Linear Transformers with Learnable Kernel Functions are Better In-Context Models. - Yaroslav Aksenov; Nikita Balagansky; Sofia Maria Lo Cicero Vaina; Boris Shaposhnikov; Alexey Gorbatovski; Daniil Gavrilov (ACL (1), 2024)

Learn Your Reference Model for Real Good Alignment. - Alexey Gorbatovski; Boris Shaposhnikov; Alexey Malakhov; Nikita Surnachev; Yaroslav Aksenov; Ian Maksimov; Nikita Balagansky; Daniil Gavrilov (ICLR, 2025)

Analyze Feature Flow to Enhance Interpretation and Steering in Language Models. - Daniil Laptev; Nikita Balagansky; Yaroslav Aksenov; Daniil Gavrilov (CoRR, 2025)

You Do Not Fully Utilize Transformer's Representation Capacity. - Gleb Gerasimov; Yaroslav Aksenov; Nikita Balagansky; Viacheslav Sinii; Daniil Gavrilov (CoRR, 2025)

Train Sparse Autoencoders Efficiently by Utilizing Features Correlation. - Vadim Kurochkin; Yaroslav Aksenov; Daniil Laptev; Daniil Gavrilov; Nikita Balagansky (CoRR, 2025)

Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy. - Nikita Balagansky; Yaroslav Aksenov; Daniil Laptev; Vadim Kurochkin; Gleb Gerasimov; Nikita Koryagin; Daniil Gavrilov (CoRR, 2025)

Teach Old SAEs New Domain Tricks with Boosting. - Nikita Koriagin; Yaroslav Aksenov; Daniil Laptev; Gleb Gerasimov; Nikita Balagansky; Daniil Gavrilov (CoRR, 2025)