# STORY2GAME: Generating (Almost) Everything in an Interactive Fiction Game

arXiv: https://arxiv.org/abs/2505.03547 · HTML: https://mlsanity.com/paper/2505.03547/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Bringing Stories Alive: Generating Interactive Fiction Worlds](https://mlsanity.com/paper/2001.10161/) (2020)
- [Word2World: Generating Stories and Worlds through Large Language Models](https://mlsanity.com/paper/2405.06686/) (2024)
- [STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models](https://mlsanity.com/paper/2406.05872/) (2024)
- [Neural Story Planning](https://mlsanity.com/paper/2212.08718/) (2022)

## Built on by (most cited first, 4 of 4)

- [AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents](https://mlsanity.com/paper/2606.24893/) (2026)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [IVIE: A Neuro-symbolic Approach to Incremental and Validated Generation of Interactive Fiction Worlds](https://mlsanity.com/paper/2606.13348/) (2026)
- [Reconstructing Persistent Worlds from Narratives for Narrative-Grounded Interactive Experiences](https://mlsanity.com/paper/2608.04037/) (2026)

## Abstract (arXiv)

We introduce STORY2GAME, a novel approach to using Large Language Models to generate text-based interactive fiction games that starts by generating a story, populates the world, and builds the code for actions in a game engine that enables the story to play out interactively. Whereas a given set of hard-coded actions can artificially constrain story generation, the ability to generate actions means the story generation process can be more open-ended but still allow for experiences that are grounded in a game state. The key to successful action generation is to use LLM-generated preconditions and effects of actions in the stories as guides for what aspects of the game state must be tracked and changed by the game engine when a player performs an action. We also introduce a technique for dynamically generating new actions to accommodate the player's desire to perform actions that they think of that are not part of the story. Dynamic action generation may require on-the-fly updates to the game engine's state representation and revision of previously generated actions. We evaluate the success rate of action code generation with respect to whether a player can interactively play through the entire generated story.