Jinpeng Lu | Video World Models, Embodied AI, and Medical AI

👨‍🎓 About Me

I am a master's student in Information and Communication Engineering at the University of Science and Technology of China, advised by Prof. Zhiwei Xiong. Before joining USTC, I received a bachelor's degree in Data Science and Big Data Technology from Harbin Institute of Technology, Shenzhen.

My research interests include video world models, embodied AI, and medical AI. I study whether video generation models can preserve coherent task-relevant event states as a camera moves, looks away, and later returns. I also investigate how visual representations in vision-language-action models shape decisions and actions, and how medical AI systems can remain efficient, reliable, and clinically reviewable.

My recent work includes WRBench, a benchmark for testing whether generated events remain coherent across camera changes. I also co-authored VLA-Trace, which traces how vision-language-action models turn representations into behavior, and Pelican-Unify 1.0, which jointly generates future video and actions.

Related work has appeared at ICLR and MICCAI, and in Medical Image Analysis.

📢 I am actively seeking research collaborations and research internship opportunities. Please feel free to email me.

Video World Models I design evaluations that test whether generated events remain coherent as the camera moves and returns.
Embodied AI I study how visual representations in vision-language-action models drive reasoning, action, and future prediction.
Medical AI I have worked on efficient 3D medical imaging and pathology, with an emphasis on reliable model analysis.

🔥 News

2026.06Released first-author work Current World Models Lack a Persistent State Core and WRBench, a diagnostic benchmark that asks whether camera changes alter only what is seen rather than the event being generated.
2026.05Co-authored VLA-Trace on representation-to-behavior tracing and Pelican-Unify 1.0 on joint future-video and action generation.
2026.01Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation accepted to ICLR 2026.
2024.03H2ASeg accepted to MICCAI 2024 as an early-accept paper.

📘 Selected Publications

Full list: Google Scholar h-index 5 · i10-index 3

🌍 Video World Models & Generative Video Evaluation

arXiv 2026

WRBench source figure showing viewpoint changes and state-consistency evaluation

First Author 2026.06

Current World Models Lack a Persistent State Core

J. Lu, D. Zhu, H. Shi, L. Cai, G. Tang, Y. Chen, J. Cao, D. Tang, Y. Zhang, Y. Dai, X. Ju.

Question: When the camera changes only what is observed, does one task-relevant event state continue to evolve coherently?
Approach: WRBench separates camera execution, visual integrity, visible evolution, re-observation support, and conditional endpoint consistency across four native camera-control interfaces.
Finding: Across 9,600 videos from 23 models, better view synthesis and re-observation support did not reliably yield correct event endpoints.

Paper Project Code Artifacts Leaderboard Source Figure

🤖 Embodied AI & VLA Analysis

1 / 2

arXiv 2026

VLA-Trace source figure showing representation and behavior tracing framework

Co-author 2026.05

VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing

Question: How do internal VLA representations translate into decisions and rollout behavior?
Approach: VLA-Trace combines representation analysis, causal attention interventions, and behavioral probes across π_0.5 and OpenVLA.
Finding: The study identifies distinct adaptation and action-routing patterns, while revealing limits in fine-grained semantic following.

Paper Source Figure

arXiv 2026

Pelican-Unify source figure showing embodied intelligence model components

Co-author 2026.05

Pelican-Unify 1.0: A Unified Embodied Intelligence Model

Question: Can one embodied model connect visual understanding, reasoning, future prediction, and action generation?
Approach: Pelican-Unify uses a shared vision-language model, then jointly generates future video and action.
Training: Language, video, and action objectives are optimized together instead of as isolated specialist capabilities.

Paper Source Figure

🏥 Medical AI & Efficient 3D Perception

1 / 3

ICLR 2026

First Author CCF A 2026.01

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

J. Lu, L. Cai, Y. Chen, G. Tang, S. Jiang, H. Shi, Z. Xiong.

Task: Perform efficient multimodal 3D medical segmentation for volumetric PET/CT perception.
Method: Combine JL-guided convolution, paired window attention, and spatially decoupled knowledge transfer in VeloxSeg.
Result: Delivers a 1.66M-parameter segmentation model that keeps the architecture lightweight for 3D medical-image analysis.

Paper Code Source Figure

Tech Report 2025

Co-first Author 2025.10

Does DINOv3 Set a New Medical Vision Standard?

C. Liu*, Y. Chen*, H. Shi*, J. Lu*, B. Jian*, J. Pan*, L. Cai*, et al.

Task: Test whether DINOv3-style self-supervised visual features transfer reliably to medical vision tasks.
Method: Benchmark 2D and 3D classification, segmentation, and registration across multiple medical modalities.
Result: Identifies where transfer is useful, where task limits remain, and which modality gaps matter for medical foundation-model use.

Paper HF Paper Source Figure

MICCAI 2024

H2ASeg source figure showing PET/CT hierarchical adaptive interaction architecture

First Author CCF B 2024.03

H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images

J. Lu, J. Chen, L. Cai, S. Jiang, Y. Zhang.

Task: Segment tumor regions in paired PET/CT images under heterogeneous lesion appearance and modality imbalance.
Method: Use hierarchical adaptive interaction and weighting to fuse PET metabolic cues with CT anatomical structure.
Result: Improves robust lesion-region segmentation and establishes the medical-segmentation line that later led to VeloxSeg.

Paper Code Source Figure

Additional Medical AI Publications

AttriMIL: Revisiting Attention-based Multiple Instance Learning for Whole-slide Pathological Image Classification L. Cai, S. Huang, Y. Zhang, J. Lu, Y. Zhang. · 4th author

Medical Image Analysis 2025 · Contributor. Contributes to attention-based multiple-instance learning for whole-slide pathology image classification.
IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysis G. Tang, S. Jiang, J. Lu, L. Cai, Y. Zhang. · 3rd author

arXiv 2025 · Contributor. Contributes to interpretable graph-transformer modeling for pathology-based survival analysis.
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images L. Cai, J. Huang, Z. Zhu, J. Lu, Y. Zhang. · 4th author

arXiv 2023 · Contributor. Contributes to a localization-to-segmentation cascade for automatic whole-body PET/CT tumor segmentation.