5 papers
MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning
Letian Chen, Yiren Lu, Justin Fu +5
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them promising candidates for solvi…
Reflections and New Directions for Human-Centered Large Language Models
Caleb Ziems, Dora Zhao, Rose E. Wang +55
Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and…
LifeBench: A Benchmark for Long-Horizon Multi-Source Memory
Zihao Cheng, Weixin Wang, Yu Zhao +15
Long-term memory is fundamental for personalized agents capable of accumulating knowledge, reasoning over user experiences, and adapting across time. However, existing memory bench…
Enhanced Motion Forecasting with Plug-and-Play Multimodal Large Language Models
Katie Luo, Jingwei Ji, Tong He +4
Current autonomous driving systems rely on specialized models for perceiving and predicting motion, which demonstrate reliable performance in standard conditions. However, generali…
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation
Yichen Xie, Runsheng Xu, Tong He +9
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many en…