arXiv AI By Max Ku, Nok-Kan Law, Yu-Chien Tang, Shih-Ying Yeh, Ping Nie, Andy Zheng, Tat Hei Lai, Fei-Yueh Chen, Nikko Yu, Wei-Chieh Sun, Suzy Huang, Chiao-Wei Hsu, Chih-Chuan Huang, Chak-Wing Mak, Ho Yin Sam Ng, Edisy Kin Wai Chan, Min-Hung Chen, Ho Kei Cheng

World Editing: Intervening on Executable Worlds at Increasing Depth

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The paper introduces the concept of world editing—intervening on an existing executable world while preserving unchanged properties—and defines intervention depth as a measure of how strongly edits affect world entities, dynamics, and systems. It presents IGMWorld and IGMBench, a benchmark of 110 tasks across Minecraft and Terraria, evaluating edits through deterministic executability, behavioral, preservation, and visual checks. Frontier coding agents achieve up to 78.2% task-level success and 94.8% criterion-level success, though reliability drops with deeper interventions and visual consistency remains low.

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