LLM Agents

I study how LLM-based systems search, expose, retrieve, evaluate, and govern capabilities when they become reusable infrastructure.

Evaluation before dependency

How can agent capabilities and search policies be selected, inspected, and evaluated before they become production dependencies?

Scope

AI search, skill retrieval, NL2SQL benchmarks, LLM serving systems, and capability governance are treated as system surfaces that should be inspected before they are trusted.

AI search

Keeping search policies aligned with changing inventory and evidence conditions.

Skill retrieval

Evaluating ambiguity when multiple skills expose similar capabilities and must be selected reliably.

Capability governance

Studying how reusable agent capabilities are represented, selected, exposed, and evaluated.

NL2SQL and serving

Benchmarking business intelligence services and scaling LLM inference systems.

Papers in this area

LLM Agents

P/D-Serve: Serving Disaggregated Large Language Model at Scale

Yibo Jin, Tao Wang, Huimin Lin, Mingyang Song, Peiyang Li, Yipeng Ma, Yicheng Shan, Zhengfan Yuan, Cailong Li, Yajing Sun, Tiandeng Wu, Xing Chu, Ruizhi Huan, Li Ma, Xiao You, Wenting Zhou, Yunpeng Ye, Wen Liu, Xiangkun Xu, Yongsheng Zhang, Tiantian Dong, Jiawei Zhu, Zhe Wang, Xijian Ju, Jianxun Song, Haoliang Cheng, Xiaojing Li, Jiandong Ding, Hefei Guo, Zhengyong Zhang arXiv 2024