1 citations · 4 across the 11 of their papers we have counts for
13 papers
Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation
Wonsuhk Jung, Sundhar Vinodh Sangeetha, Chen Xu +4
Goal-conditioned imitation learning (GCIL) with flow matching is a promising framework that can represent multimodal behaviors while adapting to diverse, user-specified goals, yet…
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters
Ailin Huang, Ang Li, Aobo Kong +213
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most wh…
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…
Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot
Chenghao Yin, Da Huang, Di Yang +16
The development of robust and generalizable robot learning models is critically contingent upon the availability of large-scale, diverse training data and reliable evaluation bench…
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…
Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Framework
Junwei Liu, Chen Xu, Chong Wang +5
Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches larg…
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…
Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction
Chengzhi Xu, Yuyang Wang, Lai Wei +2
Recently, multimodal large language models (MLLMs) have attracted increasing research attention due to their powerful visual understanding capabilities. While they have achieved im…
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies
Chen Xu, Tony Khuong Nguyen, Emma Dixon +7
Recent years have witnessed impressive robotic manipulation systems driven by advances in imitation learning and generative modeling, such as diffusion- and flow-based approaches.…
Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction
Ailin Huang, Boyong Wu, Bruce Wang +142
Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such…