arXiv AI By Seonho Lee, Wonryeol Jeong, Alberto Cereser, Inha Kang, Hyeonjong Kim, Seungmin Kwak, Dongmin Park

A2Z GameSpec-Bench: How Faithfully Can Coding Agents Generate Games from Game Design Specifications?

Read the original on arXiv AI →

A2Z GameSpec-Bench introduces a benchmark of 100 long‑form Game Design Documents (GDDs) to evaluate how faithfully coding agents can generate complete games from detailed specifications. The benchmark measures faithfulness by checking that the game satisfies the GDD requirements and preserves the relationships among them, using a dependency‑aware contract and a combination of source‑code inspection and agent‑generated test policies. Evaluations show that current agents struggle to meet interdependent requirements, but requirement‑specific feedback improves GDD fidelity by 10.9% after two revision rounds.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 21

GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions

arXiv:2609.21562v1 Announce Type: cross Abstract: Coding agents can modify and test code across large software projects. Game development is a domain where agents must implement gameplay rules. A gam...

By Xinyu Che, Yunfei Ge, Shihao Li, Yanchen Liu, Hang Yan, Xinping Lei, Yanghai Wang, Zixuan Dong, Yifan Yao, Qianqian Xie, Letian Zhu, Jiaheng Liu
arXiv AI
Aug 11

A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents

arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.

By Xin Zhou, Chun Yong Chong, Kisub Kim, Yun Peng, Rui Shu, Zihan Wu, Xu Han, Guowen Yuan, Zeyang Zhuang, Jounghoon Kim, Jeongjin Ju, Seongmin Ju, Taein Yoon, David Lo