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concept:update-stage

Update Stage

Second stage of DiffLogic CA where a DLGN computes each cell's new binary state from perception output and current state

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Frameworks (1)

framework

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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

  • Stage 2 of character training: DPO from teacher model to student model to transfer desired behavioral expressions
  • Belief Updatingconcept0.767
    Recursive inference of hidden states and parameters using free energy minimization.
  • Perception Stageconcept0.760
    First stage of DiffLogic CA update where each cell gathers information from neighboring cells via logic gate kernels
  • Default update strategy where all cells update simultaneously; contrasted with asynchronous updates
  • Stages of Inferenceframework0.752
    The perspective that LLM inference decomposes into distinct computational stages, which the paper extends to looped models
  • Stage 3 of character training: SFT on synthetic introspective data generated by post-distillation checkpoint
  • Update strategy where random subsets of cells are updated per step, simulating independent cell clocks