Yaroslav Aksenov

According to our database, Yaroslav Aksenov authored at least 9 papers between 2024 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors.

Viacheslav Sinii, Nikita Balagansky, Gleb Gerasimov, Daniil Laptev, Yaroslav Aksenov, Vadim Kurochkin, Alexey Gorbatovski, Boris Shaposhnikov, Daniil Gavrilov

CoRR, September, 2025

Teach Old SAEs New Domain Tricks with Boosting.

Nikita Koriagin, Yaroslav Aksenov, Daniil Laptev, Gleb Gerasimov, Nikita Balagansky, Daniil Gavrilov

CoRR, July, 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, May, 2025

Train Sparse Autoencoders Efficiently by Utilizing Features Correlation.

Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev, Daniil Gavrilov, Nikita Balagansky

CoRR, May, 2025

You Do Not Fully Utilize Transformer's Representation Capacity.

Gleb Gerasimov, Yaroslav Aksenov, Nikita Balagansky, Viacheslav Sinii, Daniil Gavrilov

CoRR, February, 2025

Analyze Feature Flow to Enhance Interpretation and Steering in Language Models.

Daniil Laptev, Nikita Balagansky, Yaroslav Aksenov, Daniil Gavrilov

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Learn Your Reference Model for Real Good Alignment.

Alexey Gorbatovski, Boris Shaposhnikov, Alexey Malakhov, Nikita Surnachev, Yaroslav Aksenov, Ian Maksimov, Nikita Balagansky, Daniil Gavrilov

Proceedings of the Thirteenth International Conference on Learning Representations, 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 Koriagin, Daniil Gavrilov

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

2024

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

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024