mlsanityData to 7 Oct 2026

A Comparative Analysis of Sparse Autoencoder and Activation Difference in Language Model Steering

arXiv 2510.01246 ↗First posted 24 Sep 2025cs.CLcited by 3

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Abstract (arXiv)

Sparse autoencoders (SAEs) have recently emerged as a powerful tool for language model steering. Prior work has explored top-k SAE latents for steering, but we observe that many dimensions among the top-k latents capture non-semantic features such as punctuation rather than semantic attributes like instructions. To address this, we propose focusing on a single, most relevant SAE latent (top-1), eliminating redundant features. We further identify a limitation in constant SAE steering, which often produces degenerate outputs such as repetitive single words. To mitigate this, we introduce a token-wise decaying steering strategy, enabling more faithful comparisons with mean activation difference baselines. Empirically, we show that steering an SAE latent associated with reasoning reliably elicits step-by-step mathematical reasoning and enhances inference quality, functionally resembling the effect of appending a guiding token. Our results demonstrate that SAEs outperform mean activation difference methods on mathematical reasoning benchmarks and match their performance on IF-Eval.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 24 Sep 2025.

Builds on

22 of 43 references.

References · 22

  1. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  2. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  3. 2016Solving General Arithmetic Word Problemscited by 353
  4. 2021A Diverse Corpus for Evaluating and Developing English Math Word Problem Solverscited by 314
  5. 2023In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steeringcited by 240
  6. 2025AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoderscited by 199
  7. 2024Improving Dictionary Learning with Gated Sparse Autoencoderscited by 171
  8. 2024Improving Instruction-Following in Language Models through Activation Steeringcited by 149
  9. 2024Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Modelscited by 114
  10. 2024Steering Language Model Refusal with Sparse Autoencoderscited by 89
  11. 2024Improving Steering Vectors by Targeting Sparse Autoencoder Featurescited by 86
  12. 2024Applying sparse autoencoders to unlearn knowledge in language modelscited by 58
10 more
  1. 2024A Language Model's Guide Through Latent Spacecited by 43
  2. 2025I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoderscited by 43
  3. 2024Multi-property Steering of Large Language Models with Dynamic Activation Compositioncited by 35
  4. 2024Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Smallcited by 34
  5. 2024Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineeringcited by 33
  6. 2025Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoderscited by 21
  7. 2024Can sparse autoencoders be used to decompose and interpret steering vectors?cited by 19
  8. 2025Sparse Autoencoder Features for Classifications and Transferabilitycited by 19
  9. 2024Analyzing (In)Abilities of SAEs via Formal Languagescited by 16
  10. 2024Group-SAE: Efficient Training of Sparse Autoencoders for Large Language Models via Layer Groupscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Steered Generation via Gradient-Based Optimization on Sparse Query Featurescited by 0
  2. 2026High-Dimensional Random Projection for Activation Steering in Language Modelscited by 0
  3. 2026Kernelized Activation Steeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Do Sparse Autoencoders Identify Reasoning Features in Language Models?
  2. 2025ActivationReasoning: Logical Reasoning in Latent Activation Spaces
  3. 2026A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models
  4. 2025Evaluating Sparse Autoencoders for Monosemantic Representation
  5. 2026Thinking vs. NoThinking: Towards Interpreting Reasoning Mechanisms of Large Language Models via Sparse Autoencoders
  6. 2025Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models
  7. 2026Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models
  8. 2026Reward-Informed Sparse Autoencoders and the Solution-Completeness Confound