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#game-generation
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Huang et al.: Letting an AI Play the Generated Game, Then Fix It — Fukai Reads
A game-generation paper by Yixu Huang and colleagues (Fudan University, Xiaohongshu and others). Play2Code puts a screen-driving GUI agent into the generation loop as a playtester, evaluated on PlaytestArena, a new environment of 200 tasks and 1,548 rubric criteria. Averaged over three backbones, rubric pass-rate goes from 29.7% for single-pass generation and 52.2% for a code-inspection-only pipeline to 66.8%.
Zhou et al.: The Verifier is the Curriculum — Training Game Generation on a Launch Check Alone — Fukai Reads
A game-generation paper by Chenyu Zhou and colleagues. Starting from a diagnosis that the learned judge is gameable, they gate self-distillation on a single binary signal — does the generated Godot project launch cleanly — and over three rounds lift clean generation on four unseen families from 8.8% to 42.2%, with best-of-16 coverage going 18/25 to 25/25. Loosen the gate and the gain disappears.
Luo 等人：AI 智能体能否在真实引擎中制作出可以游玩的完整游戏？——Fukai 的解读
Luo、Wang 等人提出的评估基准论文 GameCraft-Bench，衡量编程智能体是否能端到端生成游戏。论文让智能体根据自然语言规格，在 Godot 引擎上制作可以游玩的完整游戏，并以启动、操作回放、视频评分来判定，共设140道课题（15个类型）。最强配置的全体得分也仅为41.46%，表明智能体虽能制作出机制框架，但距离具备内容厚度、界面易读性和精加工的完成品仍有差距。
