Sparse circuits

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Minimal Circuit Descriptionconcept0.798
  Hypothesis that discretization finds minimum-size circuits equivalent to minimal algorithmic descriptions of patterns
• Circuit Findingmethod0.776
  Interpretability technique for identifying functional sub-circuits in neural networks, supported by pyvene
• Sparse Crosscodersframework0.766
  Extension of SAEs that jointly learns latents across representations from different models; proposed as alternative for extended fine-tuning
• Sparse Coding Hypothesisconcept0.765
  The hypothesis that semantic concepts in neural representations can be captured using sparsity priors
• Sparse Feature Dictionaryconcept0.760
  The extracted set of sparse interpretable features from model embeddings via SAEs
• Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models (Marks et al., 2025)concept0.760
  Cited as enabling precise behavioral control through SAE features, extending the same methodological line
• Sparse and smooth codingconcept0.760
  Coding scheme where qualities are represented by few neurons with continuous similarity relations.
• Noisy Simulation of Sparse Networksconcept0.755
  Mechanism by which superposition works: small neural networks exploit sparsity to approximately simulate much larger sparse networks