MURANO: Design, Run, and Reproduce Mechanistic Interpretability Experiments as Composable Pipelines

2026-08-31

AI Summary

本文介绍了一个名为Murano的开源框架，旨在通过可组合的步骤解决大型语言模型机制解释性研究中设计、运行与复现的问题。

Abstract

This paper presents Murano, an open source framework for designing, running, and reproducing mechanistic interpretability studies of large language models, intended for researchers across disciplines. These studies often combine loading, recording, attribution, intervention, and evaluation, while existing libraries tend to focus on different parts of this workflow. As a result, researchers using several libraries may need to adapt outputs from one for use by another. To bridge this gap, Murano represents operations from these five areas as composable steps. Steps exchange named result artifacts and declare the inputs they require and the outputs they produce. A pipeline executes its steps in the order supplied, and Murano uses canonical addresses when component identities pass between operations. Murano builds on existing interpretability and machine learning libraries. We demonstrate Murano through two reproductions of established interpretability studies and one illustrative sparse autoencoder case study.

Innovation

• composable steps
• mechanistic interpretability
• large language models
• pipeline execution
• result artifacts