Wenhao Wang

Orcid: 0009-0005-2165-9693

Affiliations:
  • Zhejiang University, Hangzhou, China


According to our database1, Wenhao Wang authored at least 16 papers between 2024 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Towards Self-Evolving Agentic Literature Retrieval.
CoRR, May, 2026

DataMaster: Data-Centric Autonomous AI Research.
CoRR, May, 2026

FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems.
CoRR, April, 2026

MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments.
CoRR, February, 2026

MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

2025
InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents.
CoRR, October, 2025

LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects.
CoRR, April, 2025

VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization.
CoRR, April, 2025

FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data.
CoRR, March, 2025

FedMobileAgent: Training Mobile Agents Using Decentralized Self-Sourced Data from Diverse Users.
CoRR, February, 2025

LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects.
Trans. Mach. Learn. Res., 2025

FedMABench: Benchmarking Mobile GUI Agents on Decentralized Heterogeneous User Data.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

2024
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units.
IEEE Trans. Intell. Transp. Syst., November, 2024

FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units.
CoRR, 2024

OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024


  Loading...