794 citations · 966 across the 51 of their papers we have counts for
27 papers · 1 filter
ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning
Shengyuan Ding, Xinyu Fang, Ziyu Liu +10
Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to…
STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence
Zihan Liu, Zhikang Niu, Qiuyang Xiao +10
Despite rapid progress in Multi-modal Large Language Models and Large Audio-Language Models, existing audio benchmarks largely test semantics that can be recovered from text captio…
Think Visually, Reason Textually: Vision-Language Synergy in ARC
Beichen Zhang, Yuhang Zang, Xiaoyi Dong +4
Abstract reasoning from minimal examples remains a core unsolved problem for frontier foundation models such as GPT-5 and Grok 4. These models still fail to infer structured transf…
Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning
Yuhong Liu, Beichen Zhang, Yuhang Zang +6
Spatial understanding remains a weakness of Large Vision-Language Models (LVLMs). Existing supervised fine-tuning (SFT) and recent reinforcement learning with verifiable rewards (R…
OMeGa: Joint Optimization of Explicit Meshes and Gaussian Splats for Robust Scene-Level Surface Reconstruction
Yuhang Cao, Haojun Yan, Danya Yao
Neural rendering with Gaussian splatting has advanced novel view synthesis, and most methods reconstruct surfaces via post-hoc mesh extraction. However, existing methods suffer fro…
2nd Place Report of MOSEv2 Challenge 2025: Concept Guided Video Object Segmentation via SeC
Zhixiong Zhang, Shuangrui Ding, Xiaoyi Dong +3
Semi-supervised Video Object Segmentation aims to segment a specified target throughout a video sequence, initialized by a first-frame mask. Previous methods rely heavily on appear…
CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning
Long Xing, Xiaoyi Dong, Yuhang Zang +6
Image captioning is a fundamental task that bridges the visual and linguistic domains, playing a critical role in pre-training Large Vision-Language Models (LVLMs). Current state-o…
SPARK: Synergistic Policy And Reward Co-Evolving Framework
Ziyu Liu, Yuhang Zang, Shengyuan Ding +5
Recent Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) increasingly use Reinforcement Learning (RL) for post-pretraining, such as RL with Verifiable Rewards (…
SIM-CoT: Supervised Implicit Chain-of-Thought
Xilin Wei, Xiaoran Liu, Yuhang Zang +5
Implicit Chain-of-Thought (CoT) methods offer a token-efficient alternative to explicit CoT reasoning in Large Language Models (LLMs), but a persistent performance gap has limited…
CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
Zeyi Sun, Yuhang Cao, Jianze Liang +8
Autonomous agents for Graphical User Interfaces (GUIs) face significant challenges in specialized domains such as scientific computing, where both long-horizon planning and precise…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…
Beyond Fixed: Training-Free Variable-Length Denoising for Diffusion Large Language Models
Jinsong Li, Xiaoyi Dong, Yuhang Zang +3
Diffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capa…
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
Zeyi Sun, Ziyu Liu, Yuhang Zang +5
Repurposing large vision-language models (LVLMs) as computer use agents (CUAs) has led to substantial breakthroughs, primarily driven by human-labeled data. However, these models o…
Advancing Complex Video Object Segmentation via Progressive Concept Construction
Zhixiong Zhang, Shuangrui Ding, Xiaoyi Dong +7
We propose Segment Concept (SeC), a concept-driven video object segmentation (VOS) framework that shifts from conventional feature matching to the progressive construction and util…
ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing
Long Xing, Qidong Huang, Xiaoyi Dong +10
This paper presents ScaleCap, an inference-time scalable image captioning strategy that generates comprehensive and detailed image captions. The key challenges of high-quality imag…
Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings
Yubo Ma, Jinsong Li, Yuhang Zang +8
Despite the strong performance of ColPali/ColQwen2 in Visualized Document Retrieval (VDR), it encodes each page into multiple patch-level embeddings and leads to excessive memory u…
Visual Agentic Reinforcement Fine-Tuning
Ziyu Liu, Yuhang Zang, Yushan Zou +6
A key trend in Large Reasoning Models (e.g., OpenAI's o3) is the native agentic ability to use external tools such as web browsers for searching and writing/executing code for imag…
HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
Jiazi Bu, Pengyang Ling, Yujie Zhou +7
Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution im…
MM-IFEngine: Towards Multimodal Instruction Following
Shengyuan Ding, Shenxi Wu, Xiangyu Zhao +7
The Instruction Following (IF) ability measures how well Multi-modal Large Language Models (MLLMs) understand exactly what users are telling them and whether they are doing it righ…
Light-A-Video: Training-free Video Relighting via Progressive Light Fusion
Yujie Zhou, Jiazi Bu, Pengyang Ling +10
Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video…
Visual-RFT: Visual Reinforcement Fine-Tuning
Ziyu Liu, Zeyi Sun, Yuhang Zang +5
Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is sc…
BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning
Beichen Zhang, Yuhong Liu, Xiaoyi Dong +6
Large language models (LLMs) have demonstrated impressive ability in solving complex mathematical problems with multi-step reasoning and can be further enhanced with well-designed…
SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
Zihan Liu, Shuangrui Ding, Zhixiong Zhang +6
Text-to-song generation, the task of creating vocals and accompaniment from textual inputs, poses significant challenges due to domain complexity and data scarcity. Existing approa…
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
Xilin Wei, Xiaoran Liu, Yuhang Zang +9
While Rotary Position Embedding (RoPE) and its variants are widely adopted for their long-context capabilities, the extension of the 1D RoPE to video, with its complex spatio-tempo…