@inproceedings{huang-etal-2026-gui-agents,
title = "{GUI} Agents for Continual Game Generation",
author = "Huang, Yixu and
Li, Bo and
Na, Li and
Wang, Zhe and
Chen, Kaijie and
Ge, Haonan and
Si, Qingyi and
Shen, Yuanzhe and
Yang, Ruihan and
Wang, Guangjing and
Guo, Hongcheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1100/",
pages = "23260--23278",
ISBN = "979-8-89176-462-0",
abstract = "Generating a game is not the same as making one playable. Existing code-generation approaches often translate a prompt directly into an artifact, leaving interaction-level failures undetected. We argue that game generation requires a player and study two roles for graphical user interface (GUI) agents. First, we introduce \textbf{PlaytestArena}, an evaluation environment containing 200 browser-based game-generation tasks across eight genres, each paired with rubrics of expected in-play behaviors. An independent GUI judge loads and plays each build to adjudicate these rubrics. Second, we propose \textbf{Play2Code}, in which a game agent and a rubric-blind GUI playtester iteratively generate, play, and refine games through shared memory. The playtester provides gameplay traces and actionable feedback, while a separate GPT-5.5 judge assigns final benchmark scores. Across three frontier backbones, Play2Code achieves a 66.8{\%} rubric pass rate, outperforming single-pass and agentic-coding baselines by 37.1 and 14.6 points, respectively. Its scores also improve monotonically across refinement rounds. Further analysis shows that GUI-agent feedback is more traceable than human reports yet exhibits similarly idiosyncratic priorities. These results establish GUI playtesting as an evaluation and refinement signal for interactive code generation. Our project website is available at \url{https://continual-game-generation.vercel.app/}."
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<abstract>Generating a game is not the same as making one playable. Existing code-generation approaches often translate a prompt directly into an artifact, leaving interaction-level failures undetected. We argue that game generation requires a player and study two roles for graphical user interface (GUI) agents. First, we introduce PlaytestArena, an evaluation environment containing 200 browser-based game-generation tasks across eight genres, each paired with rubrics of expected in-play behaviors. An independent GUI judge loads and plays each build to adjudicate these rubrics. Second, we propose Play2Code, in which a game agent and a rubric-blind GUI playtester iteratively generate, play, and refine games through shared memory. The playtester provides gameplay traces and actionable feedback, while a separate GPT-5.5 judge assigns final benchmark scores. Across three frontier backbones, Play2Code achieves a 66.8% rubric pass rate, outperforming single-pass and agentic-coding baselines by 37.1 and 14.6 points, respectively. Its scores also improve monotonically across refinement rounds. Further analysis shows that GUI-agent feedback is more traceable than human reports yet exhibits similarly idiosyncratic priorities. These results establish GUI playtesting as an evaluation and refinement signal for interactive code generation. Our project website is available at https://continual-game-generation.vercel.app/.</abstract>
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%0 Conference Proceedings
%T GUI Agents for Continual Game Generation
%A Huang, Yixu
%A Li, Bo
%A Na, Li
%A Wang, Zhe
%A Chen, Kaijie
%A Ge, Haonan
%A Si, Qingyi
%A Shen, Yuanzhe
%A Yang, Ruihan
%A Wang, Guangjing
%A Guo, Hongcheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F huang-etal-2026-gui-agents
%X Generating a game is not the same as making one playable. Existing code-generation approaches often translate a prompt directly into an artifact, leaving interaction-level failures undetected. We argue that game generation requires a player and study two roles for graphical user interface (GUI) agents. First, we introduce PlaytestArena, an evaluation environment containing 200 browser-based game-generation tasks across eight genres, each paired with rubrics of expected in-play behaviors. An independent GUI judge loads and plays each build to adjudicate these rubrics. Second, we propose Play2Code, in which a game agent and a rubric-blind GUI playtester iteratively generate, play, and refine games through shared memory. The playtester provides gameplay traces and actionable feedback, while a separate GPT-5.5 judge assigns final benchmark scores. Across three frontier backbones, Play2Code achieves a 66.8% rubric pass rate, outperforming single-pass and agentic-coding baselines by 37.1 and 14.6 points, respectively. Its scores also improve monotonically across refinement rounds. Further analysis shows that GUI-agent feedback is more traceable than human reports yet exhibits similarly idiosyncratic priorities. These results establish GUI playtesting as an evaluation and refinement signal for interactive code generation. Our project website is available at https://continual-game-generation.vercel.app/.
%U https://aclanthology.org/2026.findings-emnlp.1100/
%P 23260-23278
Markdown (Informal)
[GUI Agents for Continual Game Generation](https://aclanthology.org/2026.findings-emnlp.1100/) (Huang et al., Findings 2026)
ACL
- Yixu Huang, Bo Li, Li Na, Zhe Wang, Kaijie Chen, Haonan Ge, Qingyi Si, Yuanzhe Shen, Ruihan Yang, Guangjing Wang, and Hongcheng Guo. 2026. GUI Agents for Continual Game Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 23260–23278, Budapest, Hungary. Association for Computational Linguistics.