Saved in:
| Main Authors: | Lee, Jaehyuk, Kim, Hanyoung, Kim, Yanggee, Lee, Donghun |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.22372 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition
by: Jung, Jaeheun, et al.
Published: (2024)
by: Jung, Jaeheun, et al.
Published: (2024)
IPPRO: Importance-based Pruning with PRojective Offset for Magnitude-indifferent Structural Pruning
by: Jung, Jaeheun, et al.
Published: (2025)
by: Jung, Jaeheun, et al.
Published: (2025)
Catalyst: a Novel Regularizer for Structured Pruning with Auxiliary Extension of Parameter Space
by: Jung, Jaeheun, et al.
Published: (2025)
by: Jung, Jaeheun, et al.
Published: (2025)
ASAP: Attention-Shift-Aware Pruning for Efficient LVLM Inference
by: Pathak, Surendra, et al.
Published: (2026)
by: Pathak, Surendra, et al.
Published: (2026)
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization
by: Son, Seungwoo, et al.
Published: (2024)
by: Son, Seungwoo, et al.
Published: (2024)
Trust Region Q Adjoint Matching
by: Dong, Yonghoon, et al.
Published: (2026)
by: Dong, Yonghoon, et al.
Published: (2026)
Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs
by: Kim, Kibum, et al.
Published: (2026)
by: Kim, Kibum, et al.
Published: (2026)
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
by: Cha, Taehun, et al.
Published: (2024)
by: Cha, Taehun, et al.
Published: (2024)
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
by: Kim, Juno, et al.
Published: (2023)
by: Kim, Juno, et al.
Published: (2023)
ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate
by: Park, Heewon, et al.
Published: (2025)
by: Park, Heewon, et al.
Published: (2025)
ASAP: Amortized Doubly-Stochastic Attention via Sliced Dual Projection
by: Tran, Huy, et al.
Published: (2026)
by: Tran, Huy, et al.
Published: (2026)
Data-Driven Dimensional Synthesis of Diverse Planar Four-bar Function Generation Mechanisms via Direct Parameterization
by: Kim, Woon Ryong, et al.
Published: (2025)
by: Kim, Woon Ryong, et al.
Published: (2025)
Attention Sinks and Outliers in Attention Residuals
by: Luo, Haozheng, et al.
Published: (2026)
by: Luo, Haozheng, et al.
Published: (2026)
DiaTool-DPO: Multi-Turn Direct Preference Optimization for Tool-Augmented Large Language Models
by: Jung, Sunghee, et al.
Published: (2025)
by: Jung, Sunghee, et al.
Published: (2025)
Sink-Aware Pruning for Diffusion Language Models
by: Myrzakhan, Aidar, et al.
Published: (2026)
by: Myrzakhan, Aidar, et al.
Published: (2026)
Grouped Differential Attention
by: Lim, Junghwan, et al.
Published: (2025)
by: Lim, Junghwan, et al.
Published: (2025)
A Training-free Sub-quadratic Cost Transformer Model Serving Framework With Hierarchically Pruned Attention
by: Lee, Heejun, et al.
Published: (2024)
by: Lee, Heejun, et al.
Published: (2024)
Attention Sinks Induce Gradient Sinks: Massive Activations as Gradient Regulators in Transformers
by: Chen, Yihong, et al.
Published: (2026)
by: Chen, Yihong, et al.
Published: (2026)
The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks
by: Cha, Taehun, et al.
Published: (2026)
by: Cha, Taehun, et al.
Published: (2026)
OPC: One-Point-Contraction Unlearning Toward Deep Feature Forgetting
by: Jung, Jaeheun, et al.
Published: (2025)
by: Jung, Jaeheun, et al.
Published: (2025)
Multi-View Node Pruning for Accurate Graph Representation
by: Kim, Hanjin, et al.
Published: (2025)
by: Kim, Hanjin, et al.
Published: (2025)
DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning
by: Kim, Dohoon, et al.
Published: (2025)
by: Kim, Dohoon, et al.
Published: (2025)
Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
by: Fu, Zizhuo, et al.
Published: (2026)
by: Fu, Zizhuo, et al.
Published: (2026)
Locality-Aware Redundancy Pruning for LLM Depth Compression
by: Yun, Vincent-Daniel, et al.
Published: (2026)
by: Yun, Vincent-Daniel, et al.
Published: (2026)
Are Self-Attentions Effective for Time Series Forecasting?
by: Kim, Dongbin, et al.
Published: (2024)
by: Kim, Dongbin, et al.
Published: (2024)
Training-Free Restoration of Pruned Neural Networks
by: Lee, Keonho, et al.
Published: (2025)
by: Lee, Keonho, et al.
Published: (2025)
EdgeFusion: On-Device Text-to-Image Generation
by: Castells, Thibault, et al.
Published: (2024)
by: Castells, Thibault, et al.
Published: (2024)
Stochastic Parroting in Temporal Attention -- Regulating the Diagonal Sink
by: Hankemeier, Victoria, et al.
Published: (2026)
by: Hankemeier, Victoria, et al.
Published: (2026)
Softpick: No Attention Sink, No Massive Activations with Rectified Softmax
by: Zuhri, Zayd M. K., et al.
Published: (2025)
by: Zuhri, Zayd M. K., et al.
Published: (2025)
Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series
by: Lee, Joohyung, et al.
Published: (2026)
by: Lee, Joohyung, et al.
Published: (2026)
AdaRank: Adaptive Rank Pruning for Enhanced Model Merging
by: Lee, Chanhyuk, et al.
Published: (2025)
by: Lee, Chanhyuk, et al.
Published: (2025)
On the Existence and Behavior of Secondary Attention Sinks
by: Wong, Jeffrey T. H., et al.
Published: (2025)
by: Wong, Jeffrey T. H., et al.
Published: (2025)
SAP: Syntactic Attention Pruning for Transformer-based Language Models
by: Lee, Tzu-Yun, et al.
Published: (2025)
by: Lee, Tzu-Yun, et al.
Published: (2025)
Learning Where It Matters: Geometric Anchoring for Robust Preference Alignment
by: Cho, Youngjae, et al.
Published: (2026)
by: Cho, Youngjae, et al.
Published: (2026)
Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
by: Lee, Sungyoung, et al.
Published: (2026)
by: Lee, Sungyoung, et al.
Published: (2026)
Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation
by: Su, Zunhai, et al.
Published: (2026)
by: Su, Zunhai, et al.
Published: (2026)
SEA: Sparse Linear Attention with Estimated Attention Mask
by: Lee, Heejun, et al.
Published: (2023)
by: Lee, Heejun, et al.
Published: (2023)
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
by: Lee, Gihun, et al.
Published: (2024)
by: Lee, Gihun, et al.
Published: (2024)
Attention-Based Neural-Augmented Kalman Filter for Legged Robot State Estimation
by: Lee, Seokju, et al.
Published: (2026)
by: Lee, Seokju, et al.
Published: (2026)
BoA: Attention-aware Post-training Quantization without Backpropagation
by: Kim, Junhan, et al.
Published: (2024)
by: Kim, Junhan, et al.
Published: (2024)
Similar Items
-
Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition
by: Jung, Jaeheun, et al.
Published: (2024) -
IPPRO: Importance-based Pruning with PRojective Offset for Magnitude-indifferent Structural Pruning
by: Jung, Jaeheun, et al.
Published: (2025) -
Catalyst: a Novel Regularizer for Structured Pruning with Auxiliary Extension of Parameter Space
by: Jung, Jaeheun, et al.
Published: (2025) -
ASAP: Attention-Shift-Aware Pruning for Efficient LVLM Inference
by: Pathak, Surendra, et al.
Published: (2026) -
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization
by: Son, Seungwoo, et al.
Published: (2024)