2 citations · 2 across the 6 of their papers we have counts for
7 papers
CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation
Yuchen Liu, Zongzhen Yang, Binhang Qi +2
Model merging has recently attracted significant attention as a promising paradigm for constructing unified multi-task models without requiring additional retraining. However, para…
MASFuzzer: Fuzz Driver Generation and Adaptive Scheduling via Multidimensional API Sequences
Xingyu Liu, Zengqin Huang, Xiang Gao +1
Fuzz testing of software libraries relies on fuzz drivers to invoke library APIs. Traditionally, these drivers are written manually by developers - a process that is time-consuming…
NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
Xiaohan Bi, Binhang Qi, Hailong Sun +3
With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs have become a significant challenge. Model reuse…
Context-Enhanced Vulnerability Detection Based on Large Language Model
Yixin Yang, Bowen Xu, Xiang Gao +1
Vulnerability detection is a critical aspect of software security. Accurate detection is essential to prevent potential security breaches and protect software systems from maliciou…
Agent That Debugs: Dynamic State-Guided Vulnerability Repair
Zhengyao Liu, Yunlong Ma, Jingxuan Xu +4
In recent years, more vulnerabilities have been discovered every day, while manual vulnerability repair requires specialized knowledge and is time-consuming. As a result, many dete…
Type-aware LLM-based Regression Test Generation for Python Programs
Runlin Liu, Zhe Zhang, Yunge Hu +3
Automated regression test generation has been extensively explored, yet generating high-quality tests for Python programs remains particularly challenging. Because of the Python's…
CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging
Zongzhen Yang, Binhang Qi, Hailong Sun +3
Model merging based on task vectors, i.e., the parameter differences between fine-tuned models and a shared base model, provides an efficient way to integrate multiple task-specifi…