@inproceedings{lee-etal-2026-prime,
title = "{PRIME}: Ultra-Low-Rank Principal{--}Residual Model Merging",
author = "Lee, Seung-Ho and
Lee, Kyungsu and
Zuchi, Bazarvaani and
Ahn, Jeongmin and
Seo, Insuk and
Jeon, Donghyeon and
Kang, Inho and
Na, Seung-Hoon",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.168/",
pages = "3415--3436",
ISBN = "979-8-89176-395-1",
abstract = "Model merging has emerged as an effective approach for integrating multiple task-specific fine-tuned models into a single unified model without requiring additional data-intensive training. A central challenge in model merging is to reduce task interference while preserving the task-specific capabilities of the original models. In this work, we propose PRIME, an ultra-low-rank principal-residual model merging framework that decomposes task vector merging into two complementary stages. First, ultra-low-rank principal task vector merging retains only a small fraction of singular vectors, effectively reducing task interference while preserving most of the task-specific performance. Second, orthogonal residual task vector merging incorporates the remaining components by projecting them onto the null space of the principal subspace, thereby avoiding interference while recovering additional task-relevant information. Extensive experiments on eight natural language processing tasks demonstrate that PRIME consistently outperforms existing model merging methods, achieving improvements of up to 1.18{\%} on T5 and 1.9{\%} on LLaMA-3.2-3B."
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<abstract>Model merging has emerged as an effective approach for integrating multiple task-specific fine-tuned models into a single unified model without requiring additional data-intensive training. A central challenge in model merging is to reduce task interference while preserving the task-specific capabilities of the original models. In this work, we propose PRIME, an ultra-low-rank principal-residual model merging framework that decomposes task vector merging into two complementary stages. First, ultra-low-rank principal task vector merging retains only a small fraction of singular vectors, effectively reducing task interference while preserving most of the task-specific performance. Second, orthogonal residual task vector merging incorporates the remaining components by projecting them onto the null space of the principal subspace, thereby avoiding interference while recovering additional task-relevant information. Extensive experiments on eight natural language processing tasks demonstrate that PRIME consistently outperforms existing model merging methods, achieving improvements of up to 1.18% on T5 and 1.9% on LLaMA-3.2-3B.</abstract>
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%0 Conference Proceedings
%T PRIME: Ultra-Low-Rank Principal–Residual Model Merging
%A Lee, Seung-Ho
%A Lee, Kyungsu
%A Zuchi, Bazarvaani
%A Ahn, Jeongmin
%A Seo, Insuk
%A Jeon, Donghyeon
%A Kang, Inho
%A Na, Seung-Hoon
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lee-etal-2026-prime
%X Model merging has emerged as an effective approach for integrating multiple task-specific fine-tuned models into a single unified model without requiring additional data-intensive training. A central challenge in model merging is to reduce task interference while preserving the task-specific capabilities of the original models. In this work, we propose PRIME, an ultra-low-rank principal-residual model merging framework that decomposes task vector merging into two complementary stages. First, ultra-low-rank principal task vector merging retains only a small fraction of singular vectors, effectively reducing task interference while preserving most of the task-specific performance. Second, orthogonal residual task vector merging incorporates the remaining components by projecting them onto the null space of the principal subspace, thereby avoiding interference while recovering additional task-relevant information. Extensive experiments on eight natural language processing tasks demonstrate that PRIME consistently outperforms existing model merging methods, achieving improvements of up to 1.18% on T5 and 1.9% on LLaMA-3.2-3B.
%U https://aclanthology.org/2026.findings-acl.168/
%P 3415-3436
Markdown (Informal)
[PRIME: Ultra-Low-Rank Principal–Residual Model Merging](https://aclanthology.org/2026.findings-acl.168/) (Lee et al., Findings 2026)
ACL
- Seung-Ho Lee, Kyungsu Lee, Bazarvaani Zuchi, Jeongmin Ahn, Insuk Seo, Donghyeon Jeon, Inho Kang, and Seung-Hoon Na. 2026. PRIME: Ultra-Low-Rank Principal–Residual Model Merging. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3415–3436, San Diego, California, United States. Association for Computational Linguistics.