I Have Covered All the Bases Here: Interpreting Reasoning Features in
  Large Language Models via Sparse Autoencoders
Authors: name: Andrey Galichin, name: Alexey Dontsov, name: Polina Druzhinina, name: Anton Razzhigaev, name: Oleg Y. Rogov, name: Elena Tutubalina, name: Ivan Oseledets

Large Language Models (LLMs) have achieved remarkable success in natural
language processing. Recent advances have led to the developing of a new class
of reasoning LLMs; for example, open-source DeepSeek-R1 has achieved
state-of-the-art performance by integrating deep thinking and complex
reasoning. Despite these impressive capabilities, the internal reasoning
mechanisms of such models remain unexplored. In this work, we employ Sparse
Autoencoders (SAEs), a method to learn a sparse decomposition of latent
representations of a neural network into interpretable features, to identify
features that drive reasoning in the DeepSeek-R1 series of models. First, we
propose an approach to extract candidate ''reasoning features'' from SAE
representations. We validate these features through empirical analysis and
interpretability methods, demonstrating their direct correlation with the
model's reasoning abilities. Crucially, we demonstrate that steering these
features systematically enhances reasoning performance, offering the first
mechanistic account of reasoning in LLMs. Code available at
https://github.com/AIRI-Institute/SAE-Reasoning