4 citations · 5 across the 27 of their papers we have counts for
29 papers
Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification
Yimeng Ye, Shuang Chen, Wenxuan Huang +8
While Reinforcement Learning (RL) effectively incentivizes reasoning in Large Language Models, current pipelines are hindered by training instability and rapid entropy collapse. Th…
Evidence-RL: Towards Evidence-intensive Visual Reasoning
Haojie Huang, Xinlei Yu, Chengming Xu +6
Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context. Existing perception-aware pos…
SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments
Chih-Ting Liao, Xi Xiao, Chunlei Meng +6
Multimodal large language models (MLLMs) have advanced static visual--spatial reasoning, yet they often fail to preserve long-horizon spatial coherence in embodied settings where b…
Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning
Zhikai Pan, Chih-Ting Liao, Chunrui Liu +5
Whether large language models (LLMs) construct internal spatial world models from pure-text descriptions remains contested, and whether such capabilities transfer across languages…
LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
Xiang An, Yin Xie, Feilong Tang +27
We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of mu…
4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +9
Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vision-language models (VLMs). Prio…
SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents
Yifan Zhou, Zhentao Zhang, Ziming Cheng +8
As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, re…
Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology
Jucheng Hu, Zhangquan Chen, Yulin Chen +9
Deciphering animal intent is a fundamental challenge in computational ethology, largely because of semantic aliasing, the phenomenon where identical external signals (e.g., a cat's…
Script-a-Video: Deep Structured Audio-visual Captions via Factorized Streams and Relational Grounding
Tencent Hunyuan Team
Advances in Multimodal Large Language Models (MLLMs) are transforming video captioning from a descriptive endpoint into a semantic interface for both video understanding and genera…
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
Xinlei Yu, Zhangquan Chen, Yongbo He +36
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an inc…
Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation
Zhiqi Yu, Zhangquan Chen, Mengting Liu +2
Reinforcement Learning with Verifiable Rewards (RLVR), particularly GRPO, has become the standard for eliciting LLM reasoning. However, its efficiency in exploration and difficulty…
Where and How to Prune: An Empirical Study of Visual Token Pruning for GUI Agent Navigation
Daiqiang Li, Zihao Pan, Zeyu Zhang +8
In recent years, GUI agents have demonstrated strong potential in navigation tasks. However, preserving complete historical screenshots introduces substantial computational overhea…
Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution
Chengyan Deng, Zhangquan Chen, Li Yu +3
Diffusion-based Real-World Image Super-Resolution (Real-ISR) achieves impressive perceptual quality but suffers from high computational costs due to iterative sampling. While recen…
OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention
Zhangquan Chen, Jiale Tao, Ruihuang Li +10
While humans perceive the world through diverse modalities that operate synergistically to support a holistic understanding of their surroundings, existing omnivideo models still f…
EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines
Shuo Zhang, Chaofa Yuan, Ryan Guo +11
While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work…
Dual Latent Memory for Visual Multi-agent System
Xinlei Yu, Chengming Xu, Zhangquan Chen +8
While Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration, empirical evidence reveals a counter-intuitive "scaling wall":…
Topology-Agnostic Animal Motion Generation from Text Prompt
Keyi Chen, Mingze Sun, Zhenyu Liu +2
Motion generation is fundamental to computer animation and widely used across entertainment, robotics, and virtual environments. While recent methods achieve impressive results, mo…
Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving
Dapeng Zhang, Zhenlong Yuan, Zhangquan Chen +5
Vision-Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving. However, existing VLAs often struggle with achieving efficient in…
VisMem: Latent Vision Memory Unlocks Potential of Vision-Language Models
Xinlei Yu, Chengming Xu, Guibin Zhang +7
Despite the remarkable success of Vision-Language Models (VLMs), their performance on a range of complex visual tasks is often hindered by a "visual processing bottleneck": a prope…
Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +8
Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limit…
LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
Xiang An, Yin Xie, Kaicheng Yang +20
We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial co…
Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow
Xinlei Yu, Chengming Xu, Guibin Zhang +8
Multi-Agent System (MAS) powered by Visual Language Models (VLMs) enables challenging tasks but suffers from a novel failure term, multi-agent visual hallucination snowballing, whe…
SIFThinker: Spatially-Aware Image Focus for Visual Reasoning
Zhangquan Chen, Ruihui Zhao, Chuwei Luo +4
Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods hav…
Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling
Xinlei Yu, Chengming Xu, Zhangquan Chen +6
The dominant paradigm of monolithic scaling in Vision-Language Models (VLMs) is failing for understanding and reasoning in documents, yielding diminishing returns as it struggles w…