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WenHao Wang

WenHao Wang has 5 papers in our library, in 2026, most of them on Evaluation & benchmarks, Language models & pretraining, Code & programming languages; often with Guangyi Liu, Pengxiang Zhao, Siheng Chen.

5 papers in our library · 2026

OpenAlex · h-index 1 · 5 works · 3 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 21 times in our library · ⭐ Paper Stars ranking: #8783

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

🎓University of Massachusetts DartmouthNorth Dartmouth · United States2026 · 1 papercurrent🎓Zhejiang UniversityHangzhou · China2026 · 1 papercurrent

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 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksHuman-Computer Interaction · 3 worksInformation Systems · 1 workControl and Systems Engineering · 1 workEngineering · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 2 worksCode & programming languages · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20265 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. MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026 · 7 citations)
    LLM agents increasingly need to work with tools exposed through the Model Contextual Protocol (MCP), but even strong models handle real MCP servers poorly.
  3. 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.
  4. Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation (2026 · 2 citations)
    Robots that interpret natural-language commands usually depend on cloud-hosted or very large LLMs. This rules out platforms such as UAVs and small ground vehicles, which may have weak connectivity and limited compute. Ro-SLM addresses this by transferring LLM knowledge into a small model that runs onboard.
  5. 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 17 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 21 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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