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researcher

Hui Liu

4 papers here

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

author position
  • middle author2
  • last author1

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL1
  • cs.CV1
  • cs.IR1
  • cs.LG1
ORCID 0000-0002-7887-7537
same name
  • Hui Liu — 21 papers, h 67
  • Hui Liu — 20 papers, h 25
  • Hui Liu — 10 papers
  • Hui Liu — 9 papers, h 22
  • Hui Liu — 9 papers, h 7
  • Hui Liu — 8 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

most citedRethinking Large Language Model Architectures for Sequential Recommendations

2 citations · 2 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2025

Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation

Pengxiang Li, Zechen Hu, Zirui Shang +15

Vision-language model (VLM) based GUI agents show promise for automating complex desktop and mobile tasks, but face significant challenges in applying reinforcement learning (RL):…

cs.CL2025

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association

Weiqi Wang, Limeng Cui, Xin Liu +14

Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-com…

cs.CV2025

ViLBench: A Suite for Vision-Language Process Reward Modeling

Haoqin Tu, Weitao Feng, Hardy Chen +3

Process-supervised reward models serve as a fine-grained function that provides detailed step-wise feedback to model responses, facilitating effective selection of reasoning trajec…

cs.IR2024★ 2 cited

Rethinking Large Language Model Architectures for Sequential Recommendations

Hanbing Wang, Xiaorui Liu, Wenqi Fan +7

Recently, sequential recommendation has been adapted to the LLM paradigm to enjoy the power of LLMs. LLM-based methods usually formulate recommendation information into natural lan…

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