11 citations · 19 across the 13 of their papers we have counts for
13 papers
Covering Human Action Space for Computer Use: Data Synthesis and Benchmark
Miaosen Zhang, Xiaohan Zhao, Zhihong Tan +14
Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions is still poor, limiting user…
XPERT: Expert Knowledge Transfer for Effective Training of Language Models
Chang Liu, Boyu Shi, Xu Yang +1
Mixture-of-Experts (MoE) language models organize knowledge into explicitly routed expert modules, making expert-level representations traceable and analyzable. By analyzing expert…
Learngene Search Across Multiple Datasets for Building Variable-Sized Models
Boyu Shi, Junbo Zhou, Chang Liu +3
Deep learning methods are widely used under diverse resource constraints, resulting in models of varying sizes, such as the Vision Transformer (ViT) series. Deploying these models…
Towards On-Policy SFT: Distribution Discriminant Theory and its Applications in LLM Training
Miaosen Zhang, Yishan Liu, Shuxia Lin +8
Supervised fine-tuning (SFT) is computationally efficient but often yields inferior generalization compared to reinforcement learning (RL). This gap is primarily driven by RL's use…
Distribution-Conditional Generation: From Class Distribution to Creative Generation
Fu Feng, Yucheng Xie, Xu Yang +2
Text-to-image (T2I) diffusion models are effective at producing semantically aligned images, but their reliance on training data distributions limits their ability to synthesize tr…
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization
Huiyi Chen, Jiawei Peng, Kaihua Tang +2
In-context learning (ICL) enables Large Vision-Language Models (LVLMs) to adapt to new tasks without parameter updates, using a few demonstrations from a large support set. However…
Redefining <Creative> in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation
Fu Feng, Yucheng Xie, Xu Yang +2
``Creative'' remains an inherently abstract concept for both humans and diffusion models. While text-to-image (T2I) diffusion models can easily generate out-of-distribution concept…
LIVE: Learnable In-Context Vector for Visual Question Answering
Yingzhe Peng, Chenduo Hao, Xu Yang +3
As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefi…
Manipulating the Label Space for In-Context Classification
Haokun Chen, Xu Yang, Yuhang Huang +3
After pre-training by generating the next word conditional on previous words, the Language Model (LM) acquires the ability of In-Context Learning (ICL) that can learn a new task co…
Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models
Qiufeng Wang, Xu Yang, Shuxia Lin +2
During the continuous evolution of one organism's ancestry, its genes accumulate extensive experiences and knowledge, enabling newborn descendants to rapidly adapt to their specifi…
Genes in Intelligent Agents
Fu Feng, Jing Wang, Xu Yang +1
The genes in nature give the lives on earth the current biological intelligence through transmission and accumulation over billions of years. Inspired by the biological intelligenc…
Exploring Diverse In-Context Configurations for Image Captioning
Xu Yang, Yongliang Wu, Mingzhuo Yang +2
After discovering that Language Models (LMs) can be good in-context few-shot learners, numerous strategies have been proposed to optimize in-context sequence configurations. Recent…
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Boyu Zhang, Jiayuan Chen, Yinfei Xu +3
The action quality assessment (AQA) of videos is a challenging vision task since the relation between videos and action scores is difficult to model. Thus, AQA has been widely stud…