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Guangyi Liu

Guangyi Liu has 4 papers in our library, in 2026, most of them on Evaluation & benchmarks; often with Pengxiang Zhao, WenHao Wang, Zhengxi Lu.

4 papers in our library · 2026

OpenAlex · h-index 1 · 4 works · 8 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 15 times in our library · ⭐ Paper Stars ranking: #9322

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Zhejiang UniversityHangzhou · China2026 · 2 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 3 worksHuman-Computer Interaction · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 2 worksEngineering · 1 workIndustrial and Manufacturing Engineering · 1 workSoftware · 1 work

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 2 works

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026 · 9 citations)
    Mobile GUI agents built on large language models often fail in rare, app-specific situations that pre-training or fine-tuning cannot fully cover.
  2. MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026 · 6 citations)
    Mobile agents driven by multimodal LLMs usually act only through the interface: they tap, swipe and type step by step. This is flexible but slow. Shortcuts such as APIs, deep-links and RPA scripts can skip whole sequences of GUI steps, but no benchmark has systematically tested agents that mix the two.
  3. UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026 · 1 citation)
    Multimodal GUI agents perform well on short mobile tasks but break down on long, memory-heavy ones. Existing 7B models lose about 90.9% of their accuracy on MemGUI-Bench. The authors trace these failures to three causes: forgetting earlier observations, losing track of task progress, and making arithmetic mistakes.
  4. FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems (2026)
    GUI agents built on vision–language models usually depend on centrally gathered, manually labelled interaction data, which is expensive and hard to scale.

 Co-authors here

12 of 22 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 15 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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