15 citations · 16 across the 8 of their papers we have counts for
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cs.SE2026
KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models
Fuyuan Xia, Qixin Zhang, Chenhao Ying +5
As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical. To this end, many LLM-based fuzzing appro…
cs.SE2025★ 1 cited
A Preference-Driven Methodology for High-Quality Solidity Code Generation
Zhiyuan Peng, Xin Yin, Chenhao Ying +2
While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing ga…
cs.SE2025
SolEval: Benchmarking Large Language Models for Repository-level Solidity Code Generation
Zhiyuan Peng, Xin Yin, Rui Qian +5
Large language models (LLMs) have transformed code generation. However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity lang…