◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Xu Yang

13 papers hereh-index 7408 citations18 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author9
  • last author1

Across the 11 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV7
  • cs.LG3
  • cs.CL2
  • cs.NE1
same name
  • Xu Yang — 17 papers, h 15
  • Xu Yang — 17 papers, h 12
  • Xu Yang — 13 papers, h 15
  • Xu Yang — 12 papers, h 4
  • Xu Yang — 11 papers, h 8
  • Xu Yang — 11 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212026
most citedExploring Diverse In-Context Configurations for Image Captioning

11 citations · 14 across the 12 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CV2026

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…

cs.CL2026

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…

cs.LG2026

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…

cs.LG2026

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…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.