20 citations · 37 across the 13 of their papers we have counts for
13 papers
SCOPE: Leveraging Subgoal Critiques for Code Generation
Yueke Zhang, Yifan Zhang, Zihan Fang +3
Code generation with large language models (LLMs) remains unreliable because generated programs can appear correct while still violating key semantic requirements in the natural la…
VERITAS: Verifier-Guided Proof Search for Zero-Shot Formal Theorem Proving
Manish Acharya, Zhenyu Liao, Yueke Zhang +3
LLM-based formal provers often collapse rich verifier signals (syntax errors, type mismatches, partial goal progress) into a binary pass/fail bit. We present VERITAS, a zero-shot f…
Constraint-Guided Multi-Agent Decompilation for Executable Binary Recovery
Yifan Zhang, Xiaohan Wang, Yueke Zhang +2
Decompilation -- recovering source code from compiled binaries -- is essential for security analysis, malware reverse engineering, and legacy software maintenance. However, existin…
DPO-F+: Aligning Code Repair Feedback with Developers' Preferences
Zihan Fang, Yifan Zhang, Yueke Zhang +2
Large Language Models (LLMs) are increasingly used in software engineering tasks, especially code repair. However, developers often struggle to interpret model outputs, limiting ef…
CodeGrad: Integrating Multi-Step Verification with Gradient-Based LLM Refinement
Yueke Zhang, Yifan Zhang, Kevin Leach +1
While Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, they often produce solutions that lack guarantees of correctness, robustness, and e…
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
Yifan Zhang, Chen Huang, Yueke Zhang +5
Code Language Models (CodeLLMs) learn token importance from data correlations, whereas human developers attend selectively to semantically salient code. We present EyeMulator, a mo…
Enhancing Code LLM Training with Programmer Attention
Yifan Zhang, Chen Huang, Zachary Karas +3
Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost o…
Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation
Manish Acharya, Yifan Zhang, Kevin Leach +1
Optimizing software performance through automated code refinement offers a promising avenue for enhancing execution speed and efficiency. Despite recent advancements in LLMs, a sig…
MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning
Jiliang Li, Yifan Zhang, Yu Huang +1
Recent growth and proliferation of malware have tested practitioners ability to promptly classify new samples according to malware families. In contrast to labor-intensive reverse…
LineBreaker: Finding Token-Inconsistency Bugs with Large Language Models
Hongbo Chen, Yifan Zhang, Xing Han +7
Token-inconsistency bugs (TIBs) involve the misuse of syntactically valid yet incorrect code tokens, such as misused variables and erroneous function invocations, which can often l…
EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Yifan Zhang, Jiliang Li, Zachary Karas +5
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led…
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
Jiliang Li, Yifan Zhang, Zachary Karas +3
Recent language models have demonstrated proficiency in summarizing source code. However, as in many other domains of machine learning, language models of code lack sufficient expl…
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
Chen Huang, Haoyang Li, Yifan Zhang +2
The vanilla Graph Convolutional Network (GCN) uses a low-pass filter to extract low-frequency signals from graph topology, which may lead to the over-smoothing problem when GCN goe…