Robust tooling and publicly available pre-trained models have helped drive recent advances in mechanistic interpretability for language models. However, similar progress in vision mechanistic interpretability has been hindered by the lack of accessible frameworks and pre-trained weights. We present Prisma (Access the codebase here: https://github.com/Prisma-Multimodal/ViT-Prisma), an open-source framework designed to accelerate vision mechanistic interpretability research, providing a unified toolkit for accessing 75+ vision and video transformers; support for sparse autoencoder (SAE), transcoder, and crosscoder training; a suite of 80+ pre-trained SAE weights; activation caching, circuit analysis tools, and visualization tools; and educational resources. Our analysis reveals surprising findings, including that effective vision SAEs can exhibit substantially lower sparsity patterns than language SAEs, and that in some instances, SAE reconstructions can decrease model loss. Prisma enables new research directions for understanding vision model internals while lowering barriers to entry in this emerging field.

本研究解决了视觉机制可解释性研究中缺乏可访问框架和预训练权重的问题。文章提出了Prisma，这是一个开源框架，提供了统一的工具包以加速视觉机制可解释性研究，包括75个以上的视觉和视频变换器、稀疏自编码器的支持以及80多个预训练权重。研究发现，有效的视觉自编码器可以表现出显著低于语言自编码器的稀疏性模式，这为理解视觉模型内部工作机制开辟了新的研究方向。

Prisma：一个用于视觉和视频机制可解释性的开源工具包