EasySteer: A Unified Framework for High-Performance and Extensible LLM
  Steering
Authors: name: Haolei Xu, name: Xinyu Mei, name: Yuchen Yan, name: Rui Zhou, name: Wenqi Zhang, name: Weiming Lu, name: Yueting Zhuang, name: Yongliang Shen
Organization: Zhejiang University

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 5.5-11.4times 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.