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Zhongxin Liu

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#artificial intelligence Review Oct 2026

An Empirical Study of Agent Skills'Downstream Utility

Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance. Existing studies characterize Skill content and evaluate downstream performance, yet provide limited explanations of how utility depends on content, execution configuration, and multi-Sk...

Yu Cheng, De-Hai Zhao, Zhong-Xin Liu et al. · 0 citations
Preprint Aug 2026

Effective and Efficient Context Retrieval via Partial Dependency Graph for Repository-Level Code Generation

DyRetriever is an efficient context retrieval method via partial dependency graphs that uses an LLM to first select a set of entry-point functions and then perform multi-hop reasoning along the code dependency graph, eliminating manually designed rules and enabling flexibility across scenarios.

Zhongxin Liu, Zhonghao Jiang, Zhi-Fan Ye et al. · 1 citation
Jul 2026

Evaluating Incompatible Third-party Library API Usage in LLM-based Code Completion

An automated framework that builds a versioned TPL API Knowledge Base and a large-scale benchmark that proposes two lightweight solutions, Real-time Detection and Lightweight Repair, to mitigate ITAUs and provide a foundation for more compatibility-aware code generation in evolving software ecosystems.

Li Lin, Yaorui Fei, Yunfeng Shen et al. · 0 citations

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