Author

Xingzhuo Guo

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Preprint Aug 2026

HarnessEval-W: Agentifying the Evaluation of Visual Worlds

A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, where judging a rollout requires understanding whether physics, causality, and world state evolve correctly. Humans spot such violations natu...

Weiliang Chen, Haowen Sun, Jun Gao et al. · 2 citations

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, Weiliang Chen et al. · 4 citations · ⚡1