I Have Covered All the Bases Here: Interpreting Reasoning Features in
  Large Language Models via Sparse Autoencoders
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
67e25fe88e6c927eb7794abd
Andrey Galichin
admin_assigned
2025-03-28T12:24:57.801Z
661e44cf1d8ffc49b57ba07e
/avatars/3e937cc4f784b369b9f996ba82d1b81d.svg
Andrey Galichin
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andreuka18
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Alexey Dontsov
claimed_verified
2025-03-25T08:18:43.467Z
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Alexey Dontsov
user
therem
67e25fe88e6c927eb7794abf
Polina Druzhinina
67e25fe88e6c927eb7794ac0
Anton Razzhigaev
claimed_verified
2025-03-25T09:05:58.409Z
6172aaeec8e66e2aa84c06b9
https://cdn-avatars.huggingface.co/v1/production/uploads/6172aaeec8e66e2aa84c06b9/ZdRZSp3P1SU6CIDbvQwkv.jpeg
Anton Razzhigaev
user
razzant
67e25fe88e6c927eb7794ac1
Oleg Y. Rogov
67e25fe88e6c927eb7794ac2
Elena Tutubalina
claimed_verified
2025-03-25T09:05:56.401Z
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Elena Tutubalina
user
tlenusik
67e25fe88e6c927eb7794ac3
Ivan Oseledets
claimed_verified
2025-03-25T19:02:24.076Z
6169a581d05945bfd8718dfa
/avatars/1892ab06a7ddb557232777de3cbec470.svg
Ivan Oseledets
user
oseledets
I Have Covered All the Bases Here: Interpreting Reasoning Features in
  Large Language Models via Sparse Autoencoders
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
A study uses Sparse Autoencoders to identify and enhance reasoning features in Large Language Models, providing insights into the mechanisms driving their reasoning capabilities.
Sparse Autoencoders SAEs latent representations interpretable features reasoning features empirical analysis interpretability methods reasoning performance