# Usando LLMs para Programar Jogos de Tabuleiro e Variacc~oes

arXiv: https://arxiv.org/abs/2511.05114 · HTML: https://mlsanity.com/paper/2511.05114/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [A Performance Study of LLM-Generated Code on Leetcode](https://mlsanity.com/paper/2407.21579/) (2024)
- [Ludii -- The Ludemic General Game System](https://mlsanity.com/paper/1905.05013/) (2019)
- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [Grammar-based Game Description Generation using Large Language Models](https://mlsanity.com/paper/2407.17404/) (2024)
- [Boardwalk: Towards a Framework for Creating Board Games with LLMs](https://mlsanity.com/paper/2508.16447/) (2025)
- [Evolutionary Tabletop Game Design: A Case Study in the Risk Game](https://mlsanity.com/paper/2310.20008/) (2023)

## Abstract (arXiv)

Creating programs to represent board games can be a time-consuming task. Large Language Models (LLMs) arise as appealing tools to expedite this process, given their capacity to efficiently generate code from simple contextual information. In this work, we propose a method to test how capable three LLMs (Claude, DeepSeek and ChatGPT) are at creating code for board games, as well as new variants of existing games.