13 papers
DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs
Bumjun Kim, Dongjae Jeon, Moongyu Jeon +1
Parallel decoding for Diffusion LLMs (dLLMs) is difficult because each denoising step provides only token-wise marginal distributions, while unmasking multiple tokens simultaneousl…
VLMs Trace Without Tracking: Diagnosing Failures in Visual Path Following
Hyesoo Hong, Minsoo Kim, Wonje Jeung +3
Vision-language models (VLMs) achieve strong performance on multimodal benchmarks, but may still lack robust control over basic visual operations. We study \textit{line tracing}, w…
Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs
Yoonjun Cho, Dongjae Jeon, Soeun Kim +2
Quantization Error Reconstruction (QER) reduces accuracy loss in Post-Training Quantization (PTQ) by approximating weights as …
Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs
Sangyeon Yoon, Wonje Jeung, Yoonjun Cho +2
Fine-tuning APIs make frontier LLMs easy to customize, but they can also weaken safety alignment during fine-tuning. While prior work shows that benign supervised fine-tuning (SFT)…
Multi-Level Knowledge Distillation and Dynamic Self-Supervised Learning for Continual Learning
Taeheon Kim, San Kim, Minhyuk Seo +3
Class-incremental with repetition (CIR), where previously trained classes repeatedly introduced in future tasks, is a more realistic scenario than the traditional class incremental…
TTA-DAME: Test-Time Adaptation with Domain Augmentation and Model Ensemble for Dynamic Driving Conditions
Dongjae Jeon, Taeheon Kim, Seongwon Cho +2
Test-time Adaptation (TTA) poses a challenge, requiring models to dynamically adapt and perform optimally on shifting target domains. This task is particularly emphasized in real-w…