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Han-Yang Wang

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Preprint Aug 2026

HarnessEval-W: Agentifying the Evaluation of Visual Worlds

This work introduces HarnessEval-W, an agentified evaluation pipeline that brings the harness paradigm from the LLM ecosystem to world model benchmarking, and applies HarnessEval-W to 18 representative world models over 330 evaluation cases.

Wei-Liang Chen, Haowen Sun, Jun Gao et al. · 3 citations
Sep 2026

High-Fidelity Implicit Text-to-Shape Generation with Voxelized Diffusion

Experimental results show that Diffusion-SDF is capable of generating both high-quality and highly diversified 3D shapes that conform well to the given text descriptions, and Diffusion-SDF++ further outperforms Diffusion-SDF with more precise shape details due to the adaptive voxelization strategy.

Yue-Qi Duan, Fang-Fu Liu, Han-Yang Wang et al. · 0 citations
Preprint Sep 2026

RoboFolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects

RoboFolDeX is introduced, a physical-world benchmark built entirely from real-robot data, with garment folding as its primary task, and a fair real-robot evaluation platform for externally submitted policies, with standardized tasks, held-out physical objects, controlled initializations, and a unified execution protoco...

Chen-Huan Liu, Yi Xu, Feng Wu et al. · 0 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, Wei-Liang Chen et al. · 16 citations · ⚡2
Jul 2026

DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation

DSWAM is introduced, a Dual-System World Action Foundation Model for fine-grained robot manipulation and built and evaluated under the DeMaVLA real-world deformable manipulation setting with matched robot platform, pretraining data, post-training data, and evaluation criteria.

Jian Zhu, Jianjun Zhang, Taiyi Su et al. · 2 citations

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