@inproceedings{yang-etal-2026-reasonany,
title = "{R}eason{A}ny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging",
author = "Yang, Junyao and
Qian, Chen and
Shen, Wen and
Liu, Yong and
Shao, Jing and
Liu, Dongrui",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2201/",
pages = "47650--47675",
ISBN = "979-8-89176-390-6",
abstract = "Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilities, referred to as ``Reasoning + X'', remains a significant challenge. While model merging offers a promising training-free solution, existing methods often suffer from a destructive performance collapse: existing methods tend to both weaken reasoning depth and compromise domain-specific utility. Interestingly, we identify a counter-intuitive phenomenon underlying this failure: \textit{reasoning ability predominantly resides in parameter regions with low gradient sensitivity, contrary to the common assumption that domain capabilities correspond to high-magnitude parameters}. Motivated by this insight, we propose \textbf{ReasonAny}, a novel merging framework that resolves the {reasoning{--}domain performance collapse} through Contrastive Gradient Identification. Experiments across safety, biomedicine, and finance domains show that ReasonAny effectively synthesizes ``Reasoning + X'' capabilities, significantly outperforming state-of-the-art baselines while retaining robust reasoning performance."
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<abstract>Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilities, referred to as “Reasoning + X”, remains a significant challenge. While model merging offers a promising training-free solution, existing methods often suffer from a destructive performance collapse: existing methods tend to both weaken reasoning depth and compromise domain-specific utility. Interestingly, we identify a counter-intuitive phenomenon underlying this failure: reasoning ability predominantly resides in parameter regions with low gradient sensitivity, contrary to the common assumption that domain capabilities correspond to high-magnitude parameters. Motivated by this insight, we propose ReasonAny, a novel merging framework that resolves the reasoning–domain performance collapse through Contrastive Gradient Identification. Experiments across safety, biomedicine, and finance domains show that ReasonAny effectively synthesizes “Reasoning + X” capabilities, significantly outperforming state-of-the-art baselines while retaining robust reasoning performance.</abstract>
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%0 Conference Proceedings
%T ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging
%A Yang, Junyao
%A Qian, Chen
%A Shen, Wen
%A Liu, Yong
%A Shao, Jing
%A Liu, Dongrui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-etal-2026-reasonany
%X Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilities, referred to as “Reasoning + X”, remains a significant challenge. While model merging offers a promising training-free solution, existing methods often suffer from a destructive performance collapse: existing methods tend to both weaken reasoning depth and compromise domain-specific utility. Interestingly, we identify a counter-intuitive phenomenon underlying this failure: reasoning ability predominantly resides in parameter regions with low gradient sensitivity, contrary to the common assumption that domain capabilities correspond to high-magnitude parameters. Motivated by this insight, we propose ReasonAny, a novel merging framework that resolves the reasoning–domain performance collapse through Contrastive Gradient Identification. Experiments across safety, biomedicine, and finance domains show that ReasonAny effectively synthesizes “Reasoning + X” capabilities, significantly outperforming state-of-the-art baselines while retaining robust reasoning performance.
%U https://aclanthology.org/2026.acl-long.2201/
%P 47650-47675
Markdown (Informal)
[ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging](https://aclanthology.org/2026.acl-long.2201/) (Yang et al., ACL 2026)
ACL