3 citations · 5 across the 9 of their papers we have counts for
12 papers
Do Coding Agents Understand Why Systems Crash---and Why They Don't?
Chenxi Huang, Haonan Wang, Jinghe Zhang +7
Coding agents are increasingly capable of fixing real-world bugs, yet they still struggle with large systems codebases such as the Linux kernel. To understand why, we test how agen…
WebUIProof: Benchmarking WebUI Code Generators with UI-Agent Execution Harness
Yun-Yun Tsai, Yuning Mao, Shiqi Wang +2
Evaluating WebUI code generation at scale is difficult: outputs may compile and look plausible yet fail under user interaction, and prior benchmarks largely rely on free-form promp…
TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization
Alex Mathai, Shobini Iyer, Aleksandr Nogikh +4
Coding agents are increasingly used to accelerate code generation in many downstream tasks, such as fixing bugs, building applications, and prototyping. However, despite their valu…
Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All
Chenxi Huang, Alex Mathai, Feiyang Yu +7
Repairing system crashes discovered by kernel fuzzers like Syzkaller is a critical yet underexplored challenge in software engineering. While recent works have introduced Large Lan…
SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
Jinjun Peng, Magnus Saebo, Tianjun Zhong +6
The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suf…
Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers
Simin Chen, Jinjun Peng, Yixin He +2
Deep learning (DL) compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental v…
Proactive defense against LLM Jailbreak
Weiliang Zhao, Jinjun Peng, Daniel Ben-Levi +2
The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving adversarial attacks, including m…
kAgent: An execution-guided crash resolution agent for the Linux kernel
Alex Mathai, Chenxi Huang, Suwei Ma +7
Fuzzing frameworks like syzkaller have uncovered thousands of Linux kernel crashes, many of which are critical and security-sensitive. However, the ability to rapidly repair these…
EditLord: Learning Code Transformation Rules for Code Editing
Weichen Li, Albert Jan, Baishakhi Ray +3
Code editing is a foundational task in software development, where its effectiveness depends on whether it introduces desired code property changes without changing the original co…
CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation
Jinjun Peng, Leyi Cui, Kele Huang +2
Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrec…
KGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolution
Alex Mathai, Chenxi Huang, Petros Maniatis +4
Large Language Models (LLMs) are consistently improving at increasingly realistic software engineering (SE) tasks. In real-world software stacks, significant SE effort is spent dev…
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
Yangruibo Ding, Jinjun Peng, Marcus J. Min +3
Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to…