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A2Z GameSpec-Bench:编码智能体能否忠实按游戏设计文档生成游戏?
AI 导读
研究者推出 A2Z GameSpec-Bench,用 100 份长篇幅游戏设计文档(GDD)评测编码智能体端到端开发游戏的忠实度,即游戏是否满足 GDD 需求并保持需求间依赖关系。
正文
Abstract:Delegating complete application development to coding agents requires preserving the intended design rather than simply producing plausible outputs through naive prompting. Game development provides a demanding testbed, as long-form Game Design Documents (GDDs) describe requirements that must work together across game logic, visual rendering, and player interactions. However, existing game-development benchmarks typically use compact specifications and provide limited support for evaluating interdependent requirements across these aspects in long-form GDDs. We introduce A2Z GameSpec-Bench, a benchmark of 100 long-form GDDs for evaluating end-to-end game development by agents. We measure faithfulness by checking whether the game satisfies the GDD requirements and preserves the relationships among them. Each GDD is turned into a dependency-aware contract that contains rules, constraints, and prerequisite relations. Following game-development practices, we combine source-code inspection with agent-generated test policies for scenario-based replay and adaptive playtesting. The contract remains fixed across agents and revision rounds, while judgments and evidence linked to the same requirements support consistent comparison and failure detection. Our evaluations show that current agents struggle to jointly satisfy interdependent requirements across code implementation and actual play. Requirement-specific feedback improves GDD Fidelity by 10.9% relative to self-revision after two rounds. A2Z GameSpec-Bench assesses end-to-end specification-following ability beyond implementation judgments and provides targeted feedback to support more faithful game development. Code and datasets are available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2609.39564 [cs.AI] |
| (or arXiv:2609.39564v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.39564 arXiv-issued DOI via DataCite |
Submission history
From: Seonho Lee [view email]
[v1]
Wed, 30 Sep 2026 12:00:50 UTC (47,205 KB)
来源:HuggingFace Daily Papers(社区热门论文) · arxiv.org