Yuan Si — accepted AI-conference papers

2026

MUSE: Multimodal Uncertainty-Based Self-Driven Evolution for Robust Physiological-Signal–Based Driver Fatigue Detection

Jiaheng Wang, Yuan Si, Ang Li, Zhenyu Wang, Tianheng Xu, Honglin Hu

AAAI 2026technical

Precise detection of driver mental fatigue is critical for reducing traffic accidents and enhancing road safety. Compared with vision-based detection—which is susceptible to illumination and occlusion—multimodal physiological‑signal-based approaches integrate complementary information from diverse

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2026

WorldAgen: Unified State-Action Prediction with Test-Time World Model Training

Chi Wan, Kangrui Wang, Yuan Si, Pingyue Zhang, Manling Li

AAAI 2026technical

How can vision-language-action (VLA) models adapt to new environments where world dynamics shift? While recent research has combined world modeling and action prediction to improve VLA performance, existing methods largely rely on pretraining in static datasets, without mechanisms for active adaptat

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