How Matryoshka Sparse AutoEncoders Recover Feature Hierarchies That Vanilla SAEs Lose

A walkthrough of the core findings and guided replication of the concepts from the original research on “Multi-level features discovery with Matryoshka Sparse AutoEncoders”.TL;DRSparse AutoEncoders (SAEs) are a cornerstone of mechanistic interpretability, but they struggle with scalability. As we increase the dictionary size to capture more features, we often encounter "feature splitting" and "feature absorption," where general concepts are lost or broken into fragmented, less interpretable comp...