activity
20232026
most citedRevColV2: Exploring Disentangled Representations in Masked Image Modeling

6 citations · 7 across the 15 of their papers we have counts for

collaborators

17 papers

cs.CL2026

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…

cs.RO2026

DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI

En Yu, Haoran Lv, Jianjian Sun +46

Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed…

cs.CL2026

PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering

Xiangfeng Wang, Hangyu Guo, Yanlin Lai +11

While model-based verifiers are essential for scaling Reinforcement Learning with Verifiable Rewards (RLVR), current outcome-centric verification paradigms primarily focus on the c…

cs.AI2026

GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments

Haodong Li, Jingwei Wu, Quan Sun +18

An image generation model used as a graphical user interface (GUI) environment must produce not only a plausible screen but also the correct response to an action. Existing visual-…

cs.CL2026

R-Align: Enhancing Generative Reward Models through Rationale-Centric Meta-Judging

Yanlin Lai, Mitt Huang, Hangyu Guo +11

Reinforcement Learning from Human Feedback (RLHF) remains indispensable for aligning large language models (LLMs) in subjective domains. To enhance robustness, recent work shifts t…

cs.CL2026

BoRP: Bootstrapped Regression Probing for Scalable and Human-Aligned LLM Evaluation

Peng Sun, Xiangyu Zhang, Duan Wu +5

Accurate evaluation of user satisfaction is critical for iterative development of conversational AI. However, for open-ended assistants, traditional A/B testing lacks reliable metr…

cs.CV2025

Thinking in 360°: Humanoid Visual Search in the Wild

Heyang Yu, Yinan Han, Xiangyu Zhang +9

Humans rely on the synergistic control of head (cephalomotor) and eye (oculomotor) to efficiently search for visual information in 360°. However, prior approaches to visual search…

cs.CL2025★ 1 cited

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…

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

WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling

Jiacheng Li, Jianchao Tan, Zhidong Yang +11

Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architect…

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.CV2025

Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning

Yana Wei, Liang Zhao, Jianjian Sun +15

The remarkable reasoning capability of large language models (LLMs) stems from cognitive behaviors that emerge through reinforcement with verifiable rewards. This work investigates…

stat.ML2025

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes

Ruijia Zhang, Xiangyu Zhang, Zhengling Qi +2

Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual…

cs.LG2025

Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Jingcheng Hu, Yinmin Zhang, Qi Han +3

We introduce Open-Reasoner-Zero, the first open source implementation of large-scale reasoning-oriented RL training on the base model focusing on scalability, simplicity and access…

cs.LG2025

Block Flow: Learning Straight Flow on Data Blocks

Zibin Wang, Zhiyuan Ouyang, Xiangyun Zhang

Flow-matching models provide a powerful framework for various applications, offering efficient sampling and flexible probability path modeling. These models are characterized by fl…

cs.LG2024

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning

Xiangyu Zhang

The generation of drug-like molecules is crucial for drug design. Existing reinforcement learning (RL) methods often overlook structural information. However, feature engineering-b…

cs.CV2023★ 6 cited

RevColV2: Exploring Disentangled Representations in Masked Image Modeling

Qi Han, Yuxuan Cai, Xiangyu Zhang

Masked image modeling (MIM) has become a prevalent pre-training setup for vision foundation models and attains promising performance. Despite its success, existing MIM methods disc…