7 citations · 48 across the 69 of their papers we have counts for
79 papers
Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference
Sunghyun Wee, Geunjae Choi, Hyeonjin Kim +2
Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost. Post-training quanti…
Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors
Junhoo Lee, Hyeonjin Kim, Sangbum Han +1
Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utility depends on generalizing b…
3DROID: A Renderable 3D Gaussian Dataset with Measured Per-Scene Reliability
Wonguen Cho, Junhoo Lee, Nojun Kwak
Robot manipulation models primarily reason from 2D observations while acting in the 3D physical world. To bridge this gap, recent work has augmented robot data with geometric prior…
Bootstrapping Video Interaction Generation with Synthetic State Transitions
Jiho Jang, Jinyoung Kim, Nojun Kwak +2
While recent video generative models can synthesize high-fidelity videos, they struggle to portray plausible physical interactions and the resulting state transitions, a critical b…
Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations
Jungwon Park, Changin Choi, Jimyeong Kim +2
As large language models are increasingly deployed across diverse downstream tasks, efficient task adaptation has emerged as a central challenge. In response, a wide range of task…
-Bench: A Multilingual Machine Unlearning Benchmark
Kyomin Hwang, Hyeonjin Kim, Hyunho Lee +3
Undesired information such as harmful content and private data propagates through Multilingual Large Language Models (LLMs) via direct training and indirect cross-linguistic spread…
MoARa: Module-Aware Rank Allocation and Structure-Preserving Decomposition for Low-Rank LLM Pre-training
Keunyoung Kim, Nojun Kwak
Low-rank gradient projection reduces the optimizer-state memory cost of large language model (LLM) pretraining, but the steps and wall-clock time needed to reach a target quality r…
Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models
Hyunho Lee, Kyomin Hwang, Hyeonjin Kim +3
With an increasing number of Object Detection (OD) models being deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these models when access to the or…
Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
Kyomin Hwang, Hyeonjin Kim, Hyunho Lee +1
Recent advances in Large Language Models (LLMs) have motivated their adoption across a wide range of domains, including Artificial Intelligence (AI) for mental health. Given the gr…
ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
Suyoung Kim, Sunghyun Wee, Hyeonjin Kim +3
Rotation-based Post-Training Quantization (PTQ) has emerged as a promising solution for mitigating activation outliers in the quantization of Large Language Models (LLMs). Global r…
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
Kyomin Hwang, Hyeonjin Kim, Sangyeon Cho +1
While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII). For LLMs trained on multili…
Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts
Hahyeon Choi, Nojun Kwak
We propose S3 (Specialization, Selection, Sparsification), a framework that rethinks multimodal learning through a structural perspective. Instead of encoding all signals into a fi…
When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models
Jungwon Park, Jimyeong Kim, Jungmin Ko +2
Diffusion language models generate text by iteratively selecting and denoising masked positions, making position selection a central inference-time decision. Most training-free met…
From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models
Yearim Kim, Sangyu Han, Nojun Kwak
Modern vision models achieve remarkable accuracy, but explaining where evidence arises, what the model encodes, and how internal computations assemble that evidence remains fragmen…
Retrieval-Augmented Generation Based Nurse Observation Extraction
Kyomin Hwang, Nojun Kwak
Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into th…
CSF: Black-box Fingerprinting via Compositional Semantics for Text-to-Image Models
Junhoo Lee, Mijin Koo, Nojun Kwak
Text-to-image models are commercially valuable assets often distributed under restrictive licenses, but such licenses are enforceable only when violations can be detected. Existing…
4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video
Jae Won Jang, Yeonjin Chang, Wonsik Shin +2
We introduce 4DGS360, a diffusion-free framework for 360 dynamic object reconstruction from casual monocular video. Existing methods often fail to reconstruct consistent…
Targeted Data Protection for Diffusion Model by Matching Training Trajectory
Hojun Lee, Mijin Koo, Yeji Song +1
Recent advancements in diffusion models have made fine-tuning text-to-image models for personalization increasingly accessible, but have also raised significant concerns regarding…
Multi-dimensional Preference Alignment by Conditioning Reward Itself
Jiho Jang, Jinyoung Kim, Kyungjune Baek +1
Reinforcement Learning from Human Feedback has emerged as a standard for aligning diffusion models. However, we identify a fundamental limitation in the standard DPO formulation be…
LoGoColor: Local-Global 3D Colorization for 360° Scenes
Yeonjin Chang, Juhwan Cho, Seunghyeon Seo +2
Single-channel 3D reconstruction is widely used in fields such as robotics and medical imaging. While these methods are good at reconstructing 3D geometry, their outputs are typica…
Point-to-Point: Sparse Motion Guidance for Controllable Video Editing
Yeji Song, Jaehyun Lee, Mijin Koo +2
Accurately preserving motion while editing a subject remains a core challenge in video editing tasks. Existing methods often face a trade-off between edit and motion fidelity, as t…
Safety-Preserving PTQ via Contrastive Alignment Loss
Sunghyun Wee, Suyoung Kim, Hyeonjin Kim +2
Post-Training Quantization (PTQ) has become the de-facto standard for efficient LLM deployment, yet its optimization objective remains fundamentally incomplete. Standard PTQ method…
Uncovering the Potential Risks in Unlearning: Danger of English-only Unlearning in Multilingual LLMs
Kyomin Hwang, Hyeonjin Kim, Seungyeon Kim +2
There have been a couple of studies showing that attempting to erase multilingual knowledge using only English data is insufficient for multilingual LLMs. However, their analyses r…
Unlocking the Potential of Diffusion Language Models through Template Infilling
Junhoo Lee, Seungyeon Kim, Nojun Kwak
Diffusion Language Models (DLMs) have emerged as a promising alternative to Autoregressive Language Models, yet their inference strategies remain limited to prefix-based prompting…