6 papers
Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders
Gleb Gerasimov, Timofei Rusalev, Nikita Balagansky +3
Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are reproducible across training r…
Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors
Viacheslav Sinii, Nikita Balagansky, Gleb Gerasimov +6
The mechanisms by which reasoning training reshapes LLMs' internal computations remain unclear. We study lightweight steering vectors inserted into the base model's residual stream…
Kronecker Factorization Improves Efficiency and Interpretability of Sparse Autoencoders
Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev +2
Sparse Autoencoders (SAEs) have demonstrated significant promise in interpreting the hidden states of language models by decomposing them into interpretable latent directions. Howe…
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
Daniil Laptev, Nikita Balagansky, Yaroslav Aksenov +1
We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined…
Teach Old SAEs New Domain Tricks with Boosting
Nikita Koriagin, Yaroslav Aksenov, Daniil Laptev +3
Sparse Autoencoders have emerged as powerful tools for interpreting the internal representations of Large Language Models, yet they often fail to capture domain-specific features n…
Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy
Nikita Balagansky, Yaroslav Aksenov, Daniil Laptev +4
Sparse Autoencoders (SAEs) have proven to be powerful tools for interpreting neural networks by decomposing hidden representations into disentangled, interpretable features via spa…