Author

Yiyang Cai

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Preprint Aug 2026

Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning

Experiments show that Code-as-World-VL achieves state-of-the-art performance on QuantiPhy and surpasses leading proprietary models, highlighting the potential of executable world representations as a scalable foundation for physical intelligence.

Han-Yang Wang, Yiyang Cai, Wei-Liang Chen et al. · 16 citations · ⚡2