Hi, I am Hongyang Du — a Sc.M. student in Computer Science at
Brown University advised by
Randall Balestriero. Currently I am
collaborating with Yue Wang at the
USC Physical SuperIntelligence Lab.
I previously obtained my B.S. in Computer Science and Mathematics from the
University of Maryland, College Park.
My research explores how Self-Supervised Learning can build World Models for Robotic Control. I focus on extracting spatiotemporal and 3D-aware priors to capture physical dynamics and how to create a scalable loop that couples causal prediction and action prediction.
Outside of research, I am an amateur powerlifter 💪 and a pro drummer 🥁; I am a Jazz cat and prog metalhead — think Chick Corea meets Animals as Leaders. Also, I live with a cat named Dingzhen(丁真)🐱, who keeps me grounded.
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Brown UniversityDepartment of Computer Science
Master's StudentAug. 2025 - present -
University of Maryland, College ParkB.S. in Computer Science and MathematicsAug. 2021 - May. 2025
Honors & Awards
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Robert Ma Scholarship Recipent2024
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Break Thhrough Tech Scholarship Recipent2021
News
Video INR Compression accepted to WACV 2026!VideoHallu accepted to NeurIPS 2025!Bears: Starting the Sc.M. Computer Science Program at Brown University in Fall 2025University of Maryland, College Park!VLM Survey accepted to CVPR Workshop 2025 as Oral presentation!iFLYTEK, as a Machine Learning Engineer Intern in the AI + Agriculture group this summer.Ipelets for Convex Polygonal Geometry accepted to SoCG 2024!Selected Publications (view all )
Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills
Dawei Liu*, Zongxia Li*, Hongyang Du, Xiyang Wu, Shihang Gui, Yongbei Kuang, Lichao Sun (* equal contribution)
Preprint 2026
TL;DR: Graph of Skills (GoS): offline skill dependency graph + inference-time retrieval (seeding + PageRank + token budget) so agents load a small, relevant skill bundle instead of the whole library—higher reward, fewer tokens.
Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills
Dawei Liu*, Zongxia Li*, Hongyang Du, Xiyang Wu, Shihang Gui, Yongbei Kuang, Lichao Sun (* equal contribution)
Preprint 2026
TL;DR: Graph of Skills (GoS): offline skill dependency graph + inference-time retrieval (seeding + PageRank + token budget) so agents load a small, relevant skill bundle instead of the whole library—higher reward, fewer tokens.
A Cookbook of 3D Vision: Data, Learning Paradigms, and Application
Hongyang Du*, Zongxia Li*, Dawei Liu*, Runhao Li*, Haoyuan Song, Qingyu Zhang, Yubo Wang, Jingcheng Ni, Shihang Gui, Congchao Dong, Tao Hu (* equal contribution)
CVPR Workshop 2026 Poster
TL;DR: A data-centric “cookbook” of 3D vision: how representations (points, meshes, Gaussians, etc.), datasets, and learning setups connect to reconstruction, generation, and video / 4D trends.
A Cookbook of 3D Vision: Data, Learning Paradigms, and Application
Hongyang Du*, Zongxia Li*, Dawei Liu*, Runhao Li*, Haoyuan Song, Qingyu Zhang, Yubo Wang, Jingcheng Ni, Shihang Gui, Congchao Dong, Tao Hu (* equal contribution)
CVPR Workshop 2026 Poster
TL;DR: A data-centric “cookbook” of 3D vision: how representations (points, meshes, Gaussians, etc.), datasets, and learning setups connect to reconstruction, generation, and video / 4D trends.
MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data
Zongxia Li*, Hongyang Du*, Chengsong Huang*, Xiyang Wu, Lantao Yu, Yicheng He, Jing Xie, Xiaomin Wu, Zhichao Liu, Jiarui Zhang, Fuxiao Liu (* equal contribution)
Preprint 2026
TL;DR: MM-Zero trains one base VLM in three roles—Proposer, Coder (code-to-image), Solver—with GRPO and rich rewards, achieving self-evolution on multimodal reasoning without any seed images.
MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data
Zongxia Li*, Hongyang Du*, Chengsong Huang*, Xiyang Wu, Lantao Yu, Yicheng He, Jing Xie, Xiaomin Wu, Zhichao Liu, Jiarui Zhang, Fuxiao Liu (* equal contribution)
Preprint 2026
TL;DR: MM-Zero trains one base VLM in three roles—Proposer, Coder (code-to-image), Solver—with GRPO and rich rewards, achieving self-evolution on multimodal reasoning without any seed images.
VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation
Hongyang Du*, Junjie Ye*, Xiaoyan Cong*, Runhao Li, Jingcheng Ni, Aman Agarwal, Zeqi Zhou, Zekun Li, Randall Balestriero, Yue Wang (* equal contribution)
Preprint 2026
TL;DR: VideoGPA uses a geometry foundation model to mine preference pairs for DPO, nudging video diffusion toward 3D-consistent motion without hand-labeled human preferences.
VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation
Hongyang Du*, Junjie Ye*, Xiaoyan Cong*, Runhao Li, Jingcheng Ni, Aman Agarwal, Zeqi Zhou, Zekun Li, Randall Balestriero, Yue Wang (* equal contribution)
Preprint 2026
TL;DR: VideoGPA uses a geometry foundation model to mine preference pairs for DPO, nudging video diffusion toward 3D-consistent motion without hand-labeled human preferences.
How to Design and Train Your Implicit Neural Representation for Video Compression
Matthew Gwilliam, Roy Zhang, Namitha Padmanabhan, Hongyang Du, Abhinav Shrivastava
WACV 2026
TL;DR: We dissect NeRV-style video INRs in a library, propose Rabbit NeRV (RNeRV) as a strong recipe under equal training budget, then use hyper-networks + weight masking to speed encoding while keeping quality competitive.
How to Design and Train Your Implicit Neural Representation for Video Compression
Matthew Gwilliam, Roy Zhang, Namitha Padmanabhan, Hongyang Du, Abhinav Shrivastava
WACV 2026
TL;DR: We dissect NeRV-style video INRs in a library, propose Rabbit NeRV (RNeRV) as a strong recipe under equal training budget, then use hyper-networks + weight masking to speed encoding while keeping quality competitive.
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations for Synthetic Videos
Zongxia Li*, Xiyang Wu*, Yubin Qin, Hongyang Du, Guangyao Shi, Dinesh Manocha, Tianyi Zhou, Jordan Lee Boyd-Graber (* equal contribution)
NeurIPSs 2025 Poster
TL;DR: VideoHallu: benchmark of synthetic videos (e.g. from frontier generators) with QA that exposes commonsense and physics failures; we show strong MLLMs still struggle and that GRPO fine-tuning (with counterexamples) helps.
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations for Synthetic Videos
Zongxia Li*, Xiyang Wu*, Yubin Qin, Hongyang Du, Guangyao Shi, Dinesh Manocha, Tianyi Zhou, Jordan Lee Boyd-Graber (* equal contribution)
NeurIPSs 2025 Poster
TL;DR: VideoHallu: benchmark of synthetic videos (e.g. from frontier generators) with QA that exposes commonsense and physics failures; we show strong MLLMs still struggle and that GRPO fine-tuning (with counterexamples) helps.
A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
Zongxia Li*, Xiyang Wu*, Hongyang Du, Huy Nghiem, Guangyao Shi (* equal contribution)
CVPR Workshop 2025 Oral
TL;DR: Survey of large VLMs (roughly 2019–2024): model families, training, benchmarks, applications (agents, robotics, video), and open problems like hallucination and safety.
A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
Zongxia Li*, Xiyang Wu*, Hongyang Du, Huy Nghiem, Guangyao Shi (* equal contribution)
CVPR Workshop 2025 Oral
TL;DR: Survey of large VLMs (roughly 2019–2024): model families, training, benchmarks, applications (agents, robotics, video), and open problems like hallucination and safety.
Ipelets for the Convex Polygonal Geometry
Nithin Parepally, Ainesh Chatterjee, Auguste Gezalyan, Hongyang Du, Sukrit Mangla, Kenny Wu, Sarah Hwang, David Mount
40th International Symposium on Computational Geometry (SoCG) 2024
TL;DR: New Ipelets for the Ipe editor that draw and explore convex polygonal geometry—Funk/Hilbert metric balls, polar bodies, enclosing balls, MSTs, and polygon utilities (union, intersection, Minkowski sum).
Ipelets for the Convex Polygonal Geometry
Nithin Parepally, Ainesh Chatterjee, Auguste Gezalyan, Hongyang Du, Sukrit Mangla, Kenny Wu, Sarah Hwang, David Mount
40th International Symposium on Computational Geometry (SoCG) 2024
TL;DR: New Ipelets for the Ipe editor that draw and explore convex polygonal geometry—Funk/Hilbert metric balls, polar bodies, enclosing balls, MSTs, and polygon utilities (union, intersection, Minkowski sum).