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Shiqi Wang

4 papers hereh-index 3154 citations7 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV1
  • cs.HC1
  • cs.IR1
  • cs.RO1
same name
  • Shiqi Wang — 23 papers, h 45
  • Shiqi Wang — 21 papers, h 16
  • Shiqi Wang — 20 papers
  • Shiqi Wang — 15 papers, h 18
  • Shiqi Wang — 15 papers, h 6
  • Shiqi Wang — 13 papers, h 19

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

most citedTowards Next-Generation LLM-based Recommender Systems: A Survey and Beyond

12 citations · 20 across the 4 of their papers we have counts for

collaborators

4 papers

cs.RO2026

Beyond Flat Policies: Hierarchical Post-Training for Embodied Agents in Robotic Manipulation

He Kong, Zengjue Chen, Qi Wang +6

Vision-language-action (VLA) models have demonstrated remarkable capabilities in robotic manipulation by leveraging pretrained vision-language models. However, existing post-traini…

cs.CV2026

From Bounding Boxes to Visual Reasoning: An On-Policy Data Annotation Tool for Vision-Language Models

Like Zhang, Runliang Niu, Shiqi Wang +5

Vision-language models (VLMs) are rapidly advancing toward sophisticated grounded structured visual reasoning. Training models for such advanced capabilities demands a new genre of…

cs.IR2024★ 12 cited

Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond

Qi Wang, Jindong Li, Shiqi Wang +7

Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields…

cs.HC2024★ 8 cited

ScreenAgent: A Vision Language Model-driven Computer Control Agent

Runliang Niu, Jindong Li, Shiqi Wang +6

Existing Large Language Models (LLM) can invoke a variety of tools and APIs to complete complex tasks. The computer, as the most powerful and universal tool, could potentially be c…

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