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20242026
most citedUnderstanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

6 citations · 7 across the 2 of their papers we have counts for

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5 papers · 1 filter

cs.SE2026

Dittany: Contribution-Guided Repair Framework for MAS Applications

Hanqi Sun, Yuchen Shao, Hanyi Si +3

LLM-based multi-agent system (MAS) applications can produce useful intermediate results while leaving task requirements unsatisfied. Repairing such executions requires identifying…

cs.SE2026

Understanding Tool-Mediated Interaction Failures in Agentic GUI Software: An Empirical Study

Yuchen Shao, Wenjing Liu, Yuheng Huang +6

Large language model (LLM) agents increasingly complete tasks through live graphical user interfaces (GUIs). Because the model cannot directly operate the GUI, these systems often…

cs.SE2026

CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?

Hang He, Li Wang, Hao Chen +9

Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement analyzer-specific logic, and refine the checker through repea…

cs.SE2024★ 6 cited

Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

Shengming Zhao, Yuchen Shao, Yuheng Huang +4

Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges w…

cs.SE2024★ 1 cited

Are LLMs Correctly Integrated into Software Systems?

Yuchen Shao, Yuheng Huang, Jiawei Shen +3

Large language models (LLMs) provide effective solutions in various application scenarios, with the support of retrieval-augmented generation (RAG). However, developers face challe…