This is more of an article describing their methodology than a full paper. But yes, there's plenty of peer reviewed papers on this topic, scaling sparse autoencoders to produce interpretable features for large models.

There's a ton of peer reviewed papers on SAEs in the past 2 years; some of them are presented at conferences.

For example: "Sparse Autoencoders Find Highly Interpretable Features in Language Models" https://proceedings.iclr.cc/paper_files/paper/2024/file/1fa1...

"Scaling and evaluating sparse autoencoders" https://iclr.cc/virtual/2025/poster/28040

"Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning" https://proceedings.neurips.cc/paper_files/paper/2024/hash/c...

"Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2" https://aclanthology.org/2024.blackboxnlp-1.19.pdf