29 citations · 101 across the 38 of their papers we have counts for
12 papers · 1 filter
QuSema: Detecting Silent Bugs in Quantum Libraries via Quantum-knowledge-enhanced Agents
Yujin Song, Kaining Zhang, Qixin Zhang +3
Quantum libraries are now critical infrastructure for quantum algorithm development, yet their correctness remains difficult to test. Existing testing techniques mainly rely on fai…
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…
AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
Pingchuan Ma, Zhaoyu Wang, Zimo Ji +5
Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, their autonomy poses significant…
SkillReducer: Optimizing LLM Agent Skills for Token Efficiency
Yudong Gao, Zongjie Li, Yuanyuan Yuan +3
LLM-based coding agents rely on \emph{skills}, pre-packaged instruction sets that extend agent capabilities, yet every token of skill content injected into the context window incur…
Digging Into the Internal: Causality-Based Analysis of LLM Function Calling
Zhenlan Ji, Daoyuan Wu, Wenxuan Wang +3
Function calling (FC) has emerged as a powerful technique for facilitating large language models (LLMs) to interact with external systems and perform structured tasks. However, the…
From Evaluation to Enhancement: Large Language Models for Zero-Knowledge Proof Code Generation
Zhantong Xue, Pingchuan Ma, Zhaoyu Wang +4
Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, verifiable computation, and secure finance. However, authoring ZK progr…
How Multi-Modal LLMs Reshape Visual Deep Learning Testing? A Comprehensive Study Through the Lens of Image Mutation
Liwen Wang, Yuanyuan Yuan, Ao Sun +4
Visual deep learning (VDL) systems have shown significant success in real-world applications like image recognition, object detection, and autonomous driving. To evaluate the relia…
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
Zhenlan Ji, Pingchuan Ma, Zongjie Li +1
While code generation has been widely used in various software development scenarios, the quality of the generated code is not guaranteed. This has been a particular concern in the…
Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Pingchuan Ma, Zhenlan Ji, Peisen Yao +2
Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Caus…
"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process
Pingchuan Ma, Zongjie Li, Ao Sun +1
As the popularity of large language models (LLMs) soars across various applications, ensuring their alignment with human values has become a paramount concern. In particular, given…
On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study
Zongjie Li, Chaozheng Wang, Pingchuan Ma +5
Recent advances in large language models (LLMs) significantly boost their usage in software engineering. However, training a well-performing LLM demands a substantial workforce for…
Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings
Zongjie Li, Pingchuan Ma, Huaijin Wang +4
Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program…