@inproceedings{lv-etal-2026-costeer,
title = "{C}o{S}teer: Collaborative Decoding-Time Personalization via Local Delta Steering",
author = "Lv, Hang and
Liang, Sheng and
Wang, Hao and
Gu, Hongchao and
Wu, Yaxiong and
Guo, Wei and
Lian, Defu and
Liu, Yong and
Chen, Enhong",
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.1834/",
pages = "39156--39181",
ISBN = "979-8-89176-462-0",
abstract = "Personalization has become crucial for adapting models to the diverse and evolving needs of users across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment within a single model, they struggle to achieve both real-time and high-quality personalization under the resource and privacy constraints of personal devices. To address this challenge, we propose CoSteer, a collaborative framework that enables tuning-free, real-time personalization via decoding-time adaptation. By leveraging logit differences between context-aware and -agnostic local small models, CoSteer steers cloud-based large models, ensuring effective personalization while preserving the large model{'}s capabilities. Personalization is handled locally, with only final tokens sent to the cloud, maintaining both user context and system efficiency. Through extensive experiments across a wide range of tasks, we demonstrate that CoSteer generates high-quality personalized content, ensuring both effectiveness and computational efficiency. Our results highlight its robustness across models and environments, confirming its practical applicability in real-world scenarios. Our code and data are available at https://anonymous.4open.science/r/Costeer-4977."
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<abstract>Personalization has become crucial for adapting models to the diverse and evolving needs of users across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment within a single model, they struggle to achieve both real-time and high-quality personalization under the resource and privacy constraints of personal devices. To address this challenge, we propose CoSteer, a collaborative framework that enables tuning-free, real-time personalization via decoding-time adaptation. By leveraging logit differences between context-aware and -agnostic local small models, CoSteer steers cloud-based large models, ensuring effective personalization while preserving the large model’s capabilities. Personalization is handled locally, with only final tokens sent to the cloud, maintaining both user context and system efficiency. Through extensive experiments across a wide range of tasks, we demonstrate that CoSteer generates high-quality personalized content, ensuring both effectiveness and computational efficiency. Our results highlight its robustness across models and environments, confirming its practical applicability in real-world scenarios. Our code and data are available at https://anonymous.4open.science/r/Costeer-4977.</abstract>
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%0 Conference Proceedings
%T CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
%A Lv, Hang
%A Liang, Sheng
%A Wang, Hao
%A Gu, Hongchao
%A Wu, Yaxiong
%A Guo, Wei
%A Lian, Defu
%A Liu, Yong
%A Chen, Enhong
%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 lv-etal-2026-costeer
%X Personalization has become crucial for adapting models to the diverse and evolving needs of users across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment within a single model, they struggle to achieve both real-time and high-quality personalization under the resource and privacy constraints of personal devices. To address this challenge, we propose CoSteer, a collaborative framework that enables tuning-free, real-time personalization via decoding-time adaptation. By leveraging logit differences between context-aware and -agnostic local small models, CoSteer steers cloud-based large models, ensuring effective personalization while preserving the large model’s capabilities. Personalization is handled locally, with only final tokens sent to the cloud, maintaining both user context and system efficiency. Through extensive experiments across a wide range of tasks, we demonstrate that CoSteer generates high-quality personalized content, ensuring both effectiveness and computational efficiency. Our results highlight its robustness across models and environments, confirming its practical applicability in real-world scenarios. Our code and data are available at https://anonymous.4open.science/r/Costeer-4977.
%U https://aclanthology.org/2026.findings-emnlp.1834/
%P 39156-39181
Markdown (Informal)
[CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering](https://aclanthology.org/2026.findings-emnlp.1834/) (Lv et al., Findings 2026)
ACL
- Hang Lv, Sheng Liang, Hao Wang, Hongchao Gu, Yaxiong Wu, Wei Guo, Defu Lian, Yong Liu, and Enhong Chen. 2026. CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 39156–39181, Budapest, Hungary. Association for Computational Linguistics.