Different tasks yield distinct distributions of logic gate types between perception kernels and update networks

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

• Logic Gate Perception Kernelsmethod0.825
  Named technique in DiffLogic CA where fixed-structure logic circuits replace Sobel filters for neighborhood perception
• Identification of algorithms implemented in attention layers, distributed across attention headsfinding0.780
  VPD successfully recovered interpretable attention algorithms (previous-token behavior, syntax-boundary routing) in weight space without requiring manual decomposition across heads.
• Differentiable Logic Gate Networks have not previously been shown to work in recurrent settings prior to this workclaim0.774
  Authors' novelty assertion establishing the gap filled by DiffLogic CA
• Neural networks, trained with different objectives on different data and modalities, are converging to a shared statistical model of reality in their representation spaces.quote0.766
  The paper's central thesis statement, presented prominently after the abstract
• Different neural network models trained on different objectives and modalities are converging to a shared statistical model of reality in their representation spaceshypothesis0.766
  The central hypothesis of the paper; the platonic representation hypothesis itself
• Distributed cognition in aviation operations examined via network analysis of gate-to-gate operationsfinding0.763
  Empirical study showing distributed cognitive processes across multiple human agents and systems; provides precedent for non-AI distributed cognition.
• The integration of differentiable logic gates and neural cellular automata is a potential step towards programmable matter — Computroniumclaim0.756
  Authors' broader vision claim linking their system to Toffoli and Margolus's programmable matter concept
• Analogous features and circuits form across models and tasks.claim0.750
  Third of three speculative claims asserting that learned features are not model-specific but represent universal solutions to learning problems