I am a senior undergraduate student with a strong interest in Multimodal LLMs, Tool-Using Agents and Spatial Intelligence. My research vision is to develop super-intelligent yet lightweight Multimodal LLMs, enabling machines to perceive, plan, reason, and act through the autonomous integration of multi-sensory signals, external tools, and knowledge, giving rise to superhuman yet controllable intelligence in downstream tasks such as Video Understanding, Embodied Robotics, Autonomous Driving, and Medical Diagnosis.

I'm applying for PhD programs in the 2026 Fall and Research Internship in the 2026 Summer. I am always open to collaborate, feel free to reach out! Email: open.qiancx[at]gmail.com | WeChat: qiancxdotcom

Research Interests and Highlights

  • Multimodal Intelligence: How can machines extract learnable neural-symbolic concepts from the complex physical world to enable grounded understanding, integration, interaction, and decision-making across multi-sensory signals, ultimately leading to superhuman yet interpretable intelligence?

  • Generative World Modeling and Spatial Intelligence: Toward multimodal superintelligence, guiding foundation models to deeply understand the underlying mechanisms of the complex physical world, internalize world dynamics within their parameter space, and reason about complex object properties and interactions in dynamic 3D environments.

  • In-the-wild Agents: How can we teach foundation models to see, plan, and act in open-world settings, while autonomously interacting with external environments such as tools, knowledge bases, and simulators, thereby continuously extending their capability boundaries in real-world applications?

๐Ÿ”ฅ News

  • 2026.02: ย ๐ŸŽ‰๐ŸŽ‰ Our work fMRI-LM on Medical Foundation Models has been accepted by CVPR 2026!
  • 2026.01: ย ๐ŸŽ‰๐ŸŽ‰ Three first/co-first author papers have been accepted by ICLR 2026!
    • DecAlign: Aligning Cross-Modal Semantics for Multimodal Foundation Models
    • AutoDrive-Rยฒ: Towards Physical-Grounded Multimodal Reasoning for Autonomous Driving
    • Video-STAR: Tool-Augmented Agentic RL for Thinking with Videos
  • 2026.01: ย ๐ŸŽ‰๐ŸŽ‰ We propose ProgressLM, which further investigates whether VLMs can acquire human-like, generalizable mental understanding and simulation in embodied scenarios from a single example, and serves as an early step toward building general-purpose reward models. See More: [Website] [Paper] [Code] [Model] [Dataset]
  • 2025.11: ย ๐ŸŽ‰๐ŸŽ‰ Our work LiMT, an unified multi-task liver image benchmark work, has been accepted by Journal of Biomedical and Health Informatics (JBHI)!
  • 2025.10: ย ๐ŸŽ‰๐ŸŽ‰ Our work DVP-MVS++, a multi-view stereo method that integrates depth-normal-edge priors and visibility guidance for robust 3D Reconstruction, has been accepted by IEEE Transactions on Circuits and Systems for Video Technology!
  • 2025.10: ย ๐ŸŽ‰๐ŸŽ‰ My first-author work on Medical Segmentation under sparse and noisy labeled annotations has been accepted by BIBM AIBH 2025!
  • 2025.10: ย ๐ŸŽ‰๐ŸŽ‰ We propose Video-STAR, a powerful Tool-Augmented Agentic RL approach for Thinking with Videos. On open-vocabulary action recognition benchmarks like K-400 and HMDB-51, our 3B VLM achieves nearly 40% accuracy improvement over base models!๐Ÿ”ฅ
  • 2025.09: ย ๐ŸŽ‰๐ŸŽ‰ Our work HALF-GS, an efficient dynamic 3D reconstruction framework combining sparse anchors, self-supervised guidance, and hierarchical propagation to improve reconstruction quality and temporal consistency, has been accepted by NeurIPS 2025!
  • 2025.09: ย ๐ŸŽ‰๐ŸŽ‰ We propose AutoDrive-Rยฒ, Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving. Weโ€™re also honored that our work was featured by AutoDrive Heart (่‡ชๅŠจ้ฉพ้ฉถไน‹ๅฟƒ)!
  • 2025.08: ย ๐ŸŽ‰๐ŸŽ‰ Our work Re-Align has been accepted by EMNLP 2025 Main Conference!
  • 2025.07: ย ๐ŸŽ‰๐ŸŽ‰ Our work on Generalizable Medical Vision has been Accepted by IEEE Transactions on Medical Imaging.
  • 2025.05: ย ๐ŸŽ‰๐ŸŽ‰ Our work CLIMD has been Early Accepted by MICCAI 2025 (Top 9%).
  • 2025.03: ย ๐ŸŽ‰๐ŸŽ‰ Excited to propose my first-author work DecAlign, a novel cross-modal decoupling and alignment framwork for multimodal representation learning.
  • 2024.11: ย ๐ŸŽ‰๐ŸŽ‰ Excited to propose my first-author work DynCIM, a novel dynamic multimodal curriculum learning framework in addressing cross-modal competition and imbalances, which is now available on ArXiv!
  • 2024.10: ย ๐ŸŽ‰๐ŸŽ‰ We propose FASS, a novel frequency domain-enhanced approach for Medical Image Segmentation under Low-Contrast environment.

๐Ÿ“ Selected Publications

ICLR 2026
sym

DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning

Multimodal Alignment Foundation Model Interpretability

ICLR 2026

Chengxuan Qian, Shuo Xing, Shawn Li, Yue Zhao, Zhengzhong Tuโ€ .

Preprint
sym

ProgressLM: Towards Progress Reasoning in Vision-Language Models

Spatial Intelligence Embodied Robotics Data-Centric Multimodal Reasoning Open-World Applications

Website Paper Code Model Dataset

Jianshu Zhang*, Chengxuan Qian*, Haosen Sun, Haoran Lu, Dingcheng Wang, Letian Xue, Han Liu

ICLR 2026
sym

AutoDrive-Rยฒ: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving

Multimodal Reasoning Autonomous Driving Open-World Applications

Featured by AutoDrive Heart (่‡ชๅŠจ้ฉพ้ฉถไน‹ๅฟƒ)

ICLR 2026

Zhenlong Yuan*, Chengxuan Qian*, Jing Tang, Jinguo Luo, Rui Chen, Lei Sun, Xiangxiang Chu, Yujun Cai, Dapeng Zhang, Shuo Li.

ICLR 2026
sym

Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools

Think with Videos Tool-Using Agent Multi-turn Agentic RL

ICLR 2026

Zhenlong Yuan, Xiangyan Qu, Chengxuan Qianโ€ , Rui Chen, Jing Tang, Lei Sun, Xiangxiang Chu, Dapeng Zhang, Yiwei Wang, Yujun Cai, Shuo Li.

CVPR 2026
sym

fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding

Medical LLMs Data-Centric Foundation Model

CVPR 2026

Yuxiang Wei, Yanteng Zhang, Xi Xiao, Chengxuan Qian, Tianyang Wang, Vince D. Calhoun โ€ .

MICCAI 2025
sym

CLIMD: A Curriculum Learning Framework for Imbalanced Multimodal Diagnosis

Modality Imbalances Medical AI Curriculum Learning

MICCAI 2025 Early Accept (Top 9% Paper)

Kai Han, Chongwen Lyu, Chengxuan Qian, Siqi Ma, Jun Chenโ€ , Zhe Liuโ€ ,

IEEE TMI 2025
sym

Region Uncertainty Estimation for Medical Image Segmentation with Noisy Labels

Medical Segmentation Noisy Labels Sparse Annotation Uncertainty Estimation

IEEE Transaction on Medical Imaging, 2025

Kai Han, Shuhui Wang, Jun Chenโ€ , Chengxuan Qian, Chongwen Lyu, Siqi Ma, Victor S. Shengโ€ , Qingming Huangโ€ , Zhe Liuโ€ .

๐ŸŒŸ Misc

Iโ€™m grateful for the mentorship of Prof. Zhengzhong Tu (TAMU), Prof. Yue Zhao (USC), Prof. Han Liu & Manling Li (Northwestern University). Outside of research, I enjoy Photography๐Ÿ“น, swimming๐ŸŠ, biking๐Ÿšด, billiards๐ŸŽฑ, table tennis๐Ÿ“. I strive to stay energetic every day and maintain a strong sense of passion for both academic research and life.

๐ŸŽ– Academical Services

  • Journal Reviewer: IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Multimedia (TMM).
  • Conference Reviewer: ICME 2025-2026, AAAI 2026, ICASSP 2026, CVPR 2026
  • Workshop Reviewer: ACL 2025 SRW, NeurIPS 2025 Imageomics, NeurIPS 2025 Efficient Reasoning