most citedMOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2025

SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection

Lei Yu, Shiqi Cheng, Zhirong Huang +6

With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitatio…

cs.CR2025

Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection

Lei Yu, Zhirong Huang, Hang Yuan +8

Smart contract vulnerability detection remains a major challenge in blockchain security. Existing vulnerability detection methods face two main issues: (1) Existing datasets lack c…

cs.SE2025

Towards Practical Defect-Focused Automated Code Review

Junyi Lu, Lili Jiang, Xiaojia Li +4

The complexity of code reviews has driven efforts to automate review comments, but prior approaches oversimplify this task by treating it as snippet-level code-to-text generation a…

cs.SE20252 cited

MOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models

Hang Yuan, Lei Yu, Zhirong Huang +7

Smart contract vulnerabilities pose significant security risks to blockchain systems, potentially leading to severe financial losses. Existing methods face several limitations: (1)…

cs.SE2025

DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation

Junyi Lu, Xiaojia Li, Zihan Hua +5

Code review is a vital but demanding aspect of software development, generating significant interest in automating review comments. Traditional evaluation methods for these comment…