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arXiv AI By Yijun Zhou, Minghao Shao, Ke Zhang, Renkai Yuan, Hongbo Yuan, Bin Hu, Bozhao Qian, Hongxia Shen, Yanhong Liu, Yuting Chen, Yuntao Shi, Zhongxuan Xu, Shuo Mao, Haijia Zhou, Zhendong Li, Tao Zhang, Chenggang Li, Yuxuan Wang, Zinan Zheng, Calvin Zhang, Jia Li, Heng Chang, Jie Feng

UnityBench-Mini3D: Evaluating LLM Agents for 3D Game Development in Unity

Read the original on arXiv AI →

UnityBench-Mini3D is a benchmark comprising 77 Unity project-level tasks organized into seven expert-defined skill categories and three difficulty tiers. Each task pairs a complete Unity project with natural-language requirements and is evaluated using 1,178 held-out, human-authored milestone tests that assess scene structure, component configuration, and runtime behavior. The benchmark provides deterministic partial credit, localizes failures, and distinguishes partial implementations from complete solutions without relying on subjective judges, achieving an overall pass rate of 57.7% across 21 models and 9 coding harnesses.

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.

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