Logic Gate Perception Kernels

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

• Different tasks yield distinct distributions of logic gate types between perception kernels and update networksclaim0.825
  Interpretive claim based on circuit analysis across experiments
• Binary Logic Gatesconcept0.747
  Fundamental discrete computation units used as neurons in DLGN and DiffLogic CA
• Deep Differentiable Logic Gate Networksframework0.737
  Framework by Petersen et al. using logic gates as neurons with differentiable training, integrated into DiffLogic CA
• Probabilistic Gate Selectionconcept0.713
  Each gate maintains a 16-dimensional probability distribution over binary operations, updated via gradient descent
• Kernel (representational)concept0.702
  A function characterizing how a representation measures distance/similarity between datapoints; used to compare representations
• Pass-Through Gate Biasconcept0.692
  Initial gate distribution biased toward pass-through gates A and B to facilitate training stability
• Identification of algorithms implemented in attention layers, distributed across attention headsfinding0.688
  VPD successfully recovered interpretable attention algorithms (previous-token behavior, syntax-boundary routing) in weight space without requiring manual decomposition across heads.
• Perception Stageconcept0.681
  First stage of DiffLogic CA update where each cell gathers information from neighboring cells via logic gate kernels