94 citations · 94 across the 14 of their papers we have counts for
16 papers
Understanding Machine Taste: Aesthetic Agreement Across Foundation Models in K-pop
Kaishen Wang, Yucheng Pan, Xuehao Cui +2
As foundation models enter creative industries, understanding their aesthetic preferences becomes an important step toward AI-assisted creative decision making. In this work, we in…
VideoResearcher: Self-Improving Tool Design for Long-Video Understanding
Dingqiang Ye, Dongdi Zhao, Kaishen Wang +11
Video agents have made substantial progress in long-video understanding. Yet effective video-agent systems require costly, time-consuming manual design and trial and error. Current…
Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding
Kaishen Wang, Dongdi Zhao, Yijun Liang +4
Vision-language models (VLMs) have made substantial progress in long-video understanding, with standard backbone models typically answering questions from frames sampled across the…
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
Kaishen Wang, Tong Zheng, Xuehao Cui +3
Large reasoning models (LRMs) improve language model capabilities by generating explicit thinking traces before final answers. In factuality-oriented question answering (QA), such…
Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention
Chaowen Yan, Kaishen Wang, Yong Wang +2
Oracle Bone Inscriptions (OBIs) recognition plays a crucial role in understanding ancient Chinese culture. However, accurately recognizing OBIs remains highly challenging due to th…
Unsafe by Reciprocity: How Generation-Understanding Coupling Undermines Safety in Unified Multimodal Models
Kaishen Wang, Heng Huang
Recent advances in Large Language Models (LLMs) and Text-to-Image (T2I) models have led to the emergence of Unified Multimodal Models (UMMs), where multimodal understanding and ima…
Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing
Tong Zheng, Chengsong Huang, Runpeng Dai +9
Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-traj…
Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following
Tianyi Xiong, Yi Ge, Ming Li +13
Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences.…
ImAgent: A Unified Multimodal Agent Framework for Test-Time Scalable Image Generation
Kaishen Wang, Ruibo Chen, Tong Zheng +1
Recent text-to-image (T2I) models have made remarkable progress in generating visually realistic and semantically coherent images. However, they still suffer from randomness and in…
Enhancing Feature Fusion of U-like Networks with Dynamic Skip Connections
Yue Cao, Quansong He, Kaishen Wang +3
U-like networks have become fundamental frameworks in medical image segmentation through skip connections that bridge high-level semantics and low-level spatial details. Despite th…
HalluRNN: Mitigating Hallucinations via Recurrent Cross-Layer Reasoning in Large Vision-Language Models
Le Yu, Kaishen Wang, Jianlong Xiong +3
Though Large Vision-Language Models (LVLMs) have achieved remarkable performance across various tasks, they are still prone to hallucinations-generating outputs that are textually…
FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks
Quansong He, Xiangde Min, Kaishen Wang +1
Medical image segmentation is a critical task in computer vision, with UNet serving as a milestone architecture. The typical component of UNet family is the skip connection, howeve…
Zer0-Jack: A Memory-efficient Gradient-based Jailbreaking Method for Black-box Multi-modal Large Language Models
Tiejin Chen, Kaishen Wang, Hua Wei
Jailbreaking methods, which induce Multi-modal Large Language Models (MLLMs) to output harmful responses, raise significant safety concerns. Among these methods, gradient-based app…
Strengthening Layer Interaction via Dynamic Layer Attention
Kaishen Wang, Xun Xia, Jian Liu +2
In recent years, employing layer attention to enhance interaction among hierarchical layers has proven to be a significant advancement in building network structures. In this paper…
Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making
Jingqing Ruan, Kaishen Wang, Qingyang Zhang +2
Many complicated real-world tasks can be broken down into smaller, more manageable parts, and planning with prior knowledge extracted from these simplified pieces is crucial for hu…
PanGu-: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
Wei Zeng, Xiaozhe Ren, Teng Su +35
Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demon…