SNIP Scope trains a top-k sparse autoencoder on final-layer hidden activations from
the pretrained SNIP-0.4M transformer. A 96-dimensional activation is encoded into a
384-feature overcomplete dictionary, but only the 16 largest positive features may
fire for each token.
The benchmark measures held-out reconstruction, explained variance, active-feature
count, dead-feature rate, and token exemplars for each learned feature. A 16-component
PCA reconstruction is retained as a dense low-rank control. Feature exemplars are
descriptive clues, not proof that a neuron represents one human concept.
Verified results
The SAE trained on 180,000 final-layer token activations and was measured on 40,000
held-out activations.
Metric
Top-k SAE
PCA-16 control
Reconstruction MSE
0.02585
0.29824
Explained variance
97.41%
70.11%
Active features per token
16.00
16 dense components
Dead dictionary features
3.39%
not applicable
The learned dictionary contains 384 features and the SAE has 74,112 parameters.
The Space exposes each feature's five highest-activating held-out BPE tokens and
firing rate. Those exemplars may reflect token identity, syntax, position, or mixed
causes; the project does not assign automatic human-readable concepts.
