Your SAE Passed the Cosine Similarity Bar. That Doesn't Mean It
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I ran a causal audit on sparse autoencoder features and found that up to 77% of "recovered" features never actually activate when their concept is present — even at cosine similarity ≈ 1.000. TL;DR I spent the last few months building and running a causal audit on Sparse Autoencoders (SAEs) — the tool the mechanistic interpretability field uses to decompose neural network activations into human-interpretable features. The result is now on arXiv: From Geometric Recovery to Causal Validation: A…
1Key Takeaways
- I ran a causal audit on sparse autoencoder features and found that up to 77% of "recovered" features never actually activate when their concept is present — even at cosine similarity ≈ 1.000.
- TL;DR I spent the last few months building and running a causal audit on Sparse Autoencoders (SAEs) — the tool the mechanistic interpretability field uses to decompose neural network activations into human-interpretable features.
- The result is now on arXiv: From Geometric Recovery to Causal Validation: A….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i ran a causal audit on sparse autoencoder features and found that up to 77% of "recovered" features never actually activate when their concept is present — even at cosine similarity ≈ 1.000.
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