{"total":65,"rows":[{"id":"2408.07666","title":"Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities","created":"2024-08-14","cited_by_count":254,"area":null},{"id":"2312.12148","title":"Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment","created":"2023-12-19","cited_by_count":230,"area":null},{"id":"2410.19735","title":"Model merging with SVD to tie the Knots","created":"2024-10-25","cited_by_count":122,"area":null},{"id":"2406.15479","title":"Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging","created":"2024-06-17","cited_by_count":116,"area":null},{"id":"2402.02705","title":"Representation Surgery for Multi-Task Model Merging","created":"2024-02-05","cited_by_count":96,"area":null},{"id":"2407.11046","title":"A Survey on LoRA of Large Language Models","created":"2024-07-08","cited_by_count":67,"area":null},{"id":"2410.19878","title":"Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies","created":"2024-10-24","cited_by_count":47,"area":null},{"id":"2501.01230","title":"Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent","created":"2025-01-02","cited_by_count":39,"area":null},{"id":"2312.06173","title":"Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion","created":"2023-12-11","cited_by_count":37,"area":null},{"id":"2510.08396","title":"FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts","created":"2025-10-09","cited_by_count":35,"area":"moe"},{"id":"2509.17786","title":"Accurate and Efficient Low-Rank Model Merging in Core Space","created":"2025-09-22","cited_by_count":29,"area":null},{"id":"2502.17159","title":"RobustMerge: Parameter-Efficient Model Merging for MLLMs with Direction Robustness","created":"2025-02-24","cited_by_count":26,"area":null},{"id":"2410.21804","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging","created":"2024-10-29","cited_by_count":23,"area":"moe"},{"id":"2411.18729","title":"Multi-Task Model Merging via Adaptive Weight Disentanglement","created":"2024-11-27","cited_by_count":22,"area":null},{"id":"2405.13952","title":"Spectral Adapter: Fine-Tuning in Spectral Space","created":"2024-05-22","cited_by_count":21,"area":null},{"id":"2403.08994","title":"Ethos: Rectifying Language Models in Orthogonal Parameter Space","created":"2024-03-13","cited_by_count":20,"area":null},{"id":"2504.02620","title":"Efficient Model Editing with Task-Localized Sparse Fine-tuning","created":"2025-04-03","cited_by_count":16,"area":null},{"id":"2504.14662","title":"Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning","created":"2025-04-20","cited_by_count":16,"area":null},{"id":"2407.07089","title":"Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic","created":"2024-07-09","cited_by_count":14,"area":null},{"id":"2505.11883","title":"MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging","created":"2025-05-17","cited_by_count":14,"area":null},{"id":"2410.14389","title":"SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery","created":"2024-10-18","cited_by_count":12,"area":null},{"id":"2504.18992","title":"Dynamic Fisher-weighted Model Merging via Bayesian Optimization","created":"2025-04-26","cited_by_count":12,"area":null},{"id":"2512.23017","title":"Merge before Forget: A Single LoRA Continual Learning via Continual Merging","created":"2025-12-28","cited_by_count":11,"area":null},{"id":"2502.12706","title":"Scalable Model Merging with Progressive Layer-wise Distillation","created":"2025-02-18","cited_by_count":10,"area":null},{"id":"2408.10174","title":"SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models","created":"2024-08-19","cited_by_count":9,"area":null},{"id":"2505.14136","title":"Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging","created":"2025-05-20","cited_by_count":9,"area":"moe"},{"id":"2504.03454","title":"SpectR: Dynamically Composing LM Experts with Spectral Routing","created":"2025-04-04","cited_by_count":6,"area":null},{"id":"2509.01548","title":"Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing","created":"2025-09-01","cited_by_count":6,"area":null},{"id":"2504.05342","title":"MASS: MoErging through Adaptive Subspace Selection","created":"2025-04-06","cited_by_count":5,"area":null},{"id":"2507.04790","title":"Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning","created":"2025-07-07","cited_by_count":5,"area":null},{"id":"2511.21437","title":"A Systematic Study of In-the-Wild Model Merging for Large Language Models","created":"2025-11-26","cited_by_count":5,"area":null},{"id":"2503.19201","title":"A Shared Low-Rank Adaptation Approach to Personalized RLHF","created":"2025-03-24","cited_by_count":4,"area":"alignment"},{"id":"2504.10902","title":"Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMs","created":"2025-04-15","cited_by_count":4,"area":null},{"id":"2506.14126","title":"From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging","created":"2025-06-17","cited_by_count":4,"area":null},{"id":"2507.16083","title":"Efficient Compositional Multi-tasking for On-device Large Language Models","created":"2025-07-21","cited_by_count":4,"area":null},{"id":"2603.26317","title":"Label-Free Cross-Task LoRA Merging with Null-Space Compression","created":"2026-03-27","cited_by_count":4,"area":null},{"id":"2607.16821","title":"First-Order Predictable but Pairwise Fragile: Local Task Adaptation in Trained Transformers","created":"2026-07-18","cited_by_count":4,"area":"mechinterp"},{"id":"2506.23104","title":"DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation","created":"2025-06-29","cited_by_count":3,"area":null},{"id":"2602.17385","title":"Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature","created":"2026-02-19","cited_by_count":3,"area":null},{"id":"2604.17078","title":"Understanding and Enforcing Weight Disentanglement in Task Arithmetic","created":"2026-04-18","cited_by_count":3,"area":null},{"id":"2505.23209","title":"Navigating the Accuracy-Size Trade-Off with Flexible Model Merging","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2510.17895","title":"Hierarchical Federated Unlearning for Large Language Models","created":"2025-10-19","cited_by_count":2,"area":"safety"},{"id":"2511.05171","title":"Model Merging Improves Zero-Shot Generalization in Bioacoustic Foundation Models","created":"2025-11-07","cited_by_count":2,"area":null},{"id":"2512.19320","title":"MAGIC: Achieving Superior Model Merging via Magnitude Calibration","created":"2025-12-22","cited_by_count":2,"area":null},{"id":"2603.26299","title":"Preference-Aligned LoRA Merging: Preserving Subspace Coverage and Addressing Directional Anisotropy","created":"2026-03-27","cited_by_count":2,"area":null},{"id":"2412.19098","title":"SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation","created":"2024-12-26","cited_by_count":1,"area":null},{"id":"2502.01015","title":"Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic","created":"2025-02-03","cited_by_count":1,"area":null},{"id":"2506.01194","title":"FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA","created":"2025-06-01","cited_by_count":1,"area":null},{"id":"2509.18093","title":"SEQR: Secure and Efficient QR-based LoRA Routing","created":"2025-09-22","cited_by_count":1,"area":null},{"id":"2510.13921","title":"Weight Weaving: Parameter Pooling for Data-Free Model Merging","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2603.13467","title":"Learning Not To Interfere: Adapting Experts For Model Merging","created":"2026-03-13","cited_by_count":1,"area":null},{"id":"2607.15752","title":"Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging","created":"2026-07-17","cited_by_count":1,"area":null},{"id":"2410.12927","title":"Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2411.18220","title":"R-MTLLMF: Resilient Multi-Task Large Language Model Fusion at the Wireless Edge","created":"2024-11-27","cited_by_count":0,"area":null},{"id":"2506.02478","title":"FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2510.17890","title":"MIN-Merging: Merge the Important Neurons for Model Merging","created":"2025-10-18","cited_by_count":0,"area":null},{"id":"2511.09880","title":"EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models","created":"2025-11-13","cited_by_count":0,"area":"alignment"},{"id":"2512.23210","title":"Task-oriented Learnable Diffusion Timesteps for Universal Few-shot Learning of Dense Tasks","created":"2025-12-29","cited_by_count":0,"area":"diffusion"},{"id":"2601.21690","title":"A Unified Generalization Framework for Model Merging: Trade-offs, Non-Linearity, and Scaling Laws","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2604.17753","title":"Evolutionary Negative Module Pruning for Better LoRA Merging","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2607.03522","title":"Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation","created":"2026-07-03","cited_by_count":0,"area":null},{"id":"2607.16062","title":"When Model Merging Rivals Joint Multi-Task Reinforcement Learning: A Task-Vector Geometry Analysis","created":"2026-07-17","cited_by_count":0,"area":"deeprl"},{"id":"2610.00431","title":"ChainLoRA: Geometry-Preserving Task Vector Merging for Continual Learning in LLMs","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00928","title":"Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.14558","title":"PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}