Di Yang 杨迪

Applied Machine Learning & Multimodal Generation AI | Ph.D. Researcher

🏛️ The College of William & Mary, Williamsburg, VA

🤝 Also working with: Dr. Haipeng Chen, Dr. Yufei Wang

✉️ Email: dyang06@wm.edu / diyang_uestc@outlook.com

I am a Ph.D. student in Data Science at The College of William & Mary, supervised by Dr. Yanhai Xiong, and also work with Dr. Haipeng Chen. Currently, doing research with SparcAI Inc. (mentored by Dr. Yufei Wang). Previously, I received my M.Sc. in Electrical Engineering from National University of Singapore (NUS) (advised by Dr. John S. Ho) and B.Eng. in Microelectronic Science and Engineering from University of Electronic Science and Technology of China (UESTC).

Prior to my Ph.D., I worked as a Digital Logic Engineer at Cambricon Technologies on large-scale AI accelerator hardware verification. My research focuses on 3D/4D World Models, World Action Models (WAM), Embodied AI, and Few-Step Generative Modeling.

✉️ Email (WM) ✉️ Email (Outlook) 🎓 Google Scholar 📖 OpenReview 🐙 GitHub

News & Updates

2026.09 🎉🎉 Double acceptance! Both "VTV-FM" and "HyCO" have been Accepted by NeurIPS 2026!

2026.06 Awarded the Summer Research Fellowship at The College of William & Mary.

2026.05 Developed Channel Match for high-resolution 3D distillation on TRELLIS.2.

2026.02 🎉 Our paper "LENS: Learning to Navigate with Active Search for Partially Observable MAPF in Unknown Environments" was Accepted by Transactions on Machine Learning Research (TMLR)!

2026.01 Preprint on HyCO released on arXiv:2609.07990.

2025.08 🎉 Our paper "UP-Bench: A Benchmark for Underwater Path Planning Algorithms" was accepted for an Oral Presentation at KDD 2025 (Benchmark Track)!

2025.06 Awarded the Summer Research Fellowship at The College of William & Mary.

2024.09 Joined The College of William & Mary as a Ph.D. student in Data Science.

Research Interests

3D/4D World Models & Physical Representations

Explicit 4D spatiotemporal representations (Sparc4D), compact dynamic state tokenizers, and generative world models capturing real-world physical dynamics.

World Action Models (WAM) & Embodied AI

Action-conditioned world simulation, integrating metric spatial representations with physical rollouts for long-horizon robot manipulation and policy learning.

Few-Step Generative Modeling

Continuous-time consistency distillation, second-order flow matching (VTV-FM), and custom JVP FlashAttention kernels for 3D generation (Channel Match).

Multi-Agent Systems & Simulation

Decentralized multi-agent pathfinding under partial observability (LENS, TMLR) and realistic 3D simulation platforms (UP-Bench, KDD Oral).

Selected Publications

Peer-Reviewed

HyCO: A Hybrid Neural Solver for Combinatorial Optimization

Y. Li, Di Yang*, H. Chen, Y. Xiong (*Co-first author)

Annual Conference on Neural Information Processing Systems (NeurIPS), 2026 NeurIPS 2026

Designs an RL-to-diffusion handover using policy entropy and model disagreement to combine sequential construction with generative solution completion, reducing TSP-1000 optimality gap from 15.4% to 4.6%.

[OpenReview] [arXiv] [PDF] [Code]

@inproceedings{li2026hyco,
  title={HyCO: A Hybrid Neural Solver for Combinatorial Optimization},
  author={Li, Y. and Yang, Di and Chen, H. and Xiong, Y.},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2026}
}

VTV-FM: Flow Matching through Variational Terminal-Velocity Closure

H. Jiang, Y. Li, Di Yang*, Y. Xiong, H. Chen, Y. He (*Co-first author)

Annual Conference on Neural Information Processing Systems (NeurIPS), 2026 NeurIPS 2026

Co-developed phase-space flow matching with cubic Hermite trajectories and a closed-form minimum-acceleration variational closure, achieving FID 2.99 on CIFAR-10 versus 3.45-3.73 for baselines.

[OpenReview] [PDF] [Code]

@inproceedings{jiang2026vtvfm,
  title={VTV-FM: Flow Matching through Variational Terminal-Velocity Closure},
  author={Jiang, H. and Li, Y. and Yang, Di and Xiong, Y. and Chen, H. and He, Y.},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2026}
}

LENS: Learning to Navigate with Active Search for Partially Observable MAPF in Unknown Environments

Di Yang et al.

Transactions on Machine Learning Research (TMLR), 2026 Accepted

A hybrid Multi-Agent Pathfinding architecture decoupling macro-topological spatial reasoning (via U-Nets) from micro-level collision avoidance, achieving generalization comparable to 85M foundation models with <0.2% training data.

[OpenReview] [PDF]

@article{yang2026lens,
  title={LENS: Learning to Navigate with Active Search for Partially Observable MAPF in Unknown Environments},
  author={Yang, Di and others},
  journal={Transactions on Machine Learning Research},
  year={2026}
}

UP-Bench: A Benchmark for Underwater Path Planning Algorithms

Di Yang, Yanhai Xiong

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025 Benchmark Track Oral

A configurable open-source 3D simulation benchmark platform incorporating realistic fluid dynamics, partial observability, and dynamic obstacles, benchmarking Deep RL (SAC) and classical planning algorithms.

[ACM DL / Paper] [PDF] [Code]

@inproceedings{yang2025upbench,
  title={UP-Bench: A Benchmark for Underwater Path Planning Algorithms},
  author={Yang, Di and Xiong, Yanhai},
  booktitle={Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2025}
}

Preprints & Under Review

Sparc4D: A Compact Explicit 4D Representation for Dynamic Scenes

Di Yang, Zhihao Li, Yanhai Xiong, Yufei Wang

arXiv:2610.01229, 2026 Preprint

A feed-forward autoencoder encoding monocular videos into a compact, sparse 4D scene state by sharing static features and compressing dynamic motion into spatially anchored temporal slots. Decodes directly into 2D Gaussian surfels, achieving competitive novel-view synthesis (21.51 dB on MultiCamVideo) at ~0.6M state size (~12× smaller than single-frame baselines) with zero-shot transfer to real handheld DyCheck videos.

[arXiv] [PDF] [Code (Coming Soon)]

@article{yang2026sparc4d,
  title={Sparc4D: A Compact Explicit 4D Representation for Dynamic Scenes},
  author={Yang, Di and Li, Zhihao and Xiong, Yanhai and Wang, Yufei},
  journal={arXiv preprint arXiv:2610.01229},
  year={2026}
}

Channel Match: Calibrating Shape Latents for Few-Step 3D Generation

Di Yang, Z. Li, Y. Xiong, Y. Wang

Still under review Under Review

Distills a native 3D generator into a two-step student via continuous self-consistency training with a custom JVP FlashAttention kernel. Channel Match calibrates sampling-induced latent distribution drift, achieving 4.5× faster geometry sampling on TRELLIS.2 (1024³ resolution) without texture artifacts.

[PDF] [Code (Coming Soon)]

@article{yang2026channelmatch,
  title={Channel Match: Calibrating Shape Latents for Few-Step 3D Generation},
  author={Yang, Di and Li, Z. and Xiong, Y. and Wang, Y.},
  journal={Under Review},
  year={2026}
}

Selected Research & Industry Experience

Research Collaboration & Research Intern Jan 2026 – Present

SparcAI Inc. (Remote) • Advisor: Dr. Yufei Wang

VLA Policy Manipulation: Integrating robot-centric point maps into a 5B VLA policy for metric spatial manipulation; built OmniGibson/BEHAVIOR pipelines with failure-case data augmentation.
Channel Match (Few-Step 3D Distillation): Distilled a native 3D generator to 2 steps with continuous self-consistency (4.5× faster shape sampling); implemented a custom Triton JVP FlashAttention kernel.
Explicit 4D Representation (Sparc4D): Developed Sparc4D, a compact explicit 4D autoencoder for dynamic scenes compressing time-varying features into temporal slots with 2D Gaussian surfels (~12× smaller state than single-frame baselines).
Next-Gen World Models (Ongoing Exploration): Actively exploring the potential of extending dynamic 4D representations towards next-generation World Models and unified physical world state tokenizers.

Ph.D. Researcher Sep 2024 – Present

The College of William & Mary • Advisor: Dr. Yanhai Xiong

HyCO: Designed RL-to-diffusion handover for combinatorial optimization; reduced TSP-1000 optimality gap from 15.4% to 4.6% (NeurIPS 2026, arXiv:2609.07990).
LENS & UP-Bench: Proposed LENS (TMLR) for partially observable MAPF; designed UP-Bench (KDD 2025 Oral) 3D underwater simulation benchmark.
VTV-FM: Co-developed second-order phase-space flow matching with variational terminal-velocity closure (NeurIPS 2026).

Digital Logic Engineer Aug 2022 – Jul 2024

Cambricon Technologies Co., Ltd. (Beijing, China)

Built functional verification pipelines for large-scale AI accelerator chips; owned system-level performance verification for low-latency data flow.

Graduate Researcher Jan 2021 – Jan 2022

National University of Singapore (NUS) • Advisor: Dr. John S. Ho

Conducted graduate research in wireless micro-systems and intelligent embedded hardware.