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I am a PhD candidate at Sun Yat-sen University, advised by Prof. Xiaodan Liang. My research agenda is Building the Reasoning Foundation of Physical AI: enabling multimodal systems to perceive, reason, model, and interact with the physical world.
I pursue this agenda through three core questions:
1) How can we elicit slow thinking and reliable supervision in multimodal models?
2) How can we build data-centric learning frameworks for scientific and physical intelligence?
3) How can we evaluate whether model reasoning is truly grounded in physical evidence?
Full publication list on Google Scholar. (* denotes equal contribution)
AtomThink: Multimodal Slow Thinking with Atomic Step Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026
Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026
SeePhys: Does Seeing Help Thinking? – Benchmarking Vision-Based Physics Reasoning
Neural Information Processing Systems (NeurIPS), 2025.
EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2025
Toward robust diagnosis: A contour attention preserving adversarial defense for covid-19 detection
Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Applied Soft Computing, 2021
Thinking with Geometry: Active Geometry Integration for Spatial Reasoning
arXiv preprint, 2026
RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training
arXiv preprint, 2026
Outstanding Graduate of SYSU
National Scholarship
Postgraduate Scholarship of SYSU