activity
20242026
collaborators

6 papers

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2025

REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinement

Anvith Pabba, Simin Chen, Alex Mathai +2

Large Language Models (LLMs) have recently shown strong potential in automatic program repair (APR), especially in repository-level settings where the goal is to generate patches b…

cs.SE2025

SemAgent: A Semantics Aware Program Repair Agent

Anvith Pabba, Alex Mathai, Anindya Chakraborty +1

Large Language Models (LLMs) have shown impressive capabilities in downstream software engineering tasks such as Automated Program Repair (APR). In particular, there has been a lot…

cs.SE2024

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…