# ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search

arXiv: https://arxiv.org/abs/2506.06524 · HTML: https://mlsanity.com/paper/2506.06524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [MarioGPT: Open-Ended Text2Level Generation through Large Language Models](https://mlsanity.com/paper/2302.05981/) (2023)
- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://mlsanity.com/paper/1802.10363/) (2018)
- [Game Generation via Large Language Models](https://mlsanity.com/paper/2404.08706/) (2024)
- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [General Video Game Rule Generation](https://mlsanity.com/paper/1906.05160/) (2019)
- [Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules](https://mlsanity.com/paper/2309.09476/) (2023)

## Built on by (most cited first, 2 of 2)

- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Mortar: Evolving Mechanics for Automatic Game Design](https://mlsanity.com/paper/2601.00105/) (2025)

## Abstract (arXiv)

There is much interest in using large pre-trained models in Automatic Game Design (AGD), whether via the generation of code, assets, or more abstract conceptualization of design ideas. But so far this interest largely stems from the ad hoc use of such generative models under persistent human supervision. Much work remains to show how these tools can be integrated into longer-time-horizon AGD pipelines, in which systems interface with game engines to test generated content autonomously. To this end, we introduce ScriptDoctor, a Large Language Model (LLM)-driven system for automatically generating and testing games in PuzzleScript, an expressive but highly constrained description language for turn-based puzzle games over 2D gridworlds. ScriptDoctor generates and tests game design ideas in an iterative loop, where human-authored examples are used to ground the system's output, compilation errors from the PuzzleScript engine are used to elicit functional code, and search-based agents play-test generated games. ScriptDoctor serves as a concrete example of the potential of automated, open-ended LLM-based workflows in generating novel game content.