title: Position: Mechanistic Interpretability Should Prioritize Feature
    Consistency in SAEs
summary: Sparse Autoencoders (SAEs) are a prominent tool in mechanistic
  interpretability (MI) for decomposing neural network activations into
  interpretable features. However, the aspiration to identify a canonical set of
  features is challenged by the observed inconsistency of learned SAE features
  across different training runs, undermining the reliability and efficiency of
  MI research. This position paper argues that mechanistic interpretability
  should prioritize feature consistency in SAEs -- the reliable convergence to
  equivalent feature sets across independent runs. We propose using the Pairwise
  Dictionary Mean Correlation Coefficient (PW-MCC) as a practical metric to
  operationalize consistency and demonstrate that high levels are achievable
  (0.80 for TopK SAEs on LLM activations) with appropriate architectural choices.
  Our contributions include detailing the benefits of prioritizing consistency;
  providing theoretical grounding and synthetic validation using a model
  organism, which verifies PW-MCC as a reliable proxy for ground-truth recovery;
  and extending these findings to real-world LLM data, where high feature
  consistency strongly correlates with the semantic similarity of learned feature
  explanations. We call for a community-wide shift towards systematically
  measuring feature consistency to foster robust cumulative progress in MI.
paper_authors._id: 683528109f968fc5c604495f
paper_authors.name: Xiangchen Song
paper_authors.status: extracted_pending
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paper_authors.fullname: Xiangchen Song
paper_authors.type: user
paper_authors.user: xiangchensong
paper_authors._id: 683528109f968fc5c6044960
paper_authors.name: Aashiq Muhamed
paper_authors.status: claimed_verified
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paper_authors.fullname: Aashiq Muhamed
paper_authors.type: user
paper_authors.user: aashiqmuhamed
paper_authors._id: 683528109f968fc5c6044961
paper_authors.name: Yujia Zheng
paper_authors.status: admin_assigned
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paper_authors.fullname: Yujia Zheng
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paper_authors.user: yujiazheng
paper_authors._id: 683528109f968fc5c6044962
paper_authors.name: Lingjing Kong
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paper_authors.fullname: Lingjing Kong
paper_authors.type: user
paper_authors.user: lkong
paper_authors._id: 683528109f968fc5c6044963
paper_authors.name: Zeyu Tang
paper_authors.status: admin_assigned
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paper_authors._id: 6553cd2296c5902fa386f57f
paper_authors.avatarUrl: /avatars/b4fbd284335a8093a366516fee714d71.svg
paper_authors.fullname: Zeyu Tang
paper_authors.type: user
paper_authors.user: zeyutang
paper_authors._id: 683528109f968fc5c6044964
paper_authors.name: Mona T. Diab
paper_authors._id: 683528109f968fc5c6044965
paper_authors.name: Virginia Smith
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:00:55.633Z
paper_authors._id: 67c66c3febb87abbdf7af5ff
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/49hg7ZTN0Oupydi-BP4v-.png
paper_authors.fullname: Virginia Smith
paper_authors.type: user
paper_authors.user: gingsmith
paper_authors._id: 683528109f968fc5c6044966
paper_authors.name: Kun Zhang
paper_title: Position: Mechanistic Interpretability Should Prioritize Feature
    Consistency in SAEs
paper_summary: Sparse Autoencoders (SAEs) are a prominent tool in mechanistic
  interpretability (MI) for decomposing neural network activations into
  interpretable features. However, the aspiration to identify a canonical set of
  features is challenged by the observed inconsistency of learned SAE features
  across different training runs, undermining the reliability and efficiency of
  MI research. This position paper argues that mechanistic interpretability
  should prioritize feature consistency in SAEs -- the reliable convergence to
  equivalent feature sets across independent runs. We propose using the Pairwise
  Dictionary Mean Correlation Coefficient (PW-MCC) as a practical metric to
  operationalize consistency and demonstrate that high levels are achievable
  (0.80 for TopK SAEs on LLM activations) with appropriate architectural choices.
  Our contributions include detailing the benefits of prioritizing consistency;
  providing theoretical grounding and synthetic validation using a model
  organism, which verifies PW-MCC as a reliable proxy for ground-truth recovery;
  and extending these findings to real-world LLM data, where high feature
  consistency strongly correlates with the semantic similarity of learned feature
  explanations. We call for a community-wide shift towards systematically
  measuring feature consistency to foster robust cumulative progress in MI.
paper_ai_summary: Prioritizing feature consistency in sparse autoencoders improves mechanistic interpretability of neural networks by ensuring reliable and interpretable features.
paper_ai_keywords: Sparse Autoencoders (SAEs) mechanistic interpretability (MI) feature consistency Pairwise Dictionary Mean Correlation Coefficient (PW-MCC) TopK SAEs LLM activations synthetic validation semantic similarity learned feature explanations