanity.com/paper/2405.18614/) (2024)
- [DrawTalking: Building Interactive Worlds by Sketching and Speaking](https://mlsanity.com/paper/2401.05631/) (2024)
- [GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements](https://mlsanity.com/paper/2303.06460/) (2023)
- [MoGraphGPT: Creating Interactive Scenes Using Modular LLM and Graphical Control](https://mlsanity.com/paper/2502.04983/) (2025)
- [Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives](https://mlsanity.com/paper/2009.04508/) (2020)
- [GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation](https://mlsanity.com/paper/2308.13588/) (2023)

## Built on by (most cited first, 3 of 3)

- [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025)
- [DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos](https://mlsanity.com/paper/2608.29711/) (2026)
- [MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Production](https://mlsanity.com/paper/2609.10385/) (2026)

## Abstract (arXiv)

We introduce MapStory, an LLM-powered animation prototyping tool that generates editable map animation sequences directly from natural language text by leveraging a dual-agent LLM architecture. Given a user written script, MapStory automatically produces a scene breakdown, which decomposes the text into key map animation primitives such as camera movements, visual highlights, and animated elements. Our system includes a researcher agent that accurately queries geospatial information by leveraging an LLM with web search, enabling automatic extraction of relevant regions, paths, and coordinates while allowing users to edit and query for changes or additional information to refine the results. Additionally, users can fine-tune parameters of these primitive blocks through an interactive timeline editor. We detail the system's design and architecture, informed by formative interviews with professional animators and by an analysis of 200 existing map animation videos. Our evaluation, which includes expert interviews (N=5) and a usability study (N=12), demonstrates that MapStory enables users to create map animations with ease, facilitates faster iteration, encourages creative exploration, and lowers barriers to creating map-centric stories.