180 citations · 264 across the 13 of their papers we have counts for
14 papers
Does It Render Everywhere? A Study of Cross-Environment Compatibility in MLLM-Generated Webpages
Ziyun Guo, Jingyu Xiao, Yuqiang Sun +1
Multimodal Large Language Models (MLLMs) have been increasingly adopted to automate webpage generation from visual designs (e.g., screenshots). However, existing evaluations are li…
Semantic-Enhanced Automatic Refinement of Architecture Recovery Results Using LLMs
Yiran Zhang, Chengwei Liu, Yuqiang Sun +5
Understanding the architecture is crucial for effectively maintaining and managing large software systems. However, discrepancies often exist between the designed and implemented a…
Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts
Xianhao Zhang, Jing Sun, Zijian Zhang +4
Smart contract vulnerabilities pose severe financial risks, yet existing security tools largely stop at vulnerability detection, offering limited support for explaining whether rep…
Knowledge Over Parameters: Evolving Smart Contract Vulnerability Detection
Yuqiang Sun, Han Liu, Ying Li +4
Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existi…
From Docs to Descriptions: Smell-Aware Evaluation of MCP Server Descriptions
Peiran Wang, Ying Li, Yuqiang Sun +3
The Model Context Protocol (MCP) has rapidly become a de facto standard for connecting LLM-based agents with external tools via reusable MCP servers. In practice, however, server s…
LogicScan: An LLM-driven Framework for Detecting Business Logic Vulnerabilities in Smart Contracts
Jiaqi Gao, Zijian Zhang, Yuqiang Sun +5
Business logic vulnerabilities have become one of the most damaging yet least understood classes of smart contract vulnerabilities. Unlike traditional bugs such as reentrancy or ar…
Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap
Feiyang Huang, Yuqiang Sun, Fan Zhang +3
Large Language Models (LLMs) have shown promising performance in software vulnerability detection, particularly after domain-specific Supervised Fine-Tuning (SFT). However, it rema…
Casting a SPELL: Sentence Pairing Exploration for LLM Limitation-breaking
Yifan Huang, Xiaojun Jia, Wenbo Guo +4
Large language models (LLMs) have revolutionized software development through AI-assisted coding tools, enabling developers with limited programming expertise to create sophisticat…
A Vision for Auto Research with LLM Agents
Chengwei Liu, Chong Wang, Jiayue Cao +16
This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Levera…
PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation
Ye Liu, Yue Xue, Daoyuan Wu +4
With recent advances in large language models (LLMs), this paper explores the potential of leveraging state-of-the-art LLMs,such as GPT-4, to transfer existing human-written proper…
Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications
Wei Ma, Daoyuan Wu, Yuqiang Sun +5
Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart co…
LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
Yuqiang Sun, Daoyuan Wu, Yue Xue +5
Large language models (LLMs) have demonstrated significant potential in various tasks, including those requiring human-level intelligence, such as vulnerability detection. However,…
Who is the Real Hero? Measuring Developer Contribution via Multi-dimensional Data Integration
Yuqiang Sun, Zhengzi Xu, Chengwei Liu +2
Proper incentives are important for motivating developers in open-source communities, which is crucial for maintaining the development of open-source software healthy. To provide s…
GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis
Yuqiang Sun, Daoyuan Wu, Yue Xue +5
Smart contracts are prone to various vulnerabilities, leading to substantial financial losses over time. Current analysis tools mainly target vulnerabilities with fixed control or…