Chief Scientist at X-Era AI. Associate Professor and Young Scientist at Sun Yat-sen University. Former Research Fellow at the University of Oxford.

Joined April 2012
GPT meets control theory; how can GPT control a robot?
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Guangrun Wang retweeted
When Will Physical AGI Arrive? Here’s What Our Team Is Doing
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When Will Physical AGI Arrive? Here’s What Our Team Is Doing
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Guangrun Wang retweeted
Replying to @fly51fly
This paper has been accepted to CVPR 2026: Weiqi Li, Quande Zhang, Ruifeng Zhai, Liang Lin, Guangrun Wang, VLA Models Are More Generalizable Than You Think: Revisiting Physical and Spatial Modeling
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Guangrun Wang retweeted
RADAR: Benchmarking Vision-Language-Action Generalization via Real-World Dynamics, Spatial-Physical Intelligence, and Autonomous Evaluation Yuhao Chen, Zhihao Zhan, Xiaoxin Lin, Zijian Song, Hao Liu, Qinhan Lyu, Yubo Zu, Xiao Chen, … arxiv.org/abs/2602.10980 [𝚌𝚜.𝚁𝙾]
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To address this, we propose a one-shot adaptation framework that recalibrates visual representations through lightweight learnable updates. We apply a global affine transformation to visual tokens and improves Libero viewpoint accuracy from 48.5% to 87.1% with only 4K parameters.
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Vision-language-action (VLA) models achieve strong in-distribution performance but degrade sharply under novel camera viewpoints and visual perturbations. We show that this brittleness primarily arises from misalignment in Spatial Modeling, rather than Physical Modeling.
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2.Introducing time-weighted cross-entropy loss during training to avoid the over-smoothing problem caused by MSE; 3. Using an argmax + one-hot feedback iteration during inference to achieve true step-by-step discrete denoising.
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For the first time, this mechanism faithfully transplants the principles of diffusion into the world of discrete symbols, achieving true “symbol-level diffusion.”
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Are diffusion language models all wrong? SYSU team proposes the first authentic discrete diffusion model
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