AI编码
arxiv.org ·
GameLogicBench：基于 Tick 级状态断言在运行时游戏逻辑上评估编码智能体
该论文提出 GameLogicBench，包含 72 个 Godot 游戏逻辑任务，通过 403 个手工设计场景和种子参数变化生成 1451 个测试用例，由自动评估器在每个仿真 tick 检查游戏规则。评估器需接受同一任务的不同正确实现，并拒绝移除某一必需能力的 mutant。在 20 种语言模型与脚手架组合中，最佳运行仅解决 52.78% 的任务；在 Claude Code 下，12 个模型随任务从孤立机制扩展到多系统交互和仓库级功能，解决率均下降，且仓库级任务中智能体更频繁地检查代码、调用更多工具。多数失败提交可运行但部分游戏行为实现错误。对比实验显示，未经 mutant 验证的评估器会放过错误提交；另有分析发现开放网络访问时智能体会从公开仓库复制代码。
AI推荐理由 arXiv 新论文，提出 GameLogicBench：72 个 Godot 游戏逻辑任务、403 个手工场景、1451 个测试用例，用 tick 级状态断言在运行时检查游戏规则，并要求评估器接受不同正确实现、拒绝移除单一能力的 mutant。摘要给出具体数据：20 种模型+脚手架组合中最佳仅解出 52.78%，Claude Code 下 12 个模型随任务范围扩大（孤立机制→多系统交互→仓库级）成功率下降，失败提交多数可运行但行为实现错误；还发现无 mutant 验证时错误提交会通过，以及开放网络时智能体从公开仓库复制代码。这些是对编码智能体评测方法的具体新贡献，对 Agent 评测与开发工具有实际参考价值。局限：仅覆盖 Godot 项目，摘要未给出各模型明细与统计显著性，结论需以论文全文为准。
82
正文
Computer Science > Software Engineering
arXiv:2609.21562 (cs)
[Submitted on 18 Sep 2026]
Title:GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions
Authors:Xinyu Che, Yunfei Ge, Shihao Li, Yanchen Liu, Hang Yan, Xinping Lei, Yanghai Wang, Zixuan Dong, Yifan Yao, Qianqian Xie, Letian Zhu, Jiaheng Liu
View a PDF of the paper titled GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions, by Xinyu Che and 11 other authors
View PDF
HTML (experimental)
Abstract:Coding agents can modify and test code across large software projects. Game development is a domain where agents must implement gameplay rules. A game can end in a valid state even after violating its rules during the run. Current game-development benchmarks replay fixed examples, score videos, or ask another model to judge the result. However, no existing benchmark checks game rules throughout execution across varied evaluator-selected scenarios while ensuring exactly reproducible verdicts. We introduce GameLogicBench, a benchmark of 72 gameplay-logic tasks in Godot projects. An automated evaluator checks each game's rules at every simulation tick. Across 403 hand-designed scenarios, seeded parameter variations produce 1,451 test cases. To ensure that the evaluator measures behavior rather than implementation choice, it must accept different correct implementations for each task while rejecting mutants, implementations with one required capability removed. The tasks span isolated mechanics, multi-system interactions, and repository-scale features. Across 20 combinations of language models and scaffolds, the best observed run solves 52.78% of tasks. Under Claude Code, all twelve models solve fewer tasks as task scope expands from isolated mechanics, through interacting systems, to repository-scale features. Agents inspect code more often and make more tool calls on repository-scale tasks than on isolated-mechanic tasks. Most unsuccessful submissions are runnable, but implement some required game behavior incorrectly. We compared versions of our benchmark evaluator built with and without validation using mutants. Without this validation, incorrect agent submissions passed. A separate analysis finds agents copying code from public repositories when network access is open. Reliable evaluation thus depends both on what the tests reject and on what external code agents can access.
Comments:
Subjects:
Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as:
arXiv:2609.21562 [cs.SE]
(or
arXiv:2609.21562v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2609.21562
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xinyu Che [view email]
[v1]
Fri, 18 Sep 2026 09:51:41 UTC (3,747 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions, by Xinyu Che and 11 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.SE
new
|
recent
| 2026-09
Change to browse by:
cs
cs.AI
cs.CL
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)