2 citations · 4 across the 5 of their papers we have counts for
6 papers
UNBIND: UNlearning By INference-time Directional Steering for Code LLMs
Zhengyang Shan, Jiayun Xin, Yanjun Lin +7
Code large language models acquire programming capabilities from large code corpora, but can also memorize implementations that later require removal. Code unlearning is needed to…
OBLIVION: Workflow-Level Operational Skill Unlearning for Deployed Agents
Zhengyang Shan, Xu Qian, Jiayun Xin +3
Large language model agents are becoming operational interfaces to files, memories, registries, and external tools. This deployment shift creates a new skill revocation problem: af…
SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
Zhengyang Shan, Xu Qian, Jiayun Xin +5
Software vulnerabilities are a primary threat to modern infrastructure. While static analysis and Graph Neural Networks have long served as the foundation for vulnerability detecti…
InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration
Yunkun Wang, Yue Zhang, Guochang Li +5
Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose. While existing LLM-based self-repair approaches conduct inte…
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
Yunkun Wang, Yue Zhang, Zhen Qin +5
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhausti…
Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement
Yingwei Ma, Rongyu Cao, Yongchang Cao +7
Recent advancements in LLM-based agents have led to significant progress in automatic software engineering, particularly in software maintenance and evolution. Despite these encour…