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 a significantly higher detection rate than existing tools.

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 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.

The study investigates the limitations of Phonetic Foundation Models (PFMs) for Speech-to-IPA transcription using Grapheme-to-Phoneme (G2P) labels and proposes a new approach based on continuous Articulatory Feature (AF) vectors for better performance.