From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging
Fuente:
arXiv
Saved in:
| Main Authors: | Wu, Jialin, Yang, Jian, Wang, Handing, Wen, Jiajun, Yu, Zhiyong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Transformers
by: Zhang, Boxiang, et al.
Published: (2026)
by: Zhang, Boxiang, et al.
Published: (2026)
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
by: Xing, Jiarui, et al.
Published: (2026)
by: Xing, Jiarui, et al.
Published: (2026)
Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective
by: Wang, Zhaoxin, et al.
Published: (2024)
by: Wang, Zhaoxin, et al.
Published: (2024)
Representation Surgery for Multi-Task Model Merging
by: Yang, Enneng, et al.
Published: (2024)
by: Yang, Enneng, et al.
Published: (2024)
Weight Weaving: Parameter Pooling for Data-Free Model Merging
by: Chaves, Levy, et al.
Published: (2025)
by: Chaves, Levy, et al.
Published: (2025)
AdaMerging: Adaptive Model Merging for Multi-Task Learning
by: Yang, Enneng, et al.
Published: (2023)
by: Yang, Enneng, et al.
Published: (2023)
Parameter Competition Balancing for Model Merging
by: Du, Guodong, et al.
Published: (2024)
by: Du, Guodong, et al.
Published: (2024)
Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning
by: Lee, Yeoreum, et al.
Published: (2025)
by: Lee, Yeoreum, et al.
Published: (2025)
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging
by: Guo, Kuangpu, et al.
Published: (2025)
by: Guo, Kuangpu, et al.
Published: (2025)
A CLIP-based Uncertainty Modal Modeling (UMM) Framework for Pedestrian Re-Identification in Autonomous Driving
by: Li, Jialin, et al.
Published: (2025)
by: Li, Jialin, et al.
Published: (2025)
DC-Merge: Improving Model Merging with Directional Consistency
by: Zhang, Han-Chen, et al.
Published: (2026)
by: Zhang, Han-Chen, et al.
Published: (2026)
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
by: Yang, Huitong, et al.
Published: (2025)
by: Yang, Huitong, et al.
Published: (2025)
EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving
by: Palladino, Vittorio, et al.
Published: (2026)
by: Palladino, Vittorio, et al.
Published: (2026)
Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models
by: Yang, Juncheng, et al.
Published: (2024)
by: Yang, Juncheng, et al.
Published: (2024)
EMR-Merging: Tuning-Free High-Performance Model Merging
by: Huang, Chenyu, et al.
Published: (2024)
by: Huang, Chenyu, et al.
Published: (2024)
Efficient Visual Transformer by Learnable Token Merging
by: Wang, Yancheng, et al.
Published: (2024)
by: Wang, Yancheng, et al.
Published: (2024)
Understanding Multimodal Hallucination with Parameter-Free Representation Alignment
by: Wang, Yueqian, et al.
Published: (2024)
by: Wang, Yueqian, et al.
Published: (2024)
Saliency-Aware Model Merging
by: Park, Jungin, et al.
Published: (2026)
by: Park, Jungin, et al.
Published: (2026)
Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations
by: Jiang, Yangbangyan, et al.
Published: (2025)
by: Jiang, Yangbangyan, et al.
Published: (2025)
Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
by: Hsu, Kyle, et al.
Published: (2024)
by: Hsu, Kyle, et al.
Published: (2024)
Localizing Task Information for Improved Model Merging and Compression
by: Wang, Ke, et al.
Published: (2024)
by: Wang, Ke, et al.
Published: (2024)
SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery
by: Yang, Enneng, et al.
Published: (2024)
by: Yang, Enneng, et al.
Published: (2024)
Revitalizing the Beginning: Avoiding Storage Dependency for Model Merging in Continual Learning
by: Wang, Xi, et al.
Published: (2026)
by: Wang, Xi, et al.
Published: (2026)
RegCL: Continual Adaptation of Segment Anything Model via Model Merging
by: Shu, Yuan-Chen, et al.
Published: (2025)
by: Shu, Yuan-Chen, et al.
Published: (2025)
Rapid Plug-in Defenders
by: Wu, Kai, et al.
Published: (2023)
by: Wu, Kai, et al.
Published: (2023)
Frequency Switching Mechanism for Parameter-E!cient Multi-Task Learning
by: Liu, Shih-Wen, et al.
Published: (2026)
by: Liu, Shih-Wen, et al.
Published: (2026)
Closed-Loop Unsupervised Representation Disentanglement with $β$-VAE Distillation and Diffusion Probabilistic Feedback
by: Jin, Xin, et al.
Published: (2024)
by: Jin, Xin, et al.
Published: (2024)
Efficient Multi-Source Knowledge Transfer by Model Merging
by: Osial, Marcin, et al.
Published: (2025)
by: Osial, Marcin, et al.
Published: (2025)
CubistMerge: Spatial-Preserving Token Merging For Diverse ViT Backbones
by: Gong, Wenyi, et al.
Published: (2025)
by: Gong, Wenyi, et al.
Published: (2025)
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging
by: Shen, Li, et al.
Published: (2024)
by: Shen, Li, et al.
Published: (2024)
Bidirectional Consistency Models
by: Li, Liangchen, et al.
Published: (2024)
by: Li, Liangchen, et al.
Published: (2024)
LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
by: Kim, Jinuk, et al.
Published: (2024)
by: Kim, Jinuk, et al.
Published: (2024)
T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysis
by: Imam, Raza, et al.
Published: (2025)
by: Imam, Raza, et al.
Published: (2025)
Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
by: Peng, Bincheng, et al.
Published: (2026)
by: Peng, Bincheng, et al.
Published: (2026)
ZipIt! Merging Models from Different Tasks without Training
by: Stoica, George, et al.
Published: (2023)
by: Stoica, George, et al.
Published: (2023)
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding
by: Wang, Haoxiang, et al.
Published: (2023)
by: Wang, Haoxiang, et al.
Published: (2023)
ATM: Improving Model Merging by Alternating Tuning and Merging
by: Zhou, Luca, et al.
Published: (2024)
by: Zhou, Luca, et al.
Published: (2024)
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization
by: Choi, Hongjun, et al.
Published: (2024)
by: Choi, Hongjun, et al.
Published: (2024)
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models
by: Zhang, Xinxi, et al.
Published: (2024)
by: Zhang, Xinxi, et al.
Published: (2024)
LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
by: Wang, Ke, et al.
Published: (2024)
by: Wang, Ke, et al.
Published: (2024)
Similar Items
-
CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Transformers
by: Zhang, Boxiang, et al.
Published: (2026) -
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
by: Xing, Jiarui, et al.
Published: (2026) -
Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective
by: Wang, Zhaoxin, et al.
Published: (2024) -
Representation Surgery for Multi-Task Model Merging
by: Yang, Enneng, et al.
Published: (2024) -
Weight Weaving: Parameter Pooling for Data-Free Model Merging
by: Chaves, Levy, et al.
Published: (2025)