From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models

Shuo Cai; Yanggan Gu; Zihao Wang; Yuanyi Wang; Yibo Yan; Wenjun Wang; Yuhang Liu; Guanghao Zhu; Sirui Huang; Ming Li; Hongxia Yang

arXiv · 2609.19553

From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models

Shuo Cai·Yanggan Gu·Zihao Wang·Yuanyi Wang·Yibo Yan·Wenjun Wang·Yuhang Liu·Guanghao Zhu·Sirui Huang·Ming Li·Hongxia Yang

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Abstract

Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This growing pool provides a rich base for model reuse and capability integration. Yet existing surveys often cover only separate parts of this space, and they do not provide a unified definition or a systematic taxonomy. This survey defines model fusion and organizes prior work into three levels: parameter-level, representation-level, and behavior-level fusion. We also review related metrics, benchmarks, and applications, summarize current challenges, and identify future directions. Our goal is to provide a clear map of this area and support future work on model fusion. A comprehensive list of papers about model fusion is available at https://github.com/Baicaihaochi/Awesome-Model-Fusion-Survey.

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BibTeX RIS

Shuo Cai, Yanggan Gu, Zihao Wang, Yuanyi Wang, Yibo Yan, Wenjun Wang, Yuhang Liu, Guanghao Zhu, Sirui Huang, Ming Li, Hongxia Yang. 2026-09-17. From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models. https://arxiv.org/abs/2609.19553

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