GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions
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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
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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: | 36 pages, 9 figures, 13 tables. Xinyu Che, Yunfei Ge, Shihao Li, Yanchen Liu, Hang Yan, and Xinping Lei contributed equally. Jiaheng Liu is the corresponding author. Code and benchmark: this https URL |
| 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
arXiv-issued DOI via DataCite (pending registration)
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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
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