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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 et al.: Can AI Agents Build Whole Playable Games in a Real Engine? — Fukai Reads
A paper by Luo, Wang and colleagues on GameCraft-Bench, a benchmark for end-to-end game generation by coding agents. It has agents build complete playable games on Godot from natural-language specs, judged by launch, input replay, and video-based scoring across 140 tasks in 15 families. Even the strongest configuration reaches only 41.46% overall, and the authors report that agents can build mechanics but fall short of finished games with content, readability, and polish.
