Papers
1–3 of 3Research Paper·Jan 14, 2026
SimMerge: Learning to Select Merge Operators from Similarity Signals
Model merging enables multiple large language models (LLMs) to be combined into a single model while preserving performance. This makes it a valuable tool in LLM development, offering a competitive al...
6.0 viability
Research Paper·Feb 12, 2026
Beyond Parameter Arithmetic: Sparse Complementary Fusion for Distribution-Aware Model Merging
Model merging has emerged as a promising paradigm for composing the capabilities of large language models by directly operating in weight space, enabling the integration of specialized models without ...
6.0 viability
Research Paper·Feb 9, 2026
Sparsity-Aware Evolution for Model Merging
We propose a sparsity-aware evolutionary (SAE) framework for model merging that involves iterative pruning-merging cycles to act as a novel mutation operator. We incorporate the sparsity constraints i...
5.0 viability