3 papers
cs.AI2026
Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving
Zhengqi Sun, Yiwen Sun, Boxuan Liu +3
Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic traffic. Existing methods ei…
cs.CV2026
Replacement Learning: Training Neural Networks with Fewer Parameters
Yuming Zhang, Peizhe Wang, Tianyang Han +5
End-to-end training with full-depth backpropagation remains the dominant paradigm for optimizing deep neural networks, but its efficiency deteriorates as models grow deeper. Since…
eess.SP2024
Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition
Ziwei Zhang, Mengtao Zhu, Jiabin Liu +2
Automatic Modulation Recognition (AMR) is a crucial technology in the domains of radar and communications. Traditional AMR approaches assume a closed-set scenario, where unknown sa…