Position: Mechanistic Interpretability Should Prioritize Feature
  Consistency in SAEs
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.
683528109f968fc5c604495f
Xiangchen Song
extracted_pending
2025-05-27T02:48:53.929Z
5f12485c0c833276f61f1afb
https://cdn-avatars.huggingface.co/v1/production/uploads/1595033594228-noauth.jpeg
Xiangchen Song
user
xiangchensong
683528109f968fc5c6044960
Aashiq Muhamed
claimed_verified
2025-05-27T07:51:25.328Z
64755a83e0b188d3cb2579d8
/avatars/2c50590905f4bd398a4c9991e1b4b5bb.svg
Aashiq Muhamed
user
aashiqmuhamed
683528109f968fc5c6044961
Yujia Zheng
admin_assigned
2025-05-27T13:00:31.633Z
6804a894ea0734d6b26f19c3
/avatars/b3f5321366bbbb2fd91624289fbea958.svg
Yujia Zheng
user
yujiazheng
683528109f968fc5c6044962
Lingjing Kong
admin_assigned
2025-05-27T13:00:37.345Z
6685f9e40284cbb0bcaef4f3
/avatars/475a708742e5a5198409f904b81153b1.svg
Lingjing Kong
user
lkong
683528109f968fc5c6044963
Zeyu Tang
admin_assigned
2025-05-27T13:00:43.584Z
6553cd2296c5902fa386f57f
/avatars/b4fbd284335a8093a366516fee714d71.svg
Zeyu Tang
user
zeyutang
683528109f968fc5c6044964
Mona T. Diab
683528109f968fc5c6044965
Virginia Smith
admin_assigned
2025-05-27T13:00:55.633Z
67c66c3febb87abbdf7af5ff
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/49hg7ZTN0Oupydi-BP4v-.png
Virginia Smith
user
gingsmith
683528109f968fc5c6044966
Kun Zhang
Position: Mechanistic Interpretability Should Prioritize Feature
  Consistency in SAEs
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.
Prioritizing feature consistency in sparse autoencoders improves mechanistic interpretability of neural networks by ensuring reliable and interpretable features.
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