World Reasoning Arena
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866915893276049408 |
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| author | PAN Team Gao, Qiyue Zhou, Kun Xiang, Jiannan Liu, Zihan Yang, Dequan Chen, Junrong Ahmad, Arif Zeng, Cong Bannur, Ganesh Huang, Xinqi Liu, Zheqi Gu, Yi Yang, Yichi Liu, Guangyi Hu, Zhiting Liu, Zhengzhong Xing, Eric |
| author_facet | PAN Team Gao, Qiyue Zhou, Kun Xiang, Jiannan Liu, Zihan Yang, Dequan Chen, Junrong Ahmad, Arif Zeng, Cong Bannur, Ganesh Huang, Xinqi Liu, Zheqi Gu, Yi Yang, Yichi Liu, Guangyi Hu, Zhiting Liu, Zhengzhong Xing, Eric |
| contents | World models (WMs) are intended to serve as internal simulators of the real world that enable agents to understand, anticipate, and act upon complex environments. Existing WM benchmarks remain narrowly focused on next-state prediction and visual fidelity, overlooking the richer simulation capabilities required for intelligent behavior. To address this gap, we introduce WR-Arena, a comprehensive benchmark for evaluating WMs along three fundamental dimensions of next world simulation: (i) Action Simulation Fidelity, the ability to interpret and follow semantically meaningful, multi-step instructions and generate diverse counterfactual rollouts; (ii) Long-horizon Forecast, the ability to sustain accurate, coherent, and physically plausible simulations across extended interactions; and (iii) Simulative Reasoning and Planning, the ability to support goal-directed reasoning by simulating, comparing, and selecting among alternative futures in both structured and open-ended environments. We build a task taxonomy and curate diverse datasets designed to probe these capabilities, moving beyond single-turn and perceptual evaluations. Through extensive experiments with state-of-the-art WMs, our results expose a substantial gap between current models and human-level hypothetical reasoning, and establish WR-Arena as both a diagnostic tool and a guideline for advancing next-generation world models capable of robust understanding, forecasting, and purposeful action. The code is available at https://github.com/MBZUAI-IFM/WR-Arena. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25887 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | World Reasoning Arena PAN Team Gao, Qiyue Zhou, Kun Xiang, Jiannan Liu, Zihan Yang, Dequan Chen, Junrong Ahmad, Arif Zeng, Cong Bannur, Ganesh Huang, Xinqi Liu, Zheqi Gu, Yi Yang, Yichi Liu, Guangyi Hu, Zhiting Liu, Zhengzhong Xing, Eric Computer Vision and Pattern Recognition World models (WMs) are intended to serve as internal simulators of the real world that enable agents to understand, anticipate, and act upon complex environments. Existing WM benchmarks remain narrowly focused on next-state prediction and visual fidelity, overlooking the richer simulation capabilities required for intelligent behavior. To address this gap, we introduce WR-Arena, a comprehensive benchmark for evaluating WMs along three fundamental dimensions of next world simulation: (i) Action Simulation Fidelity, the ability to interpret and follow semantically meaningful, multi-step instructions and generate diverse counterfactual rollouts; (ii) Long-horizon Forecast, the ability to sustain accurate, coherent, and physically plausible simulations across extended interactions; and (iii) Simulative Reasoning and Planning, the ability to support goal-directed reasoning by simulating, comparing, and selecting among alternative futures in both structured and open-ended environments. We build a task taxonomy and curate diverse datasets designed to probe these capabilities, moving beyond single-turn and perceptual evaluations. Through extensive experiments with state-of-the-art WMs, our results expose a substantial gap between current models and human-level hypothetical reasoning, and establish WR-Arena as both a diagnostic tool and a guideline for advancing next-generation world models capable of robust understanding, forecasting, and purposeful action. The code is available at https://github.com/MBZUAI-IFM/WR-Arena. |
| title | World Reasoning Arena |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.25887 |