12 citations · 74 across the 58 of their papers we have counts for
64 papers
HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models
Jiazi Bu, Pengyang Ling, Yujie Zhou +10
Text-Image-to-Video (TI2V) models are an emerging unified architecture, where a single model simultaneously supports text-to-video (T2V) and image-to-video (I2V) generation. Given…
MLLM-DataEngine: Closing the Loop of Multimodal Instruction Tuning Data Generation
Zhiyuan Zhao, Bin Wang, Linke Ouyang +5
In this paper, we propose MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop iteration, the MLLM-DataEngine…
Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games
Shengyuan Ding, Xilin Wei, Xinyu Fang +4
Deploying multimodal foundation models as closed-loop policies increasingly requires conditioning actions on observations that are no longer visible. However, existing benchmarks e…
From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space
Jiazi Bu, Pengyang Ling, Yujie Zhou +8
Group Relative Policy Optimization (GRPO) has emerged as a powerful framework for preference alignment in text-to-image (T2I) flow models. However, we observe that the standard par…
Visual-ERM: Reward Modeling for Visual Equivalence
Ziyu Liu, Shengyuan Ding, Xinyu Fang +7
Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representations with high visual fideli…
Visual Self-Refine: A Pixel-Guided Paradigm for Accurate Chart Parsing
Jinsong Li, Xiaoyi Dong, Yuhang Zang +3
While Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities for reasoning and self-correction at the textual level, these strengths provide minimal benefit…
Demo-ICL: In-Context Learning for Procedural Video Knowledge Acquisition
Yuhao Dong, Shulin Tian, Shuai Liu +6
Despite the growing video understanding capabilities of recent Multimodal Large Language Models (MLLMs), existing video benchmarks primarily assess understanding based on models' s…
SS4D: Native 4D Generative Model via Structured Spacetime Latents
Zhibing Li, Mengchen Zhang, Tong Wu +3
We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimi…
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…
DiCache: Let Diffusion Model Determine Its Own Cache
Jiazi Bu, Pengyang Ling, Yujie Zhou +4
Recent years have witnessed the rapid development of acceleration techniques for diffusion models, especially caching-based acceleration methods. These studies seek to answer two f…
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…
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…
Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning
Yibin Wang, Zhimin Li, Yuhang Zang +6
Recent advancements highlight the importance of GRPO-based reinforcement learning methods and benchmarking in enhancing text-to-image (T2I) generation. However, current methods usi…
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…