Linear Probes
A linear probe is a small linear classifier (or linear regressor) trained on the frozen internal activations of a neural network to test whether a particular concept, property, or label is linearly decodable…
Interpretability
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Monosemanticity
Monosemanticity is a property of an internal feature or neuron in a neural network when that unit responds to a single, human-interpretable concept rather than to a heterogeneous collection of unrelated inputs.
Neural Networks Are Already Clean
Neural Networks Are Already Clean is a long interactive research essay by Nick Cammarata, published by the interpretability company Goodfire on 15 October 2026 at already-clean.goodfire.com.
Polysemanticity
Polysemanticity is the phenomenon in artificial neural networks in which a single neuron (or directional unit such as an attention head) activates strongly for multiple, semantically unrelated inputs or…
Sparse Coding
Sparse coding is a representation learning principle in which a signal is encoded as a linear combination of a small number of elements drawn from a (usually overcomplete) dictionary of atoms.
Superposition (Mechanistic Interpretability)
Superposition is the phenomenon in which an artificial neural network represents more distinct features than it has dimensions in its activation space, by assigning those features to nearly-orthogonal (rather…
Transcoder
A transcoder is a sparse neural network used in mechanistic interpretability research to approximate the input-to-output function of a component inside a transformer (most commonly an MLP sublayer) using a…
Weight-Space Programming
Weight-Space Programming is an approach in which a general-purpose large language model reads, interprets, edits or constructs another neural network's numerical weights in response to a natural-language goal