[Submitted on 1 Oct 2026]
Title:World Editing: Intervening on Executable Worlds at Increasing Depth
View a PDF of the paper titled World Editing: Intervening on Executable Worlds at Increasing Depth, by Max Ku and Nok-Kan Law and Yu-Chien Tang and Shih-Ying Yeh and Ping Nie and Andy Zheng and Tat Hei Lai and Fei-Yueh Chen and Nikko Yu and Wei-Chieh Sun and Suzy Huang and Chiao-Wei Hsu and Chih-Chuan Huang and Chak-Wing Mak and Ho Yin Sam Ng and Edisy Kin Wai Chan and Min-Hung Chen and Ho Kei Cheng
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Abstract:Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that should remain unchanged, and introduce intervention depth as an axis describing how strongly an edit couples world entities, dynamics, and systems. We instantiate this capability through industry-grade game modding and introduce IGMWorld, together with IGMBench, a benchmark of 110 tasks and over 1.1K executable state and behavioral criteria across Minecraft and Terraria. The tasks span property, entity, dynamics, and system interventions and are evaluated through deterministic executability, behavioral, preservation, and visual checks. Frontier coding agents already exhibit substantial world-editing capability: the strongest configuration solves 78.2% of tasks under a strict task-level criterion, while criterion-level performance reaches 94.8%. Reliability generally decreases with intervention depth, and this pattern persists even among tasks with similar numbers of evaluation criteria. Most failed edits still build and load successfully, suggesting that the main difficulty is making the edited world behave as requested. Visual consistency remains a separate weakness, with all evaluated configurations below 50% joint visual pass rate. These results show that world editing is a distinct capability from world generation and interaction, and that executable games provide a practical testbed for studying it.
Comments: Preprint. Project page: this https URL
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.02331 [cs.AI]
(or arXiv:2610.02331v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.02331
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Max W.F. Ku [view email] [v1] Thu, 1 Oct 2026 18:05:47 UTC (1,876 KB)
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View a PDF of the paper titled World Editing: Intervening on Executable Worlds at Increasing Depth, by Max Ku and Nok-Kan Law and Yu-Chien Tang and Shih-Ying Yeh and Ping Nie and Andy Zheng and Tat Hei Lai and Fei-Yueh Chen and Nikko Yu and Wei-Chieh Sun and Suzy Huang and Chiao-Wei Hsu and Chih-Chuan Huang and Chak-Wing Mak and Ho Yin Sam Ng and Edisy Kin Wai Chan and Min-Hung Chen and Ho Kei Cheng
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