11 citations · 13 across the 8 of their papers we have counts for
8 papers
Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents
Shijue Huang, Hangyu Guo, Guanting Dong +8
Multimodal deep search requires an agent to solve open-world problems by chaining search, tool use, and visual reasoning over evolving textual and visual context. Two bottlenecks l…
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…
A Novel Cross-Perturbation for Single Domain Generalization
Dongjia Zhao, Lei Qi, Xiao Shi +2
Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. However, the limited diversity in the…
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…