XXXiaobo Xue
Xiaobo Xue
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiaobo Xue has a total of 42 co-authors. Here are the top co-authors by number of shared publications:
- Biao Lyu
Alibaba Cloud, Hangzhou, China
4 shared publications - Shize Zhang
Alibaba Group, Hangzhou, China
4 shared publications - Xiaoqing Sun
Alibaba Cloud, Hangzhou, China
4 shared publications - Enge Song
Alibaba Cloud, Hangzhou, China
3 shared publications - Jianyuan Lu
Alibaba Cloud, Hangzhou, China
3 shared publications - Shunmin Zhu
Alibaba Cloud, Hangzhou, China
3 shared publications
Latest Publications
- Open Access
- 14 November 2026
- Published by Association for Computing Machinery (ACM)
- p. 1909-1925
Dasheng: LLM-powered Reliability Engineering Assistant for Cloud Overlay Networks
- 14 November 2026
- Published by Association for Computing Machinery (ACM)
- p. 1909-1925
Open AccessDasheng: LLM-powered Reliability Engineering Assistant for Cloud Overlay Networks
- Open Access
- 14 November 2026
- Published by Association for Computing Machinery (ACM)
- p. 1116-1132
Scaling LLM Agent Tool Access: The MCP Gateway
- 14 November 2026
- Published by Association for Computing Machinery (ACM)
- p. 1116-1132
Open AccessScaling LLM Agent Tool Access: The MCP Gateway
- Open Access
- 5 August 2026
- Published by Association for Computing Machinery (ACM)
- p. 304-307
Scaling LLM Agent Tool Access at Cloud Scale
- 5 August 2026
- Published by Association for Computing Machinery (ACM)
- p. 304-307
Open AccessScaling LLM Agent Tool Access at Cloud Scale
- Open Access
- 12 April 2026
- Published by Association for Computing Machinery (ACM)
- p. 81-93
Enabling Black-box RPC-API Testing with Multi-Agent Reinforcement Learning and LLMs: An Industry Case Study
- 12 April 2026
- Published by Association for Computing Machinery (ACM)
- p. 81-93
Open AccessEnabling Black-box RPC-API Testing with Multi-Agent Reinforcement Learning and LLMs: An Industry Case Study