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Yaqing Wang

13 papers hereh-index 344 citations26 works total

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

author position
  • first author1
  • middle author10
  • last author2

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

fields
  • cs.LG6
  • cs.AI5
  • cs.IR2
same name
  • Yaqing Wang — 16 papers, h 21
  • Yaqing Wang — 15 papers, h 12
  • Yaqing Wang — 6 papers, h 4
  • Yaqing Wang — 5 papers, h 4
  • Yaqing Wang — 5 papers, h 1
  • Yaqing Wang — 4 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
20242026
most citedPERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching

3 citations · 5 across the 13 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.AI2026

Mechanistic Attention Guidance for Agent Memory Refinement

Yechao Hong, Haiquan Qiu, Yaqing Wang +1

Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections. However, this text-based paradigm rarely inco…

cs.AI2026

PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent

Hongyi Nie, Xunyuan Liu, Yudong Bai +4

Smartphone GUI agents execute tasks by operating directly on app interfaces, offering a path to broad capability without deep system integration. However, real-world smartphone use…

cs.LG2026

DGNet: Discrete Green Networks for Data-Efficient Learning of Spatiotemporal PDEs

Yingjie Tan, Quanming Yao, Yaqing Wang

Spatiotemporal partial differential equations (PDEs) underpin a wide range of scientific and engineering applications. Neural PDE solvers offer a promising alternative to classical…

cs.LG2026

Self-Generative Adversarial Fine-Tuning for Large Language Models

Shiguang Wu, Yaqing Wang, Quanming Yao

Fine-tuning large language models (LLMs) for alignment typically relies on supervised fine-tuning or reinforcement learning from human feedback, both limited by the cost and scarci…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.