9 papers
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference
Qiang Zhang, Zhongnian Li
Binary decompilation is a critical reverse engineering task aimed at reconstructing high-level source code from stripped executables. Although Large Language Models (LLMs) have rec…
AscendCraft: Automatic Ascend NPU Kernel Generation via DSL-Guided Transcompilation
Zhongzhen Wen, Shudi Shao, Zhong Li +4
The performance of deep learning models critically depends on efficient kernel implementations, yet developing high-performance kernels for specialized accelerators remains time-co…
Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
Chenxiang Zhang, Tongxi Qu, Zhong Li +3
Deep neural networks are widely deployed with quantization techniques to reduce memory and computational costs by lowering the numerical precision of their parameters. While quanti…
Understanding Chain-of-Thought Effectiveness in Code Generation: An Empirical and Information-Theoretic Analysis
Naizhu Jin, Zhong Li, Guang Yang +2
Large language models (LLMs) achieve strong performance on code generation, but the mechanisms by which Chain-of-Thought (CoT) prompting helps remain unclear. We present a systemat…
Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis
Yu Ge, Linna Xie, Zhong Li +2
Large Language Model Powered Multi-Agent Systems (MASs) are increasingly employed to automate complex real-world problems, such as programming and scientific discovery. Despite the…
GUARD:Dual-Agent based Backdoor Defense on Chain-of-Thought in Neural Code Generation
Naizhu Jin, Zhong Li, Tian Zhang +1
With the widespread application of large language models in code generation, recent studies demonstrate that employing additional Chain-of-Thought generation models can significant…