3 papers
cs.CR2026
Fully Automated End-to-End Adversary Emulation from MITRE ATT\&CK Based Cyber Threat Intelligence Using LLMs
Jueon Choi, Seojun Lee, Sanggwon Yun +2
This paper presents a fully automated end-to-end framework for adversary emulation from MITRE ATT&CK-aligned CTI reports using LLMs. Unlike prior work, which either executes prewri…
cs.CV2026
NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification
Youngung Han, Minkyung Cha, Kyeonghun Kim +12
Minimizing invasive diagnostic procedures to reduce the risk of patient injury and infection is a central goal in medical imaging. And yet, noninvasive diagnosis of perineural inva…
cs.CL2025
QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
Hyesung Jeon, Seojune Lee, Beomseok Kang +2
The demand for efficient deployment of large language models (LLMs) has driven interest in quantization, which reduces inference cost, and parameter-efficient fine-tuning (PEFT), w…