activity
20232026
most citedScale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution

3 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

STEP3-VL-10B Technical Report

Ailin Huang, Chengyuan Yao, Chunrui Han +90

We present STEP3-VL-10B, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. STEP3-…

cs.LG2026

PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning

Jingcheng Hu, Yinmin Zhang, Shijie Shang +17

We introduce Parallel Coordinated Reasoning (PaCoRe), a training-and-inference framework designed to overcome a central limitation of contemporary language models: their inability…

cs.CV2025

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale

NextStep Team, Chunrui Han, Guopeng Li +47

Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ ve…

cs.LG2025

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…

cs.SD2025

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…

cs.CL20251 cited

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…