Paper Detail
Tongxu Luo, Rongsheng Wang, Jiaxi Bi, Chenming Xu, Zhengyang Tang, Jianlong Chen, Juhao Liang, Ke Ji, Shuqi Guo, Yuhao Du, Fan Bu, Wenyu Du, Xiaotong Zhang, Kyle Li, Shaobo Wang, Linfeng Zhang, Yuxuan Liu, Xin Lai, Chenxin Li, Yiduo Guo, Zhexin Zhang, Xinyuan Wang, Tianyi Bai, Ziniu Li, Benyou Wang
Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay. We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-game interaction in a target environment. We argue that evaluating this setting requires three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. We propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging. We instantiate this framework as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. Evaluations of frontier coding agents show that end-to-end game generation remains highly challenging: the strongest agent achieves only 41.46%, and most agents score below 40%. Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. See https://tongxuluo.github.io/gamecraft-bench-website for demos, code, and data.
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@misc{luo2026gamecraft,
title = {GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?},
author = {Tongxu Luo and Rongsheng Wang and Jiaxi Bi and Chenming Xu and Zhengyang Tang and Jianlong Chen and Juhao Liang and Ke Ji and Shuqi Guo and Yuhao Du and Fan Bu and Wenyu Du and Xiaotong Zhang and Kyle Li and Shaobo Wang and Linfeng Zhang and Yuxuan Liu and Xin Lai and Chenxin Li and Yiduo Guo and Zhexin Zhang and Xinyuan Wang and Tianyi Bai and Ziniu Li and Benyou Wang},
year = {2026},
abstract = {Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay. We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-gam},
url = {https://huggingface.co/papers/2606.17861},
keywords = {game generation, coding agents, natural-language specifications, game engine, executable gameplay, interactive verification, GameCraft-Bench, Godot, multimodal judging, code available, huggingface daily},
eprint = {2606.17861},
archiveprefix = {arXiv},
}
{}