Built with and by Teycir Ben Soltane•
How to Use•FAQ•GitHub•arXiv.org•
Share:
ArXivCSExplorer
☆☆Bookmarks🏆RSSHow to UseFAQ
Home/Authors/Young Lee

Young Lee

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

Crypto×2Vision×1AI×1ML×1Networking×1Prog. Lang.×1Software Eng.×1

Frequent co-authors

Jungin Park1×
Jiyoung Lee1×
Kwanghoon Sohn1×
Chaeyoung Lee1×
Chaeri Jung1×
Seonghoon Jeong1×

Research Timeline

2026
Symbolic Execution Meets Multi-LLM Orchestration: Detecting Memory Vulnerabilities in Incomplete Rust CVE Snippets

The paper introduces a novel multi-LLM orchestration system combined with symbolic execution to successfully detect memory vulnerabilities in uncompilable, incomplete Rust CVE code snippets, achieving a significantly higher detection rate than existing tools.

DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection

The paper proposes DRIFT, a drift-resilient Transformer framework that maintains high accuracy in detecting evolving Domain Generation Algorithms (DGAs) by learning invariant representations.

V-LynX: Token Interface Alignment for Video+X LLMs

V-LynX is a framework that enhances Video LLMs by integrating new modalities into their existing token interface, achieving state-of-the-art performance across diverse video understanding tasks.

Highlighted terms show continued research focus across papers

Papers

cs.CVcs.AIRecentMay 30, 2026

V-LynX: Token Interface Alignment for Video+X LLMs

Jungin Park, Jiyoung Lee, Kwanghoon Sohn

V-LynX is a framework that enhances Video LLMs by integrating new modalities into their existing token interface, achieving state-of-the-art performance across diverse video understanding tasks.

View →
cs.CRcs.LGcs.NIRecentMay 11, 2026

DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection

Chaeyoung Lee, Chaeri Jung, Seonghoon Jeong

The paper proposes DRIFT, a drift-resilient Transformer framework that maintains high accuracy in detecting evolving Domain Generation Algorithms (DGAs) by learning invariant representations.

View →
cs.CRcs.PLcs.SERecentApr 28, 2026

Symbolic Execution Meets Multi-LLM Orchestration: Detecting Memory Vulnerabilities in Incomplete Rust CVE Snippets

Zeyad Abdelrazek, Young Lee

The paper introduces a novel multi-LLM orchestration system combined with symbolic execution to successfully detect memory vulnerabilities in uncompilable, incomplete Rust CVE code snippets, achieving…

View →