concept
active
concept:fourier-featuresFourier features
Features identified in Llama-3.1-8B that compute sums using periods respecting base-10 addition (2, 5, 10) rather than concept-specific periods
Neighborhood — ranked by edge-count
Methods (1)
method
- Fourier analysis of neural activationsimplementsMethod used to identify the periodic features and their periods in Llama-3.1-8B's MLP neurons
Concepts (3)
concept
- Generic addition mechanismimplementsThe core finding: Llama-3.1-8B reuses one addition mechanism across all cyclic tasks rather than learning task-specific modular arithmetic
- MLP neuronsimplementsThe sparse set of 28 neurons at layer 18 identified as responsible for Fourier feature computation across all cyclic tasks
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Research thread within About Blank concerning the structure and relational properties of neural network feature representations; covariance pooling tangentially supports this thread.
- Mechanistic claim linking identified Fourier features to base-10 arithmetic
- Fourier features with period 10 contribute to base-10 sum computation in the 28-neuron clusterfinding0.746One of the three base-10 Fourier periods identified in the sparse neuron set
- The idea that interpretability should decompose representations into minimal, indivisible feature units; contrasted with manifold-level descriptions.
- Property that features activate on only a small fraction of inputs; enables compressed sensing and is what allows superposition to work
- The central object of study — the idea that a concept like truth is encoded as a direction in the LLM's latent space
- Hypothesized extension of superposition where features may be higher-dimensional manifolds rather than 1D directions
- Log-scale histogram of feature firing rates used as proxy for autoencoder quality during hyperparameter tuning