Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat MI as an observational science, summarizing analytical insights while lacking a systematic framework for actionable intervention. To bridge this gap, we present a practical survey structured around the pipeline: "Locate, Steer, and Improve." We formally categorize Localizing (diagnosis) and Steering (intervention) methods based on specific Interpretable Objects to establish a rigorous intervention protocol. Furthermore, we demonstrate how this framework enables tangible improvements in Alignment, Capability, and Efficiency, effectively operationalizing MI as an actionable methodology for model optimization. The curated paper list of this work is available at https://github.com/rattlesnakey/Awesome-Actionable-MI-Survey.

翻译：机制可解释性已成为阐释大语言模型不透明决策过程的关键方法。然而，现有综述主要将机制可解释性视为观测科学，侧重于总结分析性见解，而缺乏系统化的可操作干预框架。为弥补这一空白，我们提出围绕"定位、引导与改进"流程构建的实用综述。我们基于特定可解释对象对定位（诊断）与引导（干预）方法进行形式化分类，以建立严谨的干预协议。进一步，我们展示了该框架如何在对齐性、能力与效率方面实现实质性改进，从而将机制可解释性有效转化为模型优化的可操作方法。本工作的精选论文列表发布于 https://github.com/rattlesnakey/Awesome-Actionable-MI-Survey。