@inproceedings{cai-etal-2026-parameters,
title = "From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models",
author = "Cai, Shuo and
Gu, Yanggan and
Wang, Zihao and
Wang, Yuanyi and
Yan, Yibo and
Wang, Wenjun and
Liu, Yuhang and
Zhu, Guanghao and
Huang, Sirui and
Ming, Li and
Yang, Hongxia",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.722/",
pages = "15178--15202",
ISBN = "979-8-89176-462-0",
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 \url{https://github.com/Baicaihaochi/Awesome-Model-Fusion-Survey}."
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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.</abstract>
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%0 Conference Proceedings
%T From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
%A Cai, Shuo
%A Gu, Yanggan
%A Wang, Zihao
%A Wang, Yuanyi
%A Yan, Yibo
%A Wang, Wenjun
%A Liu, Yuhang
%A Zhu, Guanghao
%A Huang, Sirui
%A Ming, Li
%A Yang, Hongxia
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cai-etal-2026-parameters
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.722/
%P 15178-15202
Markdown (Informal)
[From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models](https://aclanthology.org/2026.findings-emnlp.722/) (Cai et al., Findings 2026)
ACL
- Shuo Cai, Yanggan Gu, Zihao Wang, Yuanyi Wang, Yibo Yan, Wenjun Wang, Yuhang Liu, Guanghao Zhu, Sirui Huang, Li Ming, and Hongxia Yang. 2026. From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 15178–15202, Budapest, Hungary. Association for Computational Linguistics.