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Zeyu Zheng

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#machine learning Preprint Sep 2026

Leveraging Inference-Time Compute for Diffusion Models via Global Scheduling of Denoising Trajectories

Diffusion models generate a sample by traversing a denoising trajectory, a sequence of stochastic noise-reduction steps that transforms pure noise into a draw from a target distribution. At deployment time, additional computation can improve sample quality without retraining: at each step, the sampler draws several can...

Yuan Cao, Yi-Fu Tang, Hang-Qi Li et al. · 0 citations
Open access Aug 2026

Learning to Simulate from Heavy-Tailed Distribution via Diffusion Model

Generative AI Learns to Sample Extreme Events Many rarest events often carry highest stakes, such as a market crash, a supply-chain rupture, or an extreme clinical outcome. Today’s leading generative AI models (e.g., diffusion models) have shown excessive power to simulate high-dimensional distributions, but they are...

Haoyu Liu, Tingyu Zhu, Nanshan Jia et al. · 5 citations
#artificial intelligence Preprint Aug 2026

Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction

Spec2Twin-Chain is considered, a framework that formulates blockchain digital-twin construction as a bi-level optimization problem and can construct behaviorally accurate twins, improve initial designs through iterative feedback, and reuse calibrated twins to support downstream decisions.

Hao-Ting Zhang, Haoxian Chen, Jiayuan Sheng et al. · 0 citations
Review Jul 2026

3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects

3D-DefectBench is introduced, a benchmark and framework for systematic analysis of VLM-based 3D defect detection pipelines, and finds that automated judges should be evaluated as complete pipelines and calibrated across human reference regimes, rather than benchmarked only as standalone models.

Zhenyu Zhao, Nanshan Jia, Jihyeon Je et al. · 0 citations

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