SWE-Game benchmark tests whether coding agents can build games
An arXiv paper introduces SWE-Game, a benchmark of 247 tasks grounded in 41 executable reference Godot games spanning 13 gameplay categories in 2D and 3D, with five task types: brief-to-game, implementation from a design document, skeleton completion, repair of 83 injected-fault cases, and Godot-to-Unity porting. Across six evaluated models, Opus5 scored highest overall in all five task types, yet best overall scores on the three construction tasks stayed below 60 out of 100, with Brief-to-Game at 50.38; the authors flag requirement omissions and gameplay logic errors as the predominant problems. The paper reports executable checks reaching 92.59% balanced accuracy on human-labeled behaviors from 100 agent-built games versus 78.41% for a video-based VLM judge, and rubric visual scores with a 0.829 Spearman correlation to human ratings.
Why it matters: Game development gives coding agents a long-horizon, automatically verifiable testbed, and this benchmark suggests current models still struggle to ship complete games that match the brief.
Computer Science > Artificial Intelligence
arXiv:2609.33678 (cs)
[Submitted on 27 Sep 2026 (v1), last revised 7 Oct 2026 (this version, v3)]
Title:SWE-Game: Can Coding Agents Build the Games We Want?
Authors:Xiaoyu Chen, Lai Wei, Jin Wang, Xiangyu Zou, Ruochen Fan, Enze Luo, Mingzhe Yao, Jiahui Zhu, Yuhua Wen, Linghe Kong, Weiran Huang
View a PDF of the paper titled SWE-Game: Can Coding Agents Build the Games We Want?, by Xiaoyu Chen and 10 other authors
View PDF HTML (experimental)Abstract:We introduce SWE-Game, a benchmark of 247 tasks grounded in 41 executable reference Godot games spanning 13 gameplay categories in 2D and 3D. Five task types cover development from a brief, implementation from a game design document, skeleton completion, repair of 83 injected-fault cases, and Godot-to-Unity porting. Reference materials specify the intended gameplay, while a shared instrumentation interface lets evaluator-owned drivers and probes execute actions and observe independently implemented games. Evaluation combines engine-state checks, certified reference-input replay, and agent-authored feature demonstrations to assess mechanic correctness, demonstrated playability, and behavioral restoration and preservation after repairs. Game-specific vision-language rubrics separately assess presentation. Across six models, Opus5 achieves the highest overall score in all five task types. Best overall scores remain below 60 out of 100 across the three construction tasks, with Brief-to-Game reaching 50.38. Analysis of reviewed submissions identifies requirement omissions and gameplay logic errors as predominant implementation problems. On human-labeled behaviors from 100 agent-built games, executable checks achieve 92.59% balanced accuracy, compared with 78.41% for a video-based VLM judge. Rubric-based visual scores reach a Spearman correlation of 0.829 with human ratings of 200 gameplay clips. Together, these results characterize current agent capabilities across game-development activities and support combining runtime evidence with visual assessment.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.33678 [cs.AI] |
| (or arXiv:2609.33678v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.33678 arXiv-issued DOI via DataCite |
Submission history
From: Xiaoyu Chen [view email][v1] Sun, 27 Sep 2026 15:38:20 UTC (2,961 KB)
[v2] Tue, 6 Oct 2026 07:03:14 UTC (2,961 KB)
[v3] Wed, 7 Oct 2026 14:37:28 UTC (2,961 KB)
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View a PDF of the paper titled SWE-Game: Can Coding Agents Build the Games We Want?, by Xiaoyu Chen and 10 other authors
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How we got here
- Kaiming He's VISTA harness lets Claude Opus 5 hit 100 on ARC-AGI-3创业邦 科技 · Claude Opus 5