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) ...
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---
name: locate-steer-and-improve-a-practical-survey-of
title: "Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.14004"
keywords: [Agents, Benchmarking]
description: "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) met..."
---
## Overview
This skill covers research on locate, steer, and improve: a practical survey of actionable mechanistic interpretability. It addresses important challenges in agent development and evaluation.
## Key Insights
The paper provides:
- Novel approaches or frameworks for agent systems
- Empirical evaluation results and benchmarks
- Generalizable principles for practitioners
## When to Use
Use this skill when working on:
- Agent-based systems and applications
- Autonomous reasoning and planning
- Agent performance evaluation and improvement
## When NOT to Use
- For non-agent-related tasks
- When seeking implementation code (consult the paper)
## Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.14004
- Full PDF: https://arxiv.org/pdf/2601.14004
- HTML: https://arxiv.org/html/2601.14004
Refer to the original paper for complete technical details, methodology, and experimental protocols.