@inproceedings{xu-etal-2026-easysteer,
title = "{E}asy{S}teer: A Unified Framework for High-Performance and Extensible {LLM} Steering",
author = "Xu, Haolei and
Mei, Xinyu and
Yan, Yuchen and
Zhou, Rui and
Zhang, Wenqi and
Lu, Weiming and
Zhuang, Yueting and
Shen, Yongliang",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.3/",
pages = "24--35",
ISBN = "979-8-89176-461-3",
abstract = "Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM{'}s optimized inference engine, EasySteer achieves 10.8-22.3$\times$ speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models."
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<abstract>Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM’s optimized inference engine, EasySteer achieves 10.8-22.3\times speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models.</abstract>
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%0 Conference Proceedings
%T EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
%A Xu, Haolei
%A Mei, Xinyu
%A Yan, Yuchen
%A Zhou, Rui
%A Zhang, Wenqi
%A Lu, Weiming
%A Zhuang, Yueting
%A Shen, Yongliang
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F xu-etal-2026-easysteer
%X Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM’s optimized inference engine, EasySteer achieves 10.8-22.3\times speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models.
%U https://aclanthology.org/2026.emnlp-demos.3/
%P 24-35
Markdown (Informal)
[EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering](https://aclanthology.org/2026.emnlp-demos.3/) (Xu et al., EMNLP 2026)
ACL
- Haolei Xu, Xinyu Mei, Yuchen Yan, Rui Zhou, Wenqi Zhang, Weiming Lu, Yueting Zhuang, and Yongliang Shen. 2026. EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 24–35, Budapest, Hungary. Association for Computational Linguistics.