From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
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Computer Science > Computation and Language
Title:From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
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 this https URL.
| Comments: | 25 pages, 4 figures. Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.19553 [cs.CL] |
| (or arXiv:2609.19553v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.19553
arXiv-issued DOI via DataCite (pending registration)
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