1 citations · 2 across the 5 of their papers we have counts for
8 papers
MTDrive: Multi-turn Interactive Reinforcement Learning for Autonomous Driving
Xidong Li, Mingyu Guo, Chenchao Xu +5
Trajectory planning is a core task in autonomous driving, requiring the prediction of safe and comfortable paths across diverse scenarios. Integrating Multi-modal Large Language Mo…
URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model
Zhe Li, Xiang Bai, Jieyu Zhang +5
Constructing accurate digital twins of articulated objects is essential for robotic simulation training and embodied AI world model building, yet historically requires painstaking…
BLM: A Boundless Large Model for Cross-Space, Cross-Task, and Cross-Embodiment Learning
Wentao Tan, Bowen Wang, Heng Zhi +15
Multimodal large language models (MLLMs) have advanced vision-language reasoning and are increasingly deployed in embodied agents. However, significant limitations remain: MLLMs ge…
Step-Audio 2 Technical Report
Boyong Wu, Chao Yan, Chen Hu +106
This paper presents Step-Audio 2, an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation. By integrating a latent…
Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding
StepFun, :, Bin Wang +195
Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hard…
Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Ailin Huang, Bingxin Li, Bruce Wang +73
Large Audio-Language Models (LALMs) have significantly advanced intelligent human-computer interaction, yet their reliance on text-based outputs limits their ability to generate na…